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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

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ΪηΎΑϲϣήϳΩΎϘϣϩΪϨϫΩϥΎθϧέ΍Ϯϧϭέ΍ΰΑ΍έ΍ϮϧˬϮϨϣΖγήϬϓˬϥ΍ϮϨϋέ΍ϮϧϞϣΎηέ΍ΰϓ΍ϡήϧϦϳ΍ Ζ˰γ΍record̮˰ϳήτγήϫϭΪηΎΑϲϣΡήτϣvariableΎϳήϴϐΘϣϥ΍ϮϨϋϪΑϪ̯Ζγ΍field̮ϳϥϮΘγήϫ ΪηΎΑϲϣΡήτϣcace ΎϳΩέϮϣ̮ϳϥ΍ϮϨϋϪΑϪ̯ ή˰ϫΖ˰γ΍ϪΤϔ˰λϦ˰ϳ΍ΕΎ˰ϋϼσ΍ζϳ΍ή˰ϳϭSPSSϪ˰ϣΎϧήΑέΎ˰̯ϭΪ˰ϨϳϮ̳ϲϣDatasheetϩΪηίΎΑϩήΠϨ̡ϪΑ Ωέ΍ΩΖϤδϗϭΩDatasheet ϢϴϨ̯ϲϣΩέ΍ϭϥ΁έΩ΍έήϳΩΎϘϣϪ̯Data view˺ ϢϴϨ̯ϲϣϒϳήόΗ΍έΎϫήϴϐΘϣϪ̯Variable view˻  ή˰ϴϐΘϣϪ˰̯έΎ˰Αή˰ϫϭΖ˰γΎϬϧ΁αΎ˰ϴϘϣΎ˰Ϭϧ΁Ζϴ˰λΎΧϦϳή˰ΘϤϬϣϪ̯Ϊϧέ΍ΩϲΗΎϴλϮμΧΎϫήϴϐΘϣSPSSέΩ ϢϴϨ̯ϒϳήόΗ΍έϥ΁αΎϴϘϣΪϳΎΑΩϮηϲϣϒϳήόΗ  ŚƷŽŚǀƤƯƕřƺƳř

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 Nominal  ϱέϮλ ϲϤγ΍αΎϴϘϣ˺ έΩΩϮ˰ηϲϤ˰γ΍αΎ˰ϴϘϣϪ˰ΑϞϳΪΒΗϭϩΩ΍ΩΪ̯ϥ΁ϪΑϢϴϧ΍ϮΘϴϣϪ̯ϢϴϨ̯ϲϣϲ̳Ϊϧίϥ΁έΩϪ̯ϱήϬηΪϨϧΎϣ ϩϭή˰̳ΐ˰γΎϨϣϭΩή˰̯ϱέ΍ά˰̳Ϊ˰̯ϥ΍ϮΗϲϣέϮσήϫϭΩέ΍ΪϧϲΗϭΎϔΗΩ΍Ϊϋ΍ϥΩϮΑ̱έΰΑϭ̮̩Ϯ̯αΎϴϘϣϦϳ΍ Ωέ΍ΪϧϲϨόϣϥ΁έΩϊϤΟϭϦϴ̴ϧΎϴϣϭΖγ΍ϱΪϨΑ  Ordinal ϲΒϴΗήΗ  ϱ΍ϪΒΗέαΎϴϘϣ˻ Ύ˰ΠΑΎΟΎ˰ϫΪ˰̯ή˰̳΍Ϊ˰ϫΩϲ˰ϣϥΎθ˰ϧ΍έΐ˰ϴΗήΗΎ˰ϳΖϳϮϟϭ΍ϭΪηΎΑϲϣΖϴϤϫ΍ϪΟέΩαΎγ΍ήΑϱέ΍ά̳Ϊ̯ Ωέ΍ΪϧΩϮΟϭΎϬϫϭή̳ϦϴΑ̵ήϴ̳ϩί΍Ϊϧ΍ϞΑΎϗϪϠλΎϓϲϟΎϋϭΪΑˬΏϮΧΕέΎϬϣΪϨϧΎϣΪϨ̯ϲϣϕήϓΪϧϮη  ScaleϲΒδϧαΎϴϘϣ.˼ Ω΍ήϓ΍ΪϗϞΜϣΩέ΍ΩϲΒδϧαΎϴϘϣΪϳΎϴΑΖγΪΑϱήϴ̳ϩί΍Ϊϧ΍ϖϳήσί΍Ϫ̯ϱΩΪϋήϫ  żĩźưţƆųŚƃ έΎ˰̰ΑϲΒδ˰ϧαΎ˰ϴϘϣϱ΍ή˰Ας˰ϘϓϪ˰̯Ζγ΍ Mean Ϧϴ̴ϧΎϴϣϥ΁ϦϳήΘϓϭήόϣϭΖγΎϫϩΩ΍Ωΰ̯ήϤΗϪϧΎθϧ Ύ˰ϫϩΩ΍Ωή˰̳΍ϲϟϭΩϭέϲϣϥ΁ϑήσϪΑΪηΎΑ̱έΰΑήϳΩΎϘϣή̳΍ϭΖγ΍αΎδΣΎϫϩΩ΍ΩϪΑέΎϴδΑΩϭέϲϣ ΩϮΑΪϫ΍ϮΨϧΎϳϮ̳Ϧϴ̴ϧΎϴϣΪϨηΎΑϩΪϨ̯΍ή̡ϲϠϴΧ ή̳΍Ϊ˰ϳ΁ϲ˰ϣΖ˰γΪΑϪ˰ΒΗέϱϭέί΍ϭΖ˰γΎ˰ϫϩΩ΍Ω̶ϧΎ˰ϴϣϪ˰ΒΗέϪ̯Ζγ΍ Median ϪϧΎϴϣή̴ϳΩκΧΎη n 1 ̱έΰ˰ΑϩΩ΍ΩϪΑϪϧΎϴϣΖγ΍ϪϧΎϴϣϲτγϭΪηΎΑΩήϓή̳΍ϭΖγ΍ϪϧΎϴϣ ϪϴΗέΪηΎΑΝϭίΎϫϩΩ΍ΩΩ΍ΪόΗ 2 ΪηΎΑϲϤϧαΎδΣ̮̩Ϯ̯Ύϳ ϲϧ΍ϭ΍ήϓϥ΍ΰϴϣΩϭέϲϣέΎ̰ΑϢϫNominalΎϳϲϤγ΍ϱΎϫϩΩ΍Ωϱ΍ήΑϪ̯Ζγ΍ Mode Ϊϣή̴ϳΩκΧΎη Ωέ΍Ω΍έΕ΍ΪϫΎθϣϦϳήΘθϴΑϭΪϫΩϲϣϥΎθϧ΍έΎϫϩΩ΍Ω     1


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř  ƾĭŶƴĩřźěƆųŚƃ

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βϧΎ˰ϳέ΍ϭϥ΁Ϧϳή˰ΘϓϭήόϣϭΩϭή˰ϴϣέΎ˰̰ΑScaleΎ˰ϳϲΒδ˰ϧϱΎ˰ϫϩΩ΍Ωϱ΍ή˰ΑϭΖγΎϫϩΩ΍Ωϲ̳ΪϨ̯΍ή̡ϪϧΎθϧ f i Ϫ˰̯Ζ˰˰γ΍ Mean = Ϧ   xi   xi f i ϴ̴ϧΎ˰˰˰˰ϴϣΖ˰˰˰˰γ΍ϩΩ΍Ωή˰˰˰˰ϫϲ˰˰˰˰ϧ΍ϭ΍ήϓ n  fi   ( xi  x ) 2 variance =       βϧΎ ϳ έ΍ϭ ( x  x )  0  i n  ϡή̳Ϯ˰Ϡϴ̯Ύ˰ϫϩΩ΍ΩΪ˰Σ΍ϭή˰̳΍Ζ˰γ΍ϲ˰̰ϳΎ˰ϫϩΩ΍ΩΪΣ΍ϭΎΑϥ΁ΪΣ΍ϭϪ̯Ζγ΍έΎϴόϣϑ΍ήΤϧ΍βϧΎϳέ΍ϭέάΟϭ ϩΩ΍ΩϥϮ˰̩ΪϨΘδ˰ϫϲΑϮ˰ΧϱΎ˰ϫκΧΎηέΎϴόϣϑ΍ήΤϧ΍ϭβϧΎϳέ΍ϭΖγ΍ϡή̳ϮϠϴ̯ϢϫέΎϴόϣϑ΍ήΤϧ΍ΪηΎΑ ΪϧήΛϮϣϥ΁έΩΎϫ R=max-min ΪηΎΑϲϣRangeΎϳΕ΍ήϴϴϐΗϪϨϣ΍Ωϲ̳ΪϨ̯΍ή̡ή̴ϳΩκΧΎη ϲ̯έΎ̩ήΒϧΎϴϣϭϢϴϨ̯ϲϣϩΩΎϔΘγ΍ϥ΁ί΍ΪϨηΎΑϩΪϨ̯΍ή̡ϲϠϴΧΎϫϩΩ΍Ωή̳΍Ϫ̯Ζγ΍̭έΎ̩ή̴ϳΩκΧΎη  R  Q3  Q1  Ϫ˰ϧΎϴϣϥΎϤϫϡϭΩ̭έΎ̩ϭΪϨΘδϫήΘθϴΑϥ΁ί΍ΎϫϩΩ΍Ω˻̋ϭήΘϤ̯ϥ΁ί΍ΎϫϩΩ΍Ὼ̋ϲϨόϳϡϮγ̭έΎ̩ Ζγ΍ ΪηΎΑϲϣϝΎϣήϧϪόϣΎΟϥ΁ΪϨηΎΑ̮ϳΩΰϧϢϫϪΑΎϫϩΩ΍ΩϦϴ̴ϧΎϴϣϭϪϧΎϴϣή̳΍ 

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˺ϝΎΜϣ κΧΎ˰ηβ̢˰γΪ˰ϴϨ̯Ωέ΍ϭϪΤϔλέΩ΍έήϳίΕΎϋϼσ΍ϭϩΩή̯ϒϳήόΗ΍έϲϠϣκϟΎΧΎϧΪϣ΁έΩϭέϮθ̯ϡΎϧϱΎϫήϴϐΘϣ ΪϴϳΎϤϧϪΒγΎΤϣ΍έϲ̳ΪϨ̯΍ή̡ϭϊϳίϮΗˬϱέΎϣ΁ϒϠΘΨϣϱΎϫ ϲϠϣκϟΎΧΎϧϡ΁έΩ ˺̋ ˻˹ ̋ ˺˹ ˺˼

έϮθ̯ϡΎϧ ϥ΍ήϳ΍ Ϫϴ̯ήΗ ϥΎΘδϧΎϐϓ΍ ϕ΍ήϋ ϥΎΘδ̯Ύ̡

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 :ϢϴϨ̯ϲϣϒϳήόΗvariable viewέΩ΍έΎϫήϴϐΘϣ countryϡΎϧϪΑΎϫέϮθ̯ϡΎϧϝϭ΍ήϴϐΘϣ ΩϮθϧ̠ϳΎΗϲγέΎϓϭΩέ΍ΪϧϲϟΎ̰η΍ϩήϴΗςΧϲϟϭΩϮηϩΩΎϔΘγ΍ϪϠλΎϓί΍ΪϳΎΒϧήϴϐΘϣϢγ΍̠ϳΎΗέΩ ϢϴϨ̯ϲϣΏΎΨΘϧ΍STRINGήϴϐΘϣωϮϧΎϳType έΩ name of countryˬ˱ϼΜϣΩ΍ΩήϴϐΘϣΩέϮϣέΩϲϓΎ̯ΕΎΤϴοϮΗϥ΍ϮΘϴϣlabelέΩ ϱΎ˰ϫή˰ϴϐΘϣϱΪ˰ϨΑΪ˰̯ιϮμ˰ΨϣϭϢϴ˰ϧίϲ˰Ϥϧϱΰ˰ϴ̩ϩΪ˰ηϒϳήόΗϲϤγ΍ήϴϐΘϣϥϮ̩valueΖϤδϗέΩ ΪϨηΎΑϱΩΪϋϪ̯Ζγ΍ϲϫϭή̳ έΩ˱ϼΜϣΩϮ˰˰ηϲ˰ϤϧϪ˰˰Θϓή̳ή˰˰ψϧέΩΕΎΒ˰γΎΤϣέΩϪ˰˰̯ΩϮ˰˰ηϲ˰ ϓήόϣϱΩ΍Ϊ˰ϋ΍ϲ˰ ϨόϳmissingΖϤδ˰˰ϗέΩ ΖϗϭήϫϪ̯ϢϴϨ̰ϴϣϲϓήόϣ΍έ˺˹˹˹ΩΪϋΎϣΖγ΍ϩΪθϧή̡ΎϳΖγΎϧ΍ϮΧΎϧΎϫϪϨϳΰ̳ϲΧήΑΎϫϪϣΎϨθγή̡ ΩϮθϧΏΎδΣϭΪηΎΑϥ΁ϲϨόϣϪΑΪηΝέΩ ϢϴϨ̯ϲϣϒϳήόΗ˺˹΍έϥϮΘγϱΎϨϬ̡ ςγϭΎϳ̠̩ˬΖγ΍έϢϴϨ̰ϴϣΏΎΨΘϧ΍΍έAlign ϢϴϨ̯ϲϣΏΎΨΘϧ΍ϲϤγ΍ϭNominal΍έήϴϐΘϣαΎϴϘϣΎϳMeasure   2


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 GNPΎϳϲϠϣκϟΎΧΎϧΪϣ΁έΩϥ΍ΰϴϣϡϭΩήϴϐΘϣ Ωέ΍ΩϱΩΪϋήϳΩΎϘϣϪΑιΎμΘΧ΍Ϫ̯ϢϴϨ̯ϲϣΏΎΨΘϧ΍Numeric ήϴϐΘϣωϮϧΎϳType έΩ Gross National Productˬ˱ϼΜϣΩ΍ΩήϴϐΘϣΩέϮϣέΩϲϓΎ̯ΕΎΤϴοϮΗϥ΍ϮΘϴϣlabelέΩ ϢϴϧίϲϤϧϱΰϴ̩valueΖϤδϗέΩ ϢϴϨ̯ϲϣϒϳήόΗ˺˹΍έϥϮΘγϱΎϨϬ̡ ςγϭΎϳ̠̩ˬΖγ΍έϢϴϨ̰ϴϣΏΎΨΘϧ΍΍έAlign ϢϴϨ̯ϲϣΏΎΨΘϧ΍ϲΒδϧϭScale ΍έήϴϐΘϣαΎϴϘϣΎϳMeasure ΩέϮΧϲϣΰϴϤϣϢϗέ˻ΎΗϢϴϧΰΑ˻ή̳΍΍έDecimal 

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Missing column 10 10

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Analyze – Report –Case Summaries ϩή˰ΠϨ̡ί΍ζ˰Ϡϓς˰γϮΗ΍έϱέΎ˰ϣ΁ϱΎ˰ϫκΧΎ˰ηϢϴ˰ψϨΗΖ˰ϬΟή˰ψϧΩέϮϣήϴϐΘϣϪ̯ΩϮηϲϣίΎΑϱ΍ϩήΠϨ̡ κΧΎ˰ηstatistic Ϫ˰Ϥ̳ΩϥΩίΎ˰Αβ̢˰γϭϢϴ˰ϫΩϲ˰ϣϝΎ˰ϘΘϧ΍Variable Ζ˰γ΍έΖϤγϩήΠϨ̡ϪΑΩϮΟϮϣ ϭ kutosis ϲ̳Ϊϴθ˰̯ˬ skewness ϲ̴ϟϮ̩ˬβϧΎϳέ΍ϭˬϦϴ̴ϧΎϴϣˬϪϧΎϴϣΪϨϧΎϣ΍έήψϧΩέϮϣϱέΎϣ΁ϱΎϫ ϢϴϨ̯ϲϣΏΎΨΘϧ΍ Ϫ˰̩ήϫϭΪ˰ϳΪϨΒϧ΍έϩή˰ΠϨ̡Ϧϳ΍Ζγ΍ήΘϬΑΩϮηϲϣήϫΎχoutput1ϡΎϧϪΑϪϧΎ̳΍ΪΟϩήΠϨ̡έΩspssϲΟϭήΧ ΪηΪϫ΍ϮΧϪϓΎο΍ϩήΠϨ̡ϦϴϤϫέΩΪϴϨ̯ϪϓΎχ΍  ΎϫκΧΎηϪΒγΎΤϣϡϭΩεϭέ Analyze – Descriptive statistics - frequencies ϢϴϨ̯Ϣγέΰϴϧ΍έϲϧ΍ϭ΍ήϓϝϭΪΟϢϴϧ΍ϮΗϲϣ ΪϨΘδϫΎϬ̯ΪλPercentileϭΎϫ̮ϫΩCutpoints ˬΎϬ̯έΎ̩QuartilesϞϣΎηPercentile Values ϲ˰ϣϊ˰ϳίϮΗέΎ˰ϴόϣ Distributionˬϲ̳Ϊ˰Ϩ̯΍ή̡έΎϴόϣDispersionˬΰ̯ήϤΗϱΎϫκΧΎηCentral Tendency ΪϨηΎΑ ϢϴϨ̯ΏΎδΣ΍έϲ̳Ϊϴθ̯ϭϲ̴ϟϮ̩Ϣϴϧ΍ϮΗϲϣΪϨηΎΑΕϭΎϔΘϣϭϩΪϨ̯΍ή̡ΎϫϩΩ΍Ωή̳΍ ̮˰ϴΗ΍έDisplay frequency table Ϫ˰Ϩϳΰ̳Ϊ˰ϳΎΑϢϴϫ΍Ϯ˰ΨΑ΍έϲ˰ϧ΍ϭ΍ήϓϝϭΪ˰ΟϭΪϨ˰ηΎΑϱέ΍ή̰ΗΎϫϩΩ΍Ωή̳΍ ϢϴϧΰΑ   ƾĭŶǀƄĩƩŚƯźƳŹŵřƺưƳ  ΪηΎΑϲϣkutosis>0ϩΪϴθ̯ΖϟΎΣέΩϭkutosis<0ϲΨ̡ΖϟΎΣέΩ Ζγ΍Ϊϣ!ϪϧΎϴϣ!Ϧϴ̴ϧΎϴϣϭskewness<0 ̠̩ϪΑϲ̴ϟϮ̩ΖϟΎΣέΩ Ϧϴ̴ϧΎ˰ϴϣ!Ϫ˰ϧΎϴϣ!Ϊ˰ϣϭ skewness>0Ζ˰γ΍έϪ˰Αϲ̴ϟϮ˰̩Ζ˰ϟΎΣέΩ Ζγ΍       3


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř 

Type

Width

Decimal

Label

car

Numeric

8

0

car

2

place

Numeric

8

0

3

time

Numeric

8

2

4

fuel

Numeric

8

2

Value

Align

Measure

10

left

Nominal

-

10

left

Nominal

-

10

left

Scale

-

10

left

Scale

lin

ab

et

.K



Missing column -

1=ca 2=taxi 3=bus 4=vanet place 0=in center 1=out center time  fuel

 ΎϫήϴϐΘϣΏΎΨΘϧ΍

e.

Name

 1

co

m

˻ϝΎΜϣ ϱΎ˰ϫκΧΎ˰ηβ̢˰γΪϴϨ̯Ωέ΍ϭϪΤϔλέΩ΍έήϳίΕΎϋϼσ΍ϭϩΩή̯ϒϳήόΗ΍έϥΎ̰ϣϭϥΎϣίˬϦϴηΎϣωϮϧϱΎϫήϴϐΘϣ ΍έϲϨϴ˰ηΎϣωϮ˰ϧϪ˰̩ςγϮΗϭϥΎϣίϡ΍Ϊ̯έΩΩΩήΗϥ΍ΰϴϣϪΠϴΘϧΪϴϳΎϤϧϪΒγΎΤϣ΍έϲ̳ΪϨ̯΍ή̡ϭϊϳίϮΗˬϱέΎϣ΁ϒϠΘΨϣ ΪϴϳΎϤϧκΨθϣ ΖΧϮγϥ΍ΰϴϣ ϥΎϣί ϥΎ̰ϣ ϦϴηΎϣωϮϧ ̌̋ ˺˻ ΡήσϞΧ΍Ω ϲμΨη ˺̋ ˺˼ ΡήσΝέΎΧ ϲμΨη ˺̊ ˺̊ ΡήσϞΧ΍Ω ϲμΨη ˺̊ ˺˼ ΡήσΝέΎΧ ϲδ̯ΎΗ ˻˹ ˺̊ ΡήσϞΧ΍Ω ϲδ̯ΎΗ ˻̌ ˺̋ ΡήσΝέΎΧ αϮΑϮΗ΍ ˼˹ ˺̌ ΡήσϞΧ΍Ω Ζϧ΍ϭ ˼˹ ˺̌ ΡήσΝέΎΧ αϮΑϮΗ΍ ̋˹ ˺̀ ΡήσϞΧ΍Ω ϲδ̯ΎΗ ̀ ˺̋ ΡήσΝέΎΧ ϲμΨη ́ ˺˼ ΡήσϞΧ΍Ω Ζϧ΍ϭ

w

w w

Ωέ΍ΩΖϴϤϫ΍ϲϧ΍ϭ΍ήϓϝϭΪΟϭΖδϴϧϢϬϣϱέΎϣ΁ϱΎϫκΧΎηΪϧ΍ϩΪηϱΪϨΑΪ̯Ϫ̯ϲϳΎϫήϴϐΘϣϱ΍ήΑ valid percentϭpercentϥϮΘ˰γϭΩϦϴ˰Αϲ˰ϧ΍ϭ΍ήϓϝϭΪ˰ΟέΩϢϳΩή˰̯ϲ˰ϣϒ˰ϳήόΗmissingή̳΍ΎΠϨϳ΍έΩΎϣ΍ ΪηϲϣΩΎΠϳ΍ΕϭΎϔΗ Ύ˰˰ϫϩΩ΍Ωή˰̳΍Ϫ˰̯Ζ˰γ΍ϲ ˰όϤΠΗΪ˰λέΩϩΪ˰˰ϨϫΩϥΎθ˰ϧϲ ˰ϧ΍ϭ΍ήϓϝϭΪ˰ΟέΩCumulative percentϥϮΘ˰γ Ωέ΍ΪϧϲϨόϣϥϮΘγϦϳ΍ΪηΎΑNominal Ϣϴϫ΍ϮΧϲϣΎϣΎϣ΍Ζγ΍ϩΪηέ΍ή̰ΗέΎΑ̮ϳΩέϮϣήϫ˱ΎΒϳήϘΗϭΪϫΪϴϤϧϥΎθϧϱΰϴ̩ΖΧϮγϲϧ΍ϭ΍ήϓϝϭΪΟ ϢϴϨ̯ϲϣϲϓήόϣ΍έGfuelϡΎϧϪΑϱΪϳΪΟήϴϐΘϣ΍άϟϢϴΠϨδΑϒϠΘΨϣϱΎϫϪϠλΎϓέΩ Transform-Record-Into Different Variable ϲϣ΍έold and new valueϪϤ̳Ωβ̢γˬϩΩί΍έchangeϭϢϴϨ̯ϲϣΝέΩ΍έΪϳΪΟήϴϐΘϣΐδ̩ήΑϭϡΎϧ ΖϤδϗέΩϢϴϧί ̮˰ϳϪ˰Α̮˰ϳήχΎ˰ϨΗϭϢϴ˰ϫΩΖΒδ˰ϧΪ˰ϳΪΟή˰ϴϐΘϣϢϳΪ˰ϗήϴϐΘϣέ΍ΪϘϣήϫϪΑϢϴϫ΍ϮΧϲϣή̳΍Old value Ϊ˰ϳΪΟΞ˰ϧέ̮˰ϳϢϳΪ˰ϗή˰ϴϐΘϣέ΍Ϊ˰Ϙϣή˰ϫϪ˰ΑϢϴϫ΍Ϯ˰Χϲϣή̳΍ϭϢϴϧίϲϣ̮ϴΗ΍έvalueϪϨϳΰ̳ϢϴϨ̯΍έ΍ήϗήΑ ΎΠϨϳ΍έΩϪ̯ϢϴϨ̯ϲϣϩΩΎϔΘγ΍RangeϪϨϳΰ̳ί΍ϢϴϨ̯ϒϳήόΗ Range<10 1 10<Range<20 2 20<Range<30 3 Range>30 4

4


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 Ϣϴ˰Ϩ̯ϲϣϒϳήόΗvalueέΩϢθηϥϮΘγέΩGfuelήϴϐΘϣϒϳήόΗΖϤδϗέΩβ̢γϭϢϴϧίϲϣ΍έaddϭ ˮΖδϴ̩̊ϭ˺ˬ˻ˬ˼Ω΍Ϊϋ΍ί΍έϮψϨϣ  

Name

Type

Width

Decimal

Label

1

Gfuel

Numeric

8

0

Gfuel

Value 1=low 2=middle 3=high 4=very high

Missing column -

10

Align left

Measure Nominal

 

m

Ύ˰ΗϢϴ˰Ϩ̯ϲ˰ϣϪΒ˰γΎΤϣfuelήϴϐΘϣϱ΍ήΑ΍έϱέΎϣ΁ϱΎϫκΧΎηϭGfuel ήϴϐΘϣϱ΍ήΑ΍έϲϧ΍ϭ΍ήϓϝϭΪΟϝΎΣ ΪηΎΑϪΘη΍ΩϲϨόϣ 

co

ŸƾūƹźųƹŪƿŚŤƳŚƿŢſřƮƸƯšŚƗLjƏřŵƹŹƹƾƬƇřƶŰƠƇƵŶƳźǀĭƁŹřżĭŵźƟƽřźŝæƩřƹŶſ

e.

ŸƮǀƴĩũŹŵƾƬƇřƶŰƠƇŹŵřŹŚƷƵŵřŵƮǀƳřƺţƾƯŹƺƐģƮǀƃŚŝƶŤ��řŵřŹƾƳřƹřźƟƩƹŶūźĭřçƩřƺŘſ

.K

et

ab

lin

ŸŚƸƷƹźĭŵřŶƘţźƔƳŻřƹƶƬƇŚƟƩƺƏźƔƳŻřŸŢƀǀģŚƷƵŵřŵƽřźŝŮǀŰƇƽŶƴŝƵŵŹƶƤƿźƏèƩřƺŘſ      

w w

 

w

         

5


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

íëææçê±¹Á{Ĉ¸m ƾƳřƹřźƟƩƹŶū   X ŚƷźǀƜŤƯ



f i ƾƳřƹřźƟ

ƮƬĜƿŵ æ



fi

f

ƾŞƀƳƾƳřƹřźƟŚƿƩŚưŤůř

ƾƘưŬţƾƳřƹřźƟ

ƾƘưŬţƾŞƀƳƾƳřƹřźƟ









êå

åç

i

ƮƬĜƿŵơƺƟ ç žƳŚƀǀƫ è …………….…… ……………………………………….. žƳŚƀǀƫơƺƟ é řźŤĩŵ ê

e.

co

m

 Ζγ΍ϝΎϤΘΣ΍ϥΎϤϫϪ̯Ζγ΍ΕϻΎΣϞ̯ϪΑΏϮϠτϣΕϻΎΣΩ΍ΪόΗϩΪϨϫΩϥΎθϧϲΒδϧϲϧ΍ϭ΍ήϓ ϖ˰Βσή˰̳΍΍ά˰ϟϥ΁Ϊ˰λέΩ̶˰όϤΠΗ̶Βδϧ̶ϧ΍ϭ΍ήϓϭΪηΎΑ̶ϣΩϮΧί΍ϞΒϗΕϻΎΣωϮϤΠϣ̶όϤΠΗ̶ϧ΍ϭ΍ήϓ ΪϨΘδϫβϧΎδϴϟήҨίΕϼϴμΤΗ̵΍έ΍Ω˻˹ΎҨήϔϧ̋˹Ϫ̯ΩϮη̶ϣκΨθϣβϧΎδϴϟ̵΍ήΑϻΎΑϝϭΪΟ Ζγ΍ϩΪηΩέ΍ϭmissing Ϫ̯Ζγ΍̶ҨΎϫϩΩ΍ΩϪΑρϮΑήϣInvalid Percent ϥϮΘγ 

w w

.K

et

ab

lin

˼ϝΎΜϣ ΍έ̶Ϡ˰λ΍ϩΩ΍ΩϝϭΪ˰ΟϝΎΣΪηΎΑ̶ϣ̶ϧ΍ϭ΍ήϓϝϭΪΟϦҨ΍Ϫ̯Ζγ΍ήҨίΡήηϪΑϩέ΍Ω΍̮ҨϥΎϨ̯έΎ̯ΕϼϴμΤΗΖδϴϟ ΪϴϨ̯ΩΎΠҨ΍ ̭έΪϣΪ̯ Ω΍ΪόΗ ̶ϠϴμΤΗ ƮƬĜƿŵ æ ˺̋ ƮƬĜƿŵơƺƟ ç ˺̀ žƳŚƀǀƫ è ˺˹ žƳŚƀǀƫơƺƟ é ̋ řźŤĩŵ ê ˼



1 2

Name

madrak frequency

w

Ϣϴ˰Ϩ̯ϒҨήόΗ΍έ̶ϧ΍ϭ΍ήϓϡΎϧϪΑ̵ήϴϐΘϣϢҨέϮΒΠϣΎϣ΍Ζγ΍̶ϠϴμΤΗ̭έΪϣϥ΁ϭϢҨέ΍ΩήϴϐΘϣ̮ҨΎϣΎΠϨҨ΍έΩ ϢϴϨ̯Ωέ΍ϭϝϭΪΟέΩ΍έΕΎϋϼσ΍ΎΗ  ΎϫήϴϐΘϣΏΎΨΘϧ΍ Type

Width

Decimal

Numeric Numeric

8 8

-

Label Value -

-

Missing -

column 10 10

Align left left

Measure Scale Scale

  έ΍ή̰ΗέΎΒ̰Ҩϡ΍Ϊ̯ήϫϥϮ̩Ϊϧί̶ϣ̮Ҩ΍έϪϤϫϢϴϫΩϞϴ̰θΗ̶ϧ΍ϭ΍ήϓϝϭΪΟ madrakήϴϐΘϣ̵΍ήΑϥϻ΍ή̳΍ ήϴϐΘϣϪΑ΍έϥίϭ ϢϴϨ̯ϒҨήόΗέ΍ΩϥίϭήϴϐΘϣΪҨΎΑβ̡ϢҨέ΍ΩϢϠ̢ҨΩήϔϧ ˺̋ΎϣϪ̶̯ΗέϮλέΩ Ζγ΍ϩΪη ΖγέΩ ϪΠϴΘϧ ϭ ϢҨήϴ̳ ̶ϣ madrak ήϴϐΘϣ αΎγ΍ ήΑ ΍έ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ ̶ϟϭϢϴϫΩ ̶ϣ ΖΒδϧ ̶ϧ΍ϭήϓ ΩϮη̶ϣϩΩ΍ΩϥΎθϧ Data – Weight Case Ϫ̯ϢϴϤϬϓ̶ϣϪΤϔλϦϴҨΎ̡Ζγ΍έΖϤγέΩ Weight OnϪϧΎθϧΎΑϢϴϨ̯ίΎΑ΍έϪΤϔλϦҨ΍Ϫ̯ϥΎϣίήϫ Ϊϧέ΍ΩϥίϭΎϫϩΩ΍Ω  6


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

̊ϝΎΜϣ ΏΎδ˰Σ΍έΪ˰ϣΖϴδ˰ϨΟή˰ϴϐΘϣ̵΍ή˰ΑϭϦϴ̴ϧΎ˰ϴϣϥίϭϭϦ˰γήϴϐΘϣ̵΍ήΑϢҨέ΍Ω΍έϥίϭϭϦγˬΖϴδϨΟήϴϐΘϣϪγ ΪϴϨ̯

ΎϫήϴϐΘϣΏΎΨΘϧ΍ 

Name

Type

Width

Decimal

Label

1

Gender

Numeric

8

-

-

2 3

Age Weight

Numeric Numeric

8 8

-

-

Value

Missing column

1=male 2=female -

Align

Measure

-

10

left

Nominal

-

10 10

left left

Scale Scale

lin

e.

co

m

 ϝϭΪ˰Ο̶ϟϭΖγ΍ϩΪηέ΍ή̰ΗέΎΒ̰ҨϩΩ΍ΩήϫϪ̯ΩϮη̶ϣκΨθϣ̶ϧ΍ϭ΍ήϓϝϭΪΟϥΪηκΨθϣί΍β̡ ΍έΎ˰ϫϩΩ΍ΩΪҨΎΑϞ̰θϣϦҨ΍ϊϓέ̵΍ήΑΪηΎΑ̋ί΍ήΘϤ̯ϥ΁̵ΎϫϩΩ΍Ω˻˹ήΜ̯΍ΪΣϪ̯Ζγ΍ΏϮΧ̶ϧ΍ϭ΍ήϓ ΩϮηήΘθϴΑ̶ϧ΍ϭ΍ήϓΎΗϢϴϨ̵̯ΪϨΑϩΩέ įŶƴŝƵŵŹƩƹřƁƹŹ Max  Min 50  20   6 ϢϴϨ̶̯ϣϩΩΎϔΘγ΍ϝϮϣήϓϦҨ΍ί΍ϦγήϴϐΘϣ̵ΪϨΑϩΩέ̵΍ήΑ  5 5 ϪϨҨΰ̳ί΍̵ΪϨΑϩΩέΩΎΠҨ΍̵΍ήΑΪҨ΁̶ϣΖγΩϪΑ̌ϩίΎΑϝϮσϭϢϴϨ̶̯ϣΏΎΨΘϧ΍̋΍έϩΩέΩ΍ΪόΗ Transform – recode- into different variable

ƵŵŹƵŶƴƿŚưƳ

İƳřƹřźƟ

.K

įŶƴŝƵŵŹ

et

ab

ϢϴϨ̶̯ϣϒҨήόΗAge groupϡΎϧϪΑ΍έΪҨΪΟήϴϐΘϣ ̋̊̊́̊̀̊˺̊˹˼̊˼˼˻̀˻˹˻̌ ̶ϣϩΩΎϔΘγ΍Ζγ΍ϩΪη̵ΪϨΑϩΩέϪ̯Age groupΪҨΪΟήϴϐΘϣί΍̶ϧ΍ϭ΍ήϓϝϭΪΟϪΒγΎΤϣ̵΍ήΑϪΘ̰ϧ ϩΩΎϔΘ˰γ΍Age ̶˰ϨόҨ̶˰ϟϭ΍ή˰ϴϐΘϣί΍Ϧϴ̴ϧΎ˰ϴϣϞ˰Μϣ̵έΎ˰ϣ΁̵Ύ˰ϫκΧΎ˰ηϪϴϘΑϪΒγΎΤϣ̵΍ήΑ̶ϟϭϢϴϨ̯ ϢϴϨ̯ϦϴϴόΗϩΪϨҨΎϤϧϩΩέήϫ̵΍ήΑή̴ϣΩέ΍Ϊϧ̶ϨόϣϩΪη̵ΪϨΑϩΩέήϴϐΘϣϦϴ̴ϧΎϴϣ΍ήҨίΩή̯Ϣϴϫ΍ϮΧ   20  26  23 2  ˻̀˼˼ 27  33  30  2  fi xi Ζγ΍ϡ΍iϩΩέϩΪϨҨΎϤϧ x ˬϦϴ̴ϧΎϴϣϝϮϣήϓϖΒσϭ  X  i  fi

˻˹˻̌

w

w w



ϢϴϨ̶̯ϣκΨθϣvalue ΖϤδϗέΩ΍έϥ΁Variable viewΖϤδϗέΩAge groupΪҨΪΟήϴϐΘϣϒҨήόΗΎΑ  

Name

Type

Width

Decimal

Label -

1

Gender

Numeric

8

-

2 3 4

Age Weight Agegroup

Numeric Numeric Numeric

8 8 8

-

Value

1=male 2=female grouping 1=20-26 variable 2=27-33 for age 3=34-40 4=41-47 5=48-54

7

Missing column Align

Measure

-

10

left

Nominal

-

10 10 10

left left left

Scale Scale Scale


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 Ϫ˰Α̶˰ϧ΍ϭ΍ήϓϝϭΪ˰ΟΪ˰ηΪ˰ϫ΍ϮΧήΘϬΑ̶ϠΒϗϝϭΪΟί΍Ϫ̯ϢϴϨ̶̯ϣϢγέ̶ϧ΍ϭ΍ήϓϝϭΪΟΪҨΪΟήϴϐΘϣ̵΍ήΑ Ζ˰γΪΑ̵ήΘϬΑ̶ϧ΍ϭ΍ήϓϝϭΪΟΎΗΩή̯ήΘ̳έΰΑ΍έϩΩέϝϮσϭήΘϤ̯΍έΎϫϩΩέϥ΍ϮΘϴϣΩέ΍Ω̶̴ΘδΑΎϣ̵ΎϫϩΩέ Ωέϭ΁ grouping variable for age

Frequency Valid

Percent

Cumulative Percent

Valid Percent

20-26

1

10.0

10.0

10.0

27-33

5

50.0

50.0

60.0

34-40

2

20.0

20.0

80.0

41-47

1

10.0

10.0

90.0 100.0

48-54

1

10.0

10.0

10

100.0

100.0

co

m

Total

įŶƴŝƵŵŹƭƹŵƁƹŹ

e.

Transform - visual BanderŚƿvisual Binning



w

w w

.K

et

ab

lin

ΩϮη̶ϤϧϡΎΠϧ΍NominalήϴϐΘϣ̵΍ήΑ̵ΪϨΑϩΩέϪ̯ΪϴηΎΑϪΘη΍ΩΖϗΩ  visual BinningΪҨΪΟϩήΠϨ̡έΩϢϴϧΰϴϣcontinueϭϢϴϨ̶̯ϣΏΎΨΘϧ΍΍έweight ήϴϐΘϣϩΪηίΎΑϩήΠϨ̡έΩ ϞΤϣϭρΎϘϧMake cutpointsϪϤ̳ΩϥΩίΎΑϝΎΣgweightϢϴϨ̶̯ϣΩέ΍ϭ΍έΪҨΪΟήϴϐΘϣΐδ̩ήΑϭϢγ΍ ϢҨίΎγ̶ϣ΍έΖδ̰η  Ωέ΍ΩΩϮΟϭΖδ̰ηρΎϘϧϦϴϴόΗ̵΍ήΑϩ΍έϪγ Equal Width Intervals ˺ Ϫ˰̯ϢϴϫΪ˰ϴϣΖ˰γ΍̊˹Ύ˰ΠϨҨ΍έΩϭΪηΎΑΎϣmin΍έϝϭ΍ϪτϘϧ ̵Ϊ˰˰όΑϢϴ˰Ϩ̯Ωέ΍ϭ΍έ˼̂̋ΩΪ˰ϋ̶̴Θ˰γϮϴ̵̡΍ή˰ΑΖ˰˰γ΍ή˰ΘϬΑ ϩΩέϝϮ˰σ̶ϣϮ˰γϭϢϴ˰Ϩ̶̯˰ϣΩέ΍ϭ˼Ϫ̯ΖγΎϫϩΩέΩ΍ΪόΗ Ω΍Ϊ˰όΗΪ˰ϴϫΪΑ΍έϩΩέϝϮ˰σή˰̳΍Ϊδ˰ҨϮϧ̶˰ϣεΩϮΧϪ̯Ζγ΍ ΖϤδϗέΩ΍έϩΩέΩΪϋϦҨήΧ΁ϭΪϫΩ̶ϣεΩϮΧ΍έϩΩέ .ΪϫΪϴϣϥΎθϧεΩϮΧ΍έ Last cutpoint location  Equal Percentiles Based on Scanned Cases2 ΍έΩήϴ̴ϴϣmax-minϪϠλΎϓί΍ϩΩέήϫϪ̵̯ΪλέΩϭϩΩέΩ΍ΪόΗ ϢϴϫΪϴϣ Cutpoints at Mean and Selected Standard Deviation˼  Based on Scanned Cases

ΪϨ̰ϴϣϦϴϴόΗ΍έΎϫϩίΎΑέΎϴόϣϑ΍ήΤϧ΍ϪγΎҨϭΩˬ̮ҨαΎγ΍ήΑ  ΍έϩΩέn+1Ω΍Ϊ˰όΗΖδ˰̰ηϪ˰τϘϧnΩ΍Ϊ˰όΗΩϮηϩΩΎϔΘγ΍Ϫ̯ϪϨҨΰ̳ϪγϦҨ΍ϡ΍Ϊ̯ήϫί΍ϝΎΣήϫϪΑϪΘ̰ϧ Ζη΍ΩΪϫ΍ϮΧ ΪϧϮη̶ϣήϫΎχϢϫΎϫϩΩέϩήΠϨ̡ϥΎϤϫέΩMake LabelϪϤ̳ΩϥΩίΎΑ 8


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

(έΎ˰ϴόϣϑ΍ή˰Τϧ΍ϭ)  έΎ˰ϴόϣϑ΍ή˰Τϧ΍ 

Ϧϴ̴ϧΎ˰ϴϣήҨΩΎ˰ϘϣΎ˰Α̶˰ϨΤϨϣϦҨ΍ωΎϔΗέ΍ Ωέ΍ΩρΎΒΗέ΍ ϭέ΍Ϊ˰Ϙϣ̮˰ҨϝϮ˰ΣΎ˰ϫϩΩ΍ΩϢ̯΍ήΗϥ΍ΰϴϣ Ζγ΍Ϧϴ̴ϧΎϴϣί΍ΎϫϩΩ΍Ω̶̳ΪϨ̯΍ή̡ϥ΍ΰϴϣ 

lin

e.

co

m

ϥ΍ίϮ˰ϣ΁ζ˰ϧ΍ΩΕ΍ή˰Ϥϧ̶˰γέΩϥΎ˰ΤΘϣ΍̮˰ҨέΩϝΎ˰Μϣ̵ϥ΍ϮϨϋϪΑ ΖϤ˰γϪ˰ΑϪ˰̩ή˰ϫϭΪ˰ηΎΑ̶˰ϣήΘθ˰ϴΑ Ϧϴ̴ϧΎ˰ϴϣϑ΍ή˰σ΍ΐϠϏ΍ ΍έΕ΍ή˰ϤϧϦ˰Ҩ΍Ϫ˰̯ ̵Ω΍ή˰ϓ΍Ω΍Ϊ˰όΗϢҨϭή˰Αζϴ˰̡ϦϴҨΎ̡ΎҨϻΎΑΕ΍ήϤϧ ̮˰Ҩ Ύ˰Αϥ΍ϮΗ̶ϣΖϟϮϬδΑ΍έέΎΘϓέϦҨ΍ΩϮη̶ϣήΘϤ̯Ϊϧ΍ϪΘϓή̳ .Ωή̯ϝΪϣϝΎϣήϧϊҨίϮΗ       

ab

ŶƿŶūźǀƜŤƯįƹŹźŝƍƹźƄƯšŚŞſŚŰƯƭŚŬƳřƹŶƿŶūźǀƜŤƯŵŚŬƿřƁƹŹ

w w

.K

et

ϢҨΩή̶̯ϣϒҨήόΗ΍έ̵ΪҨΪΟήϴϐΘϣTransform – recode- into different variable εϭέί΍ϩΩΎϔΘγ΍ΎΑ ϢҨέϭΎϴΑΖγΪΑ΍έ̵ΪҨΪΟήϴϐΘϣϪΒγΎΤϣεϭέΎΑϢϴϫ΍ϮΧ̶ϣϝΎΣ̶ϟϭ  Transform – Compute Numeric ΖϤδ˰˰ϗέΩ΍έϝϮ˰˰ϣήϓϭϢϴ˰Ϩ̶̯˰˰ϣΝέΩ΍έΪ˰˰ҨΪΟή˰˰ϴϐΘϣϡΎ˰˰ϧTarget VariabeleΖϤδ˰˰ϗέΩ ΏΎδΣϡή̳ΐδΣήΑ̵ή̴ҨΩϥϮΘγέΩ΍έWeightήϴϐΘϣϢϴϫ΍ϮΧ̶ϣή̳΍˱ϼΜϣϢϴδҨϮϧ̶ϣExpression   ϢϴϨ̯ΏΎΨΘϧ΍΍έιΎΧ̶όΑΎΗΎҨWeight 1000 ϢϴδΑϮϨΑ΍έϝϮϣήϓϦҨ΍̶ϧ΍ϮΗ̶ϣϢϴϨ̯

w

ΩϮ˰η̶Ϥϧ΍ήΟ΍ϝϮϣήϓϩΩ΍ΩήϴϴϐΗΎΑSPSS έΩϪ̯Ζγ΍ϦҨ΍ExcelΎΑSPSSέΩ̶δҨϮϧϝϮϣήϓϕήϓϪΘ̰ϧ ή˰ϴϴϐΗ΍έϩΩ΍ΩϩΎ˰̳ή˰ϫExcelέΩ̶˰ϟϭΩϮ˰η΍ή˰Ο΍ϝϮ˰ϣήϓ˱΍ΩΪ˰ΠϣΖδҨΎΑ̶ϣϭΩΩή̶̳ϤϧίϭέϪΑϪΠϴΘϧϭ ΪϨ̶̯ϣήϴϴϐΗΰϴϧϪΠϴΘϧϢϴϫΩ Ϊ˰Σ΍ϭή˰ϴϴϐΗ˱ϻΎΜϣΩϮηϡΎΠϧ΍̶λΎΧ̵ΎϫϩΩ΍Ω̵΍ήΑςϘϓϭ̶λΎΧςҨ΍ήηΖΤΗϝϮϣήϓϦҨ΍Ϣϴϫ΍ϮΨΑή̳΍ ˱ϼΜ˰ϣϢϴ˰Ϩ̯ϩΩΎϔΘ˰γ΍Computeϩή˰ΠϨ̡ϦϴҨΎ˰̡ifϪ˰Ϥ̳Ωί΍ΪҨΎΑΩϮηϡΎΠϧ΍ΎϬϤϧΎΧ̵΍ήΑςϘϓϡή̳ϪΑϥίϭ Gender =1& Age>30 ΎҨGender =1 Ϫ̯ϢҨέ΍ΰ̴Αρήη ̵΍ή˰ΑϭϢҨ΍έά˰̴Α˺̶˰λΎΧ̵Ύ˰ϫϩΩ΍Ω̵΍ήΑϢϴϨ̯ϒҨήόΗselectϡΎϧϪΑήϴϐΘϣ̮ҨϢϴϧ΍ϮΗ̶ϣϪ̰ϨҨ΍ή̴ҨΩϩ΍έ Ύϫήϔλ̵΍ήΑΎҨϢϴϨ̯ΏΎδΣΎϬ̰Ҩ̵΍ήΑ΍έϝϮϣήϓΪόΑ˹ή̴ҨΩ̵ΎϫϩΩ΍Ω

9


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř SPSS ŹŵƵŶƃřźūřšřŹƺŤſŵƕřƺƳřŢŞŧƁƹŹ

̶ϣΝέΩΎϣϥΎϣήϓϭϩΪη ίΎΑ syntax ϩήΠϨ̡ˬΕ΍έϮΘγΩί΍̵΍ϩήΠϨ̡ήϫέΩ pasteϪϤ̳Ωί΍ϩΩΎϔΘγ΍ΎΑ ϊϗϮϣΖγ΍ήΘϬΑΩή̯΍ήΟ΍˱΍ΩΪΠϣ΍έϦϴϣ΍ήϓRunέϮΘγΩΎΑϥ΍ϮΗ̶ϣϩήΠϨ̡ϦҨ΍ΩΪΠϣϥΩή̯ίΎΑΎΑ̶ΘΣΩϮη ΩϮηΝέΩϥ΁έΩΕ΍έϮΘγΩΎΗΪηΎΑίΎΑϢϫsyntaxϪΤϔλspssϪΤϔλϥΩή̯ίΎΑ   řżŬƯįŚƷİūƹźųƱŵŹƹōŢſŶŝƹŚƷƵŵřŵĨǀĪƠţ

w

w w

.K

et

ab

lin

e.

co

m

Data viewϪΤϔ˰λέΩΪ˰ҨΪΟή˰ϴϐΘϣComputeϩή˰ΠϨ̡ί΍ϩΩΎϔΘ˰γ΍ΎΑϪ̯ΖδϨҨ΍̶ϠΒϗϩ΍έΎΑϩ΍έϦҨ΍ϕήϓ ϥΩή˰̯ή˰ΘϠϴϓωϮ˰ϧ̮˰ҨϦ˰Ҩ΍ϭΩϮ˰η̶ϣϞλΎΣΕ΍ήϴϴϐΗoutputϭ̶ΟϭήΧέΩϩ΍έϦҨ΍ΎΑ̶ϟϭΩϮθϴϣΩΎΠҨ΍ Ζγ΍  ϝϭ΍εϭέ  Data – Select Cases Ϫ˰ΑΪ˰ηΎΑϝΎ˰όϓAll Casesή˰̳΍Ϫ˰̯ΩϮ˰η̶ϣίΎΑϭήΑϭέϩήΠϨ̡ if condition is ϥΩή˰̯ϝΎ˰όϓΎ˰ΑϭΖ˰γ΍ή˰ΘϠϴϓϡΪ˰ϋ̵Ύ˰Ϩόϣ ̶˰ϣρή˰η˱ϼΜ˰ϣϢϴ˰Ϩ̶̯ϣΝέΩ΍έήψϧΩέϮϣρήηsatisfied ϡΎ˰Πϧ΍Ύ˰ΑϝΎ˰Σή˰̳΍Ϫ˰̯Ϊ˰Ϩ̯ϪΒ˰γΎΤϣ΍έΎϫΩήϣςϘϓϢҨέ΍ά̳ ϭΪ˰Ϩ̰ϴϣϪΒ˰γΎΤϣΎϫΩήϣ̵΍ήΑςϘϓϢҨήϴ̴ΑϦϴ̴ϧΎϴϣή̳΍ήΘϠϴϓ ϭΪθ˰˰̶̯˰˰ϣς˰˰Χ΍έΎ˰˰Ϭϧί̵Ύ˰˰ϫΩέϮ˰˰̯έϢ˰˰ϫData viewέΩ ̵΍ή˰˰ΑϥΪ˰˰ηΏΎ˰˰ΨΘϧ΍ϥ΁έΩϪ˰˰̯Ϊ˰˰Ϩ̶̯˰˰ϣΩΎ˰˰ΠҨ΍̶ϧϮΘ˰˰γ ΪϫΩ̶ϣϥΎθϧ΍έΕΎΒγΎΤϣ   ΪϨ̶̯ϣΏΎΨΘϧ΍̶ϓΩΎμΗϪϧϮϤϧ̮ҨRandom of casesΏΎΨΘϧ΍ΎΑ ϪΤϔ˰λϦϴҨΎ˰̡έΩϭΩϮ˰η̶˰ϣϡΎΠϧ΍ήΘϠϴϓokϥΩίΎΑϭuse filter availableέΩήΘϠϴϓΩέϮϣήϴϐΘϣΏΎΨΘϧ΍ΎΑ ΪηΪϫ΍ϮΧϩΩ΍ΩζҨΎϤϧfilter onΰϴϧ  ϡϭΩεϭέ ΪόΑϢҨέ΍Ω̶ϣήΑAll CasesϪϨҨΰ̳ϥΩίϭ΍Data – Select CasesεϭέΎΑ΍έ̶ϠΒϗ̵ΎϫήΘϠϴϓϝϭ΍ Data – Split Files  Ζγ΍ήΘϠϴϓϡΪϋϝϭ΍ϪϨҨΰ̳ ̶˰˰ΟϭήΧέΩ̵΍Ϫδ˰˰ҨΎϘϣΖ˰˰ϟΎΣέΩ΍έϝϭ΍Ϊ˰˰ΟϡϭΩϪ˰˰ϨҨΰ̳ ΪϫΩ̶ϣϥΎθϧ ΪηΪϫ΍ϮΧϩΩ΍ΩϥΎθϧϪϧΎ̳΍ΪΟ˱ϼϣΎ̯ϡϮγϪϨҨΰ̳ ϥΎθ˰ϧΎ˰ϬϧίϭΎ˰ϫΩήϣΖϤδ˰ϗϭΩϪ˰Α΍έgenderήϴϐΘϣϪ̯ Ω΍ΩΪϫ΍ϮΧ 

10


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

ΪϴϨ̯ίΎΑήҨίήϴδϣί΍΍έDemoϞҨΎϓ5ϝΎΜϣ My computer – win(programming) - program files - spssEVAL – Tutorial – sample files- Demo ϭΎ˰ϫϢϧΎ˰ΧΪ˰ϣ΁έΩϥ΍ΰϴϣϦϴ̴ϧΎϴϣβ̢γΪҨέϭ΁ΖγΩϪΑΎϫήϴϐΘϣΩ΍ΪόΗˬΎϫcaseΩ΍ΪόΗΩέϮϣέΩ΍έϡίϻΕΎϋϼσ΍ ή˰Α̶˰ϧ΍ϭ΍ήϓϝϭΪ˰ΟΪ˰ҨέϭΎϴΑΖγΪΑϦϴηΎϣωϮϧαΎγ΍ήΑ΍έΪϣ΁έΩ̶ϧ΍ϭ΍ήϓϝϭΪΟϭΪϴϨ̯ΏΎδΣϪϧΎ̳΍ΪΟ΍έϥΎҨΎϗ΁ ΪҨέϭΎϴΑΖγΪΑϩΪη̵ΪϨΑϩΩέΪϣ΁έΩαΎγ΍

 ƪƿŚƟĨƿŻřšŚƗLjƏřŸųř File – display data files informationϝϭ΍εϭέ Analyze-Report – Case SummariesϡϭΩεϭέ ϪϧΎ̳΍ΪΟϞ̰ηϪΑΎϬϧίϭΎϫΩήϣΪϣ΁έΩϥ΍ΰϴϣϦϴ̴ϧΎϴϣ̵΍ήΑϒϟ΍

co

m

Data – Split Files – gender- organize output by groups- ok Ϧϴ̴ϧΎϴϣ ϪΒγΎΤϣ ̵΍ήΑ ̶ϟ ϭ ϢϴϨ̯ ̶ϣ ΏΎδΣ inccat ΎҨ Ϊϣ΁έΩ ήϴϐΘϣ ̵΍ήΑ ̶ϧ΍ϭ΍ήϓ ϝϭΪΟ β̢γ  okβ̢γϭϢϴϧί̶ϣ̮ϴΗ΍έMeanϪϨҨΰ̳statisticsΖϤδϗέΩincomeήϴϐΘϣΐδΣήΑ΍έ̶ϧ΍ϭ΍ήϓ ΩϮη̶ϤϧϪΒγΎΤϣϩΪη̵ΪϨΑϩΩέήϴϐΘϣ̵΍ήΑϦϴ̴ϧΎϴϣ  Gender = Male

e.

Gender = Female

Missing Mean

Statistics(a) Household income in thousands N Valid 3221

lin

Statistics(a) Household income in thousands N Valid 3179

Missing

0

Mean

ab

68.7798

a Gender = Male

et

a Gender = Female

0 70.1608

563

17.7

17.7

Cumulative Percent 17.7

1208

38.0

38.0

55.7

572

18.0

18.0

73.7

836

26.3

26.3

100.0

3179

100.0

100.0

$25 - $49 $50 - $74 $75+ Total

w

Under $25

Percent

w w

Frequency Valid

.K

Income category in thousands(a)

Valid Percent

a Gender = Female Income category in thousands(a)

Valid

Frequency 611

Percent 19.0

Valid Percent 19.0

Cumulative Percent 19.0

$25 - $49

1180

36.6

36.6

55.6

$50 - $74

548

17.0

17.0

72.6

$75+

882

27.4

27.4

100.0

Total

3221

100.0

100.0

Under $25

a Gender = Male

11


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

ϦϴηΎϣωϮϧΐδΣήΑ̶ϧ΍ϭ΍ήϓϝϭΪΟΏ ϦҨ΍ ςγϮΗ ΪϫΪϴϣ ϥΎθϧ ΍έ ϦϴηΎϣ ω΍Ϯϧ΍ Ϫ̯ carcat  ήϴϐΘϣ β̡ ϢϴϨ̯ ήΘϠϴϓ ΍έ ϦϴηΎϣ ω΍Ϯϧ΍ ΪҨΎΑ ˱ϻϭ΍ Data – Split Files –carcat - organize output by groups- okέϮΘγΩ ϢϴϨ̶̯ϣΏΎδΣ΍έinccat ΎҨϩΪη̵ΪϨΑϩΩέΪϣ΁έΩήϴϐΘϣ̶ϧ΍ϭ΍ήϓέϮΘγΩΪόΑϢϴϨ̶̯ϣήΘϠϴϓ Income category in thousands(a)a Primary vehicle price category = Economy

Valid

Under $25

Frequency 1174

$25 - $49 Total

Percent 63.8

Valid Percent 63.8

Cumulative Percent 63.8 100.0

667

36.2

36.2

1841

100.0

100.0

Valid

1721

75.6

75.6

$50 - $74

554

24.4

24.4

100.0

2275

100.0

100.0

Valid Percent

e.

Total

Percent

co

$25 - $49

Cumulative Percent 75.6

Frequency

m

Income category in thousands(a)a Primary vehicle price category = Standard

$75+

1718

75.2

Total

2284

100.0

Valid Percent 24.8

Cumulative Percent 24.8

75.2

100.0

ab

$50 - $74

Percent 24.8

100.0

.K

et

Valid

Frequency 566

lin

Income category in thousands(a) - a Primary vehicle price category = Luxury

w w

ƵŶƃźŤƬǀƟŚƿsplitŢƫŚůŹŵcarcatźǀƜŤƯŚƿƲǀƃŚƯƕƺƳŽŚſřźŝinccat źǀƜŤƯŚƿƵŶƃįŶƴŝƵŵŹŶƯōŹŵİƳřƹřźƟƶŞſŚŰƯ

Primary vehicle price category Economy

w

Income category in thousands

Valid

Under $25 $25 - $49

Standard

Valid

Luxury

Valid

Total $25 - $49 $50 - $74 Total $50 - $74 $75+ Total

Cumulative Percent 63.8

Frequency 1174

Percent 63.8

Valid Percent 63.8

667 1841 1721 554 2275 566

36.2 100.0 75.6 24.4 100.0 24.8

36.2 100.0 75.6 24.4 100.0 24.8

100.0

1718 2284

75.2 100.0

75.2 100.0

100.0

75.6 100.0 24.8

ŪƿŚŤƳ

ŶƳŹřŵŹLJŵçêåååźƿŻŶƯōŹŵŶƳŹřŵEconomyƩŶƯƲǀƃŚƯƕƺƳƶĩëèíæ

ŶƳŹřŵŹLJŵÔÒÍÍÍįLJŚŝŶƯōŹŵŶƴƴĩİƯƵŵŚƠŤſřLuxury ƲǀƃŚƯƕƺƳŻřƶĩÔÒÏÏ

12


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

ƵŶƃźŤƬǀƟŚƿsplitŢƫŚůŹŵmartial źǀƜŤƯŚƿƪƷŚţƕƺƳŽŚſřźŝinccat źǀƜŤƯŚƿƵŶƃįŶƴŝƵŵŹŶƯōŹŵƲǀĮƳŚǀƯƶŞſŚŰƯ Income category in thousands

Valid Percent 17.9

Cumulative Percent 17.9

$25 - $49

1228

38.1

38.1

56.0

$50 - $74

552

17.1

17.1

73.1 100.0

Under $25

866

26.9

26.9

Total

3224

100.0

100.0

Under $25

596

18.8

18.8

18.8

$25 - $49

1160

36.5

36.5

55.3

$50 - $74

568

17.9

17.9

73.2

$75+

852

26.8

26.8

Total

3176

100.0

100.0

m

$75+

co

Valid

Percent 17.9

100.0

ŪƿŚŤƳ

e.

ŶƳŹřŵŹLJŵçêåååéîåååƲǀŝŶƯōŹŵŶƴŤƀƷŵźŬƯƶĩèíææ

.K

et

ab

lin

ŶƳŹřŵŹLJŵìêåååįLJŚŝƲǀŝŶƯōŹŵŶƴŤƀƷƪƷŚŤƯƶĩçëíÏ     

w w

Married

Valid

Frequency 578

  

w

Marital status Unmarried

         

13


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř 

agegroupΪҨΪΟήϴϐΘϣΩΎΠҨ΍ϭϦγήϴϐΘϣ̵ΪϨΑϩΩέ Transform-Visual binning – cutpoint 17.5 ƵŶƃźŤƬǀƟŚƿsplitŢƫŚůŹŵagegroupźǀƜŤƯŚƿİƴſƵŵŹŽŚſřźŝinccat źǀƜŤƯŚƿƵŶƃįŶƴŝƵŵŹŶƯōŹŵƲǀĮƳŚǀƯƶŞſŚŰƯ  Income category in thousands

39 - 48

Valid

52.3

52.3

52.3

319

40.7

40.7

93.0

$50 - $74

41

5.2

5.2

98.2

1.8

100.0

$75+

14

1.8

Total

784

100.0

Under $25

331

19.1

$25 - $49

929

53.5

$50 - $74

293

16.9

72.6

16.9

89.5 100.0

10.5

10.5

100.0

100.0

Under $25

124

$25 - $49

641

$50 - $74

453

7.0

7.0

36.0

36.0

43.0

25.4

25.4

68.4 100.0

563

31.6

31.6

100.0

100.0

et

Under $25

7.0

1781 73

5.6

5.6

5.6

341

26.0

26.0

31.6

247

18.8

18.8

50.4 100.0

.K

Valid

53.5

182

$75+

650

49.6

49.6

Total

1311

100.0

100.0

Under $25

169

26.0

26.0

26.0

$25 - $49

135

20.8

20.8

46.8

$50 - $74

75

11.5

11.5

58.3 100.0

w w

w 69+

19.1

1735

$50 - $74

Valid

19.1

Total

$25 - $49

59 - 68

100.0

$75+

Total Valid

m

410

$25 - $49

$75+ 49 - 58

Cumulative Percent

Under $25

co

Valid

Valid Percent

e.

Valid

Percent

lin

29 - 38

Frequency

ab

Age in years (Binned) 19 - 28

$75+

271

41.7

41.7

Total

650

100.0

100.0

Under $25

67

48.2

48.2

48.2

$25 - $49

23

16.5

16.5

64.7

$50 - $74

11

7.9

7.9

72.7 100.0

$75+

38

27.3

27.3

Total

139

100.0

100.0

ŪƿŚŤƳ ŹLJŵìêåååįLJŚŝŶƯōŹŵêåĨƿŵżƳéíêíİƴſƵŵŹŹŵƶĪǀƫŚůŹŵŶƳŹřŵŶƯōŹŵŹLJŵçêåååźƿŻêçèæîçíİƴſƵŵŹŹŵæ .ŶƳŹřŵ

14


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

íëæç籹‡Ĉ¸m ƶƳƺưƳƭŚƀƣř 

w

w w

.K

et

ab

lin

e.

co

m

Ϫ̯Ζγ΍ϪόϣΎΟ̮Ҩί΍ήμϨϋnϞϣΎηϩΩΎγ̶ϓΩΎμΗ̵ήϴ̳ϪϧϮϤϧ˺ Ωέ΍ΩήμϨϋήϫΏΎΨΘϧ΍̵΍ήΑ̵ϭΎδϣβϧΎηΎϬϴ̳̬Ҩϭ  ̶ϓΩΎμΗζҨΎϣί΁ Ωέ΍Ω̶ҨΎΗnϪϧϮϤϧήϫΏΎΨΘϧ΍̵΍ήΑ̵ϭΎδϣβϧΎη ϝϭΪΟί΍ϩΩΎϔΘγ΍ΎΑ Ϣ̯ϪϨҨΰϫΎΑ̵έ΍ά̳ϩέΎϤηϞΑΎϓΎҨϩΪη̵έ΍ά̳ϩέΎϤηϪόϣΎΟϩΩΎϔΘγ΍Ωέ΍Ϯϣ  ̶ϓΩΎμΗΩ΍Ϊϋ΍ Ϣ̯ϪϨҨΰϫϭΖϋήγϪΑ̶γήΘγΩΖϴϠΑΎϗ̵΍έ΍Ω  ΩϮη̶ϣϞϣΎη΍έϪόϣΎΟήμϨϋϦϴϣ IήϫϪ̯Ζγ΍̵΍ϪϧϮϤϧ̮ϴΗΎϤΘδϴγ̶ϓΩΎμΗ̵ήϴ̳ϪϧϮϤϧ˻ ΐγΎϨϣ ̶ϠϴΧ ήҨά̡ ΐϴΗήΗ ϊϣ΍ϮΟ ̵΍ήΑ Ζγ΍ ήϔλ ήϴϏ ϭ ϡϮϠόϣ Ϫ̯ ϪόϣΎΟ ί΍ ήμϨϋ ήϫ ϞϣΎη ̶ϨόҨ ί΍ ̵Ω΍ΪόΗ Ϫ̩ Ϣϴϧ΍ΪΑ Ϣϴϫ΍ϮΨϴϣ ˱ϼΜϣΩέ΍Ω ΕϭΎϔΗ ˱ϼϣΎ̯ ̵ή̴ҨΩ ΎΑ ϪϧϮϤϧ ήϫ ̵ήϴ̳ ϪϧϮϤϧ ϦҨ΍ έΩϭ Ζγ΍ ̵Ύϫ Ϧ̳΍ϭ ϝϭ΍ ̵ήϴ̳ ϪϧϮϤϧ έΩ ΪϧϮη ̶ϣ Ώ΍ήΧ ̶ΘϓΎδϣ ί΍ ΪόΑ ΎΟέ ̵ήΑ ήϓΎδϣ έΎτϗ ̵Ύϫ Ϧ̳΍ϭ ̋˱ϼΜϣΩΪϋΕϭΎϔΗΎΑ̵ή̴ҨΩ̵ΎϫϦ̳΍ϭϡϭΩ̵ήϴ̳ϪϣϮϤϧέΩϭϢϴϨ̶̯ϣΏΎΨΘϧ΍΍έϭ˺̋ϭ˺˹ϭ̋ N ϩΪη κΨθϣ ̵ήϴ̳ ϪϧϮϤϧ ϪϠλΎϓ k   ΪόΑ ϩΩή̯ ̵έ΍ά̳ ϩέΎϤη N ΎΗ ˺ ί΍ ΍έ ϪόϣΎΟ εϭέ ϦҨ΍ έΩ n Ϧϴϟϭ΍ϪΑ kϥΩϭΰϓ΍ΎΑϭΖγΎϣϪϧϮϤϧΪΣ΍ϭϦϴϟϭ΍Ϫ̯ϩΪηΏΎΨΘϧ΍ kΎΗ ˺ί΍ϑΩΎμΗϪΑΩΪϋ̮Ҩβ̢γ ΪҨ΁̶ϣΖγΪΑϢϫΎϫΪΣ΍ϭήҨΎγˬΪΣ΍ϭ ̮ҨΏΎΨΘϧ΍ϭΰҨΎϤΘϣ̵ΎϫϪϘΒσϪΑϱέΎϣ΁ϪόϣΎΟ̮ҨϢϴδϘΗϩΪη̵ΪϨΑϪϘΒσ̶ϓΩΎμΗ̵ήϴ̳ϪϧϮϤϧ˼ ϪϘΒσήϫί΍̶ϓΩΎμΗϪϧϮϤϧ ΎҨ΍ΰϣ ϪϘΒσήϫέΩ̶ϓΎ̯ήλΎϨϋΩϮΟϭί΍ϥΎϨϴϤσ΍ ϪόϣΎΟ̵ΎϫήΘϣ΍έΎ̡ί΍ήΘϬΑ̵ΎϫϦϴϤΨΗϥΩέϭ΁ΖγΪΑ ϪΑ΍έΎϬϧΎδϧ΍ή̳΍̶ϟϭΪҨ΁̶ϣΖγΪΑ̵΍ϪΠϴΘϧ̮ҨϢҨήϴ̴Α΍έϪόϣΎΟ̮Ҩ̵ΎϬϧΎδϧ΍ΪϗςγϮΘϣή̳΍˱ϼΜϣ ϪΠϴΘϧϢϴϨ̯ϪΒγΎΤϣϩΪη̵ΪϨΑϪϘΒσ̵ήϴ̳ϪϧϮϤϧέΩ΍έΪϗςγϮΘϣϭϢϴϨ̯ϢϴδϘΗΩήϣϭϥίϪϘΒσϭΩ ΪҨ΁̶ϣΖγΪΑ̵ήΘϬΑ ϦҨ΍ΪηΎΑϪϘΒσήϫϞΧ΍ΩέΩήλΎϨϋϑϼΘΧ΍ί΍ήΘθϴΑΕΎϘΒσϦϴΑήλΎϨϋϑϼΘΧ΍Ϫ̶̯ΗέϮλέΩ̶Ϡ̯Ϟλ΍ ΪϫΩ̶ϣ΍έ̵ήΘϘϴϗΩΞҨΎΘϧϩΩΎγ̶ϓΩΎμΗ̵ήϴ̳ϪϧϮϤϧεϭέϪΑΖΒδϧεϭέ ϞϴϣϝΎϣήϧΖϤγϪΑϥ΁ϊҨίϮΗΩϮη̱έΰΑϪϧϮϤϧϩί΍Ϊϧ΍ή̳΍̶ϓΩΎμΗ̵ήϴ̳ϪϧϮϤϧήϫέΩ̵ΰ̯ήϣΪΣϪϴπϗ Ωή̯Ϊϫ΍ϮΧ Ω ̯ ϫ΍ϮΧ ϥΩϮΑϦ̴ϤϫρήηϪ̯Ζγ΍ϪόϣΎΟήλΎϨϋί΍̶ҨΎϫϪηϮΧΏΎΨΘϧ΍̵΍ϪηϮΧ̶ϓΩΎμΗ̵ήϴ̳ϪϧϮϤϧ̊ ΩϮηΖҨΎϋέΪҨΎΑϥ΁έΩ ΐϠϏ΍ έΩ Ύϫ ϪηϮΧ ϞϴϜθΗ ϱΎϨΒϣ ϭ ΩϮη ϲϣ ϩΪϴϣΎϧ ϪηϮΧ ˱ΎΣϼτλ΍ Ϫϛ ϲϳΎϫ ΖϤδϗ ϪΑ ΍έ ϪόϣΎΟ ˬΖγ΍ ϱΩΎΑ΁ Ύϳ ϙϮϠΑ ϭ ήϬη ˬϥΎΘδϫΩ ˬϥΎΘγήϬη ˬϥΎΘγ΍ ϞϴΒϗί΍ ϲϳΎϴϓ΍ήϐΟ ϱΎϫ ϱΪϨΑ ϢϴδϘΗ ˬΕΎϗϭ΍ ϥΎϴΑ ϪΑ. ΪϨηΎΑ ϲϧΎηϮ̡ Ϣϫ ΪϗΎϓ ϭ ΪϨϫΩ ζηϮ̡ ΍έ ϪόϣΎΟ Ϟϛ ΪϳΎΑ Ύϫ ϪηϮΧ. ΪϨϨϛ ϲϣ ϱΪϨΑ ϢϴδϘΗ ΪηΎΑ ϪΘη΍Ω ϖϠόΗ ΎϫϪηϮΧϦϳ΍ ί΍ ϲϜϳ ςϘϓ ϭ ϲϜϳ ϪΑ ΪϳΎΑ ϪόϣΎΟ ήλΎϨϋ ί΍ ϡ΍Ϊϛ ήϫ ή̴ϳΩ ˬϱ΍ ϪϠΣήϣ ΪϨ̩ Ύϳ Ϛϳ ϱ΍ ϪηϮΧ ϱήϴ̳ ϪϧϮϤϧˬ ϩΪϴ̪ϴ̡ ϱήϴ̳ ϪϧϮϤϧ Ρήσ SPSS   ϱέ΍ΰϓ΍ ϡήϧ ϱ ϪΘδΑ .ΪϨϛ ϲϣ Ϣϫ΍ήϓ Complex Samples ΎΑ΍έˬϱΪϨΑ ϪϘΒσ ϱήϴ̳ ϪϧϮϤϧ  ŸŵŹřŵİĮŤƀŝİƬƯřƺƗƶģƶŝƶĪƴƿřƹŶǀƷŵŮǀƋƺţřŹƶƳƺưƳƵŻřŶƳřƲŤƟŚƿįŚƷƁƹŹƩřƺŘſ   15


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř  Crosstabs İƤƟřƺţƩƹřŶū

έΩ΍έή˰Ҩί̶Ϙϓ΍Ϯ˰ΗϝϭΪ˰ΟΩϭέ̶˰ϣέΎ˰̰Α̶Ϥ˰γ΍̵Ύ˰ϫή˰ϴϐΘϣϦϴΑρΎΒΗέ΍ϩΩ΍ΩϥΎθϧ̵΍ήΑ̶Ϙϓ΍ϮΗϝϭ΍ΪΟ ΪϴϨ̯Ωέ΍ϭέ΍ΰϓ΍ϡήϧϪΤϔλ ϢϴϨ̶̯ϣΏΎΨΘϧ΍΍έϞϐηϭΖϴδϨΟήϴϐΘϣϭΩ   ƱŻ ŵźƯ ϢϴϨ̶̯ϣϒҨήόΗϥΩ΍Ωϥίϭ̵΍ήΑΰϴϧ΍έΩ΍ΪόΗήϴϐΘϣϪ̯ ΏΎΨΘϧ΍έΎΒ̰ҨςϘϓ΍έΖϟΎΣήϫΎϫϩΩ΍ΩΩϭέϭϪΤϔλέΩΪόΑ ˺̋ ̋ ˻˹ ŹŚĪǀŝ Male – jobless ϢϴϨ̶̯ϣ ˺˹ ˻˹ ˼˹ ƪƛŚƃ Male – employee  ˻̋ ˻̋  Female –jobless Female – employee Type

Width

Decimal

Label

8

0

-

0

-

0



Gender

Numeric

˻

job

Numeric

3

frequency

Numeric

8

Missing column Align

1=male 2=female 1=jobless 2=emploee 

10

-

Measure

left

Nominal

10

left

Nominal

10

left

Scale

m

˺

Value

co

Name



ab

lin

e.

 ΖϬΟ ϢϴϫΩ ̶ϣ ϥίϭ Ύϫ ϩΩ΍Ω ϪΑ Data – Weightcases – frequency  ̵ϮϨϣ ί΍ ϩΩΎϔΘγ΍ ΎΑ β̢γ ϮϨϣί΍̶Ϙϓ΍ϮΗϝϭΪΟϞϴ̰θΗ Analyze –Descriptive Statistics- Crosstabs έΩ ήҨί ϝϭΪΟ Ϫ̯ ϢҨέ΍ά̴Α ̶Ϥγ΍ ΎҨ Nominal ήϴϐΘϣ ϭΩ ΪҨΎΑ ϩΪη ίΎΑ ϩήΠϨ̡ ϥϮΘγ ϭ ήτγ ΖϤδϗ έΩ ΩϮη̶ϣήϫΎχoutput

et

job * gender Crosstabulation Count

Total

.K

gender male jobless emploee Total

female 15

5

w w

job

male 20

20

10

30

25

25

50

w

ήϴϐΘϣϭΩϝϼϘΘγ΍ϪϴοήϓϪ̯ϭΩ̶ΧϡΎϧϪΑ̶ϧϮϣί΁ί΍ϥ΍ϮΘϴϣcrosstabsϩήΠϨ̡έΩstatisticsϪϤ̳ΩϥΩίΎΑ ΩϮϤϧϩΩΎϔΘγ΍ˬΪϨ̰ϴϣϥϮϣί΁΍έ̶Ϥγ΍ Chi-Square Tests

Pearson Chi-Square Continuity Correction(a) Likelihood Ratio

1

Asymp. Sig. (2-sided) .004

6.750

1

.009

8.630

1

.003

Value 8.333(b)

df

Fisher's Exact Test

Exact Sig. (2-sided)

Exact Sig. (1-sided)

.009

Linear-by-Linear Association

8.167

N of Valid Cases

50

1

.004

a Computed only for a 2x2 table b 0 cells (.0%) have expected count less than 5. The minimum expected count is 10.00.

16

.004


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 ϥΎθϧήϴϐΘϣϭΩϝϼϘΘγ΍̵΍ήΑ΍έ̵έ΍Ω̶Ϩόϣ΢τγϪ̯ϢϴΘδϫAsymp. Sig. (2-sided) ϥϮΘγϝΎΒϧΩΎϣ ΪηΎΑ̶ϣήϴϐΘϣϭΩ̶̴ΘδΑ΍ϭ̶ϨόϣϪΑϪ̯Ζγ΍Ϣ̯έΎϴδΑΎΠϨҨ΍έΩΪϫΩ̶ϣ ΩϮη̶ϣΏΎδΣϝϮϣήϓϦҨ΍ςγϮΗΪηΎΑ̶ϣ̶Ϥγ΍ήϴϐΘϣϭΩϝϼϘΘγ΍ϥϮϣί΁Ϫ̯ϭΩ̶ΧϥϮϣί΁  Oi ϩΪηϩΪϫΎθϣ  Ei έΎψΘϧ΍ΩέϮϣ  

(Oi  Ei )2   2 Ϊϧ΍ϪΘδΑ΍ϭ Ei

 

(Oi  Ei )2   2 Ϊϧ΍ϞϘΘδϣ Ei ŸƶƳŚƿŵŹřŵŵƺūƹİƏŚŞţŹřįŻŚŝśŚŞſřƕƺƳƹŢǀƀƴūƲǀŝŶǀƴĩƢǀƤŰţƩřƺŘſ

w

w w

.K

et

ab

lin

e.

co

m

̶ΟϭήΧέΩϥϮΘγϭήτγήϴϴϐΗ ϩΩή̯̮ϴϠ̯ϥ΁̵ϭέέΎΑϭΩΖγ΍̶ϓΎ̯ϢϴϨ̯νϮϋ΍έϥϮΘγϭήτγ̵ΎΟϢϴϫ΍ϮΨΑή̳΍̶ΟϭήΧϝϭΪΟέΩ Ωή̯νϮϋ΍έϥϮΘγϭήτγ̵ΎΟpivoting trayϩήΠϨ̡ϭformatting toolbarέ΍ΰΑ΍έ΍Ϯϧί΍ϩΩΎϔΘγ΍ΎΑϭ Ϊϴθ̯ΰϴϧsplit filesί΍ϩΩΎϔΘγ΍ϭ̶ϠΒϗ̵ΎϫεϭέΎΑϥ΍ϮΘϴϣ΍έϝϭΪΟϦϴϤϫ  LayerήϴϐΘϣϪҨϻ̮ҨΩΎΠҨ΍ ΩϮϤϧ ϩΩΎϔΘγ΍ ̶Ϙϓ΍ϮΗ ϝϭ΍ΪΟ έΩ ̶ϣϮγ ήϴϐΘϣ ί΍ ϥ΍ϮΘϴϣ Ϫϫ΍έ Ϫγ ϝϭΪΟ ̮Ҩ ΎΗ ϢϴϨ̯ ϪϓΎο΍ ΍έ ήϴϐΘϣ ϪҨϻ ̮Ҩ ̶ϨόҨ ΍έ ϡϮγ ήϴϐΘϣ Ε΍ήϴΛΎΗ ̶Θϗϭ Ϫ̯ ΪϫΩ ̶ϣ ϥΎθϧ ϭ ϢҨίΎδΑ ϥϮΘγϭήτγ̵ΎϫήϴϐΘϣϦϴΑϪτΑ΍έϪϧϮ̴̩ϢϴϨ̶̯ϣϝήΘϨ̯ ΪΑΎҨ̶ϣήϴϴϐΗ  ϭ empcat̶ϠϐηϖΑ΍Ϯγ ϥ΍ΰϴϣϭΪϴϨ̯ίΎΑ΍έ DemoϞҨΎϓ ϪΑ΍έ genderΖϴδϨΟήϴϐΘϣϭΪҨέϭ΁ΖγΪΑ΍έinccat  Ϊϣ΁έΩ ΪϴϨ̯Ωέ΍ϭϡϮγήϴϐΘϣϥ΍ϮϨϋ ΍έήτγΪλέΩϭϩΩί΍έcellϪϤ̳Ωcrosstabs ϩήΠϨ̡έΩή̳΍ ΪϫΩ̶ϣϥΎθϧΰϴϧΪλέΩΎΑ΍έΩ΍Ϊϋ΍ϢϴϨ̯ϝΎόϓ   Years with current employer * Income category in thousands * Gender Crosstabulation Count

Total

Income category in thousands Gender Female

Years with current employer

Less than 5 5 to 15 More than 15

Years with current employer

$50 - $74 130

148

553

$75+ 61

Under $25 1117

277

211

1189

46

98

165

564

873

1208

572

836

3179

Less than 5

409

516

107

67

1099

5 to 15

131

562

274

208

1175

71

102

167

607

947

611

1180

548

882

3221

More than 15 Total

$25 - $49 557

563

Total Male

Under $25 369

Ζγ΍έϻῺ̋˹˹˹̵ϻΎΑΪϣ΁έΩϭϝΎγ˺̋ί΍ήΘθϴΑ̶ϠϐηϪϘΑΎγϪΑρϮΑήϣΩ΍ΪόΗϦҨήΗϻΎΑΎϫΩήϣϭΎϬϧίϩϭή̳έΩϪΠϴΘϧ Ζγ΍έϻῺ̋˹˹˹̵ϻΎΑΪϣ΁έΩϭϝΎγ̋ί΍ήΘϤ̶̯ϠϐηϪϘΑΎγϪΑρϮΑήϣΩ΍ΪόΗϦҨήΗϦϴҨΎ̡ΎϫΩήϣϭΎϬϧίϩϭή̳έΩ

17


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

  ̶ϨόҨ Ζγ΍ .000  ˬ Asymp. Sig. (2-sided)  ϥϮΘγ ΩΪϋ Ϫ̯ ϢҨϮη ̶ϣ ϪΟϮΘϣ ϭΩ ̶Χ ϥϮϣί΁ ΎΑ Ϊϧ΍ϪΘδΑ΍ϭϭΪϨҨ΍ϭΩέ΍ΪϧΩϮΟϭΪϣ΁έΩϭ̶ϠϐηϖΑ΍ϮγήϴϐΘϣϭΩϦϴΑ̶ϟϼϘΘγ΍ Chi-Square Tests

Value 2506.010(a)

Pearson Chi-Square Likelihood Ratio

df

2527.590

Linear-by-Linear Association N of Valid Cases

2012.691

Asymp. Sig. (2-sided) 6

.000

6

.000

1

.000

6400

m

a 0 cells (.0%) have expected count less than 5. The minimum expected count is 318.50.

w

w w

.K

et

ab

lin

e.

co

̶ϟϭΪϫΩϥΎθϧ΍έΕϼϴμΤΗϥ΍ΰϴϣϭΖϴδϨΟϦϴΑρΎΒΗέ΍Ϫ̯ΪϴϨ̯Ϣγέ̶ϟϭΪΟDemoϞҨΎϓέΩϦҨήϤΗ έΩΎϬϧ΁Ϊϣ΁έΩςγϮΘϣϥ΍ΰϴϣΩ΍ΪόΗ̵ΎΟϪΑ ΪϨ̯ΝέΩϝϭΪΟ ̶Ϙϓ΍ϮΗ ϝϭΪΟ ί΍ ϥ΍ϮΗ ̶Ϥϧ ΎΠϨҨ΍ έΩ ̶ηέΎϔγ ϝϭΪΟ ΪҨΎΑ Ϫ̰ϠΑ ΩϮϤϧ ϩΩΎϔΘγ΍ Ωή̯ΩΎΠҨ΍ Analyze – Tables – Custom Table ήτγ έΩ ϭ ΖϴδϨΟ gender ϥϮΘγ έΩ  Ϊϣ΁έΩ ήϴϐΘϣ ϭ edu ΎҨ ΕϼϴμΤΗ ΢τγ έΩ ΪηΎΑ scale ΪҨΎΑ ˱ΎϤΘΣ Ϫ̯ income ϢҨέ΍ά̶̳ϣcountΖϤδϗ  ϥϮΘγ ϭ ήτγ ήϴϐΘϣ ϢϬϣ έΎϴδΑ ϪΘ̰ϧ ή̳΍ΪηΎΑϩΪη̵ΪϨΑϩΩέήϴϐΘϣΪҨΎΑ˱ΎϤΘΣ nominal  ϪΑ ΍ήϧ΁ Ζγ΍έ ̮ϴϠ̯ ΎΑ ΩϮΒϧ ϪΒγΎΤϣ ΖϬΟ ήϴϐΘϣ ϭ ϢϴϨ̯ ̶ϣ ϞҨΪΒΗ ΪηΎΑscale ΪҨΎΑ˱ΎϤΘΣ  Gender

Level of education

Did not complete high school High school degree

Female Household income in thousands

Male Household income in thousands

Mean 59.20

Mean 60.49

65.12

67.27

Some college

71.63

68.60

College degree

76.27

81.02

84.81

89.62

Post-undergraduate degree

έΩsummary statisticsϪϤ̳Ωί΍ϩΩΎϔΘγ΍ΎΑ΍έ̵ή̴ҨΩ̵έΎϣ΁̵ΎϫκΧΎηϦϴ̴ϧΎϴϣ̵ΎΟϪΑϥ΍ϮΗ̶ϣ ΩϮϤϧϪΒγΎΤϣϩήΠϨ̡ϥΎϤϫ 18


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

  carcat ΎҨ ϦϴηΎϣ ω΍Ϯϧ΍ ϭ gender ΖϴδϨΟ ϦϴΑ ρΎΒΗέ΍ Ϫ̯ ΪϴϨ̯ Ϣγέ ̶ϟϭΪΟ Demo ϞҨΎϓ έΩ  ϦҨήϤΗ ϝϭΪΟέΩΎϬϧ΁Ϊϣ΁έΩςγϮΘϣϥ΍ΰϴϣΩ΍ΪόΗ̵ΎΟϪΑ̶ϟϭΪϫΩϥΎθϧ΍έ Primary vehicle price category ΪϨ̯ΝέΩ Analyze – Tables – Custom Table scaleΪҨΎΑ˱ΎϤΘΣϪ̯ incomeΪϣ΁έΩήϴϐΘϣϭcarcatΎҨϦϴηΎϣω΍Ϯϧ΍ήτγέΩϭΖϴδϨΟgenderϥϮΘγέΩ ˬϦϴ̴ϧΎϴϣ ̵Ύϫ κΧΎη summary statistics ϪϤ̳Ω ϥΩί ΎΑ ϭ  ϢҨέ΍ά̳ ̶ϣ count ΖϤδϗ έΩ ΪηΎΑ ϢϴϨ̶̯ϣΏΎΨΘϧ΍Ϣϫ΍έΪϣϭϢϤϴϨϴϣˬϢϤҨΰ̯Ύϣ  Gender

Mean 22.17

Maximum 31.00

Minimum 9.00

Mode 25.00

42.45

61.00

29.00

34.00

132.60

1070.00

59.00

65.00

Standard Luxury

Mean 21.62

Maximum 31.00

Minimum 9.00

Mode 23.00

61.00

29.00

31.00

1116.00

58.00

62.00

m

Economy

Male Household income in thousands

42.67

co

mary vehicle ce category

Female Household income in thousands

136.65

ab

NominalϒϠΘΨϣ̵ΎϬϫϭή̳ϦϴΑέΩscaleήϴϐΘϣ̮ҨϪδҨΎϘϣ˺ ϢϫΎΑήΘθϴΑΎҨscaleήϴϐΘϣϭΩϪҨΎϘϣ˻ ΎϫcaseϡΎϤΗϦϴΑέΩήϴϐΘϣ̮ҨϪδҨΎϘϣ˼      ΪϴϨ̯ΖγέΩ΍έΎϫέ΍ΩϮϤϧϦҨ΍ϦҨήϤΗ   ϝϭ΍ωϮϧέ΍ΩϮϤϧΖΧΎγ Graph Chart builder ˺ Ύϫx έϮΤϣέΩGenderήϴϐΘϣ˻ Ύϫy έϮΤϣέΩincomeήϴϐΘϣ˼ Clustering variable on ϪϨҨΰ̳Groups/point idϪϤ̳ΩέΩ̊ ΪϴϨ̯ΝέΎΧϥΩϮΑϝΎόϓί΍΍έX ΎҨ Graph – Legacy dialogs – bar – variable ….. mean

ƎſƺŤƯ ƎſƺŤƯ

ŵźƯ

ŶƯōŹŵ

ƶƴƿżƷ

w

w w

ƱŻ

.K

et

ŹLJŵ

80.00

Mean Household income in thousands

ŶƯōŹŵƎſƺŤƯ

lin

e.

ϦҨήΗϻΎΑΎΑϥΎҨΎϗ΁̶ϟϭΪϧήΧ̶ϣ΍έβ̯ϮϟϦϴηΎϣϦҨήΘϬΑέϻΩέ΍ΰϫ˺˹̀˹Ϊϣ΁έΩϦҨήΗϻΎΑΎΑΎϬϤϧΎΧϪΠϴΘϧ ΪϧήΧ̶ϣβ̯ϮϟϦϴηΎϣέϻΩέ΍ΰϫ˺˺˺̌Ϊϣ΁έΩ Ζγ΍ϥΎҨΎϗ΁ί΍ήΘϤ̯ΪϧήΧ̶ϣβ̯ϮϟϦϴηΎϣϪ̶̯ҨΎϫϢϧΎΧΪϣ΁έΩϦϴ̴ϧΎϴϣ  ŹřŵƺưƳƕřƺƳř

70.16

68.78 60.00

40.00

20.00

Household income in thousands Female

/ category Axis ……. gender

  

0.00 Male

Gender

19


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř  ϡϭΩωϮϧϝϭΪΟΖΧΎγ

Data – split files - gender Analyze – Descriptive statistics – Descriptive -

Household income in thousands , Price of primary vehicle



Gender

Female

Household income in thousands Mean 68.78

Price of primary vehicle Mean 29.95

70.16

30.31

Male

  ϡϭΩέ΍ΩϮϤϧΖΧΎγ Graph – Legacy dialogs – bar – simple – summaries of seprate variables - variable ….. mean

co

m

Household income in thousands / Price of primary vehicle- ok

e.

69.47

lin

60.00

et

ab

 

.K w w

20.00

30.13

w

Mean

40.00

0.00 Household income in thousands

Price of primary vehicle

20




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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

íëæçæë±¹ZÆqĈ¸m  ϞΒϗϪδϠΟΕϻ΍ϮΌγϪΑΦγΎ̡ ̮ҨέΩ΍έΪϨΘδϫ̵΍ϪϨҨΰ̳ΪϨ̩ϭΪϧέ΍ΩϥΎδ̰ҨΦγΎ̡Ϫ̶̯ҨΎϫήϴϐΘϣ DemoϞҨΎϓέΩϥ΍ϮΘϴϣϪϧϮ̴̩˺ ˮΩή̵̯έϭ΁ϊϤΟϝϭΪΟ Analyze – Tables – Custom Tables Ζγ΍έ̮ϴϠ̯ΎΑ΍ΪΘΑ΍΍έΪϧέ΍ΩήϴΧΎҨϪϠΑΏ΍ϮΟςϘϓϪ̯ϥΎδ̰ҨΦγΎ̡ΎΑ̵ΎϫήϴϐΘϣϪϴϠ̯β̢γϝϭ΍εϭέ ΖϤγ ί΍ β̢γ stacking ΖϟΎΣ ϪΑ ϩΩ΍Ωέ΍ήϗ ϝϭΪΟ ήτγ ΖϤδϗ έΩϭ ϩΩή̯ ϞҨΪΒΗ category ωϮϧ ϪΑ ϢϴϨ̶̯ϣΏΎΨΘϧ΍΍έrow labels in columns ϪϨҨΰ̳category positionΖϤδϗϩήΠϨ̡ϦϴҨΎ̡Ζγ΍έ 

3709

2691

Voice mail

3645

2755

Paging service

4819

1581

Internet

4509

1636

co

Multiple lines

Wireless service

m

Yes Count 2547

e.

No Count 3853

ϡϭΩεϭέ

lin

Analyze – Tables –Tables of frequency

nd  z.

nd 2  z 2 2

xx d

.K

 

xx x

ˮϢϴϨ̯ϪΒγΎΤϣϪϧϮ̴̩΍έϪϧϮϤϧϩί΍Ϊϧ΍˻ ̶Ϥ̯ήϴϐΘϣ̵΍ήΑ±ϒϟ΍ ΩϭΪΤϣΎϧϪόϣΎΟέΩ

et

z

ab

ΪϴϧΰΑ΍έokϭΪҨήΒΑϩήΠϨ̡έΩ΍έΎϫήϴϐΘϣϪϤϫ

z 2 2 n 2 d

w

w w

   x   n   d nd  z      Ζγ΍κΨθϣϪόϣΎΟήλΎϨϋΩ΍ΪόΗNϪ̯ ΩϭΪΤϣϪόϣΎΟέΩ n  2 2  Nz  n  ( N  1)d 2  z 2 2  z  1.64 ˬ   0.05 ̵΍ήΑ ̶ϔϴ̯ήϴϐΘϣΏ ϥ΁ϦΘη΍ΪϧϝΎϤΘΣ΍ q=1-pϭήψϧΩέϮϣΖϔλϦΘη΍ΩϝΎϤΘΣ΍ pϪ̯ϢϴϫΩ̶ϣέ΍ήϗ΍έ pqˬ ̵ΎΟϪΑ Ζγ΍Ζϔλ   Nz 2 pq n  ( N  1) d 2  z 2 pq  21


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř ƉźƟƱƺƯŻō

 κΧΎη ϥ΍ϮϨϋ ϪΑ ϪϧϮϤϧ κΧΎη έ΍ΪϘϣ ΏΎΨΘϧ΍ ϭ ϪϧϮϤϧ ΎΑ ΖϴόϤΟ κΧΎη ϪδϳΎϘϣ ϱ΍ήΑ νήϓ ϥϮϣί΁ Ωϭέϲϣ  έΎϛϪΑΖϴόϤΟ ϢϳϮηϞϤΤΘϣϢϴϧ΍ϮΗϲϣ  ϪϛΖγ΍ϱΎτΧϥ΍ΰϴϣϭϪϧϮϤϧϭΖϴόϤΟϩί΍Ϊϧ΍ϪΑϪΟϮΗΎΑΏΎΨΘϧ΍Ϧϳ΍ΖϗΩ ϊϳίϮΗϪΑΖϴόϤΟϪ̩ήϫΩϮΑΪϫ΍ϮΧήΘθϴΑϥϮϣί΁ΖϗΩΪηΎΑήΘθϴΑΖϴόϤΟϪΑΖΒδϧϪϧϮϤϧϩί΍Ϊϧ΍Ϫ̩ήϫ ΩϮΑΪϨϫ΍ϮΧήΗϥ΍ϮΗή̡  ΎϫϥϮϣί΁  ΪηΎΑήΘϜϳΩΰϧϝΎϣήϧ  ŚƷƲǀĮƳŚǀƯƶƀƿŚƤƯįřźŝƱƺƯŻōśŚŴŤƳř Á€‹

Á{YŠÌ]

ab

¶¬fˆ»

 ¶¬fˆ»

Äfˆ]YÁ

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  ƲǀĮƳŚǀƯƶŝƍƺŝźƯįŚƷƱƺƯŻō

w w



Äfˆ]YÁ

Ã{Z‡†¿ZËYÁ‚Ì·Z¿M  oneway Anova  †Ì·YÁµZ°‡Á€¯

m

 {Y| e

co

ÃÁ€³®Ë

 Ŀ¼¿®Ët ½Â»M one sample T test

w

 ϪόϣΎΟ ϪΑ ΍έ ϪϧϮϤϧ ΖϴλϮμΧ Ϣϴϫ΍ϮΧ ̶ϣ ϭ ϢҨ΍ ϩΩή̯ ΏΎΨΘϧ΍ ϪϧϮϤϧ ̮Ҩ ϭ ϢҨέ΍Ϊϧ ΍έ ϪόϣΎΟ Ϫ̯ ̶ϧΎϣί ϦϴΑ ϑϼΘΧ΍ ϥ΍ϮΘϴϣ ΕΎϴοήϓ  ϥϮϣί΁ ΖϴϠΑΎϗ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϭ ϢϴϫΩ ̶ϣ ϡΎΠϧ΍ ϥϮϣί΁ ΍άϟ ϢϴϫΪΑ ΖΒδϧ ΐ̰ΗήϣϢϴϨ̯Ωέ΍έϥ΁ΎϣϭΪηΎΑΖγέΩ̵έΎϣ΁νήϓή̳΍ˬνήϓϥϮϣί΁ϡΎΠϧ΍έΩ ΪϴΠϨγ΍έΎϫϦϴ̴ϧΎϴϣ ϢҨϮη̶ϣϡϭΩωϮϧ̵ΎτΧΐ̰ΗήϣΪηΎΑΖγέΩΎϧϢϴΘϓήҨά̡Ϫ̯΍έ̵έΎϣ΁νήϓή̳΍ϭϝϭ΍ωϮϧ̵ΎτΧ ̵ΎτΧΖγ΍ήΘϤ̯ϡϭΩωϮϧ̵ΎτΧϭΩϮΑΪϨϫ΍ϮΧήΗϥ΍ϮΗή̡ΎϫϥϮϣί΁ΩϭήΑζϴ̡ϝΎϣήϧϑήσϪΑϊҨίϮΗϪ̩ήϫ ΪηΎΑΖγέΩϪ̰ϨҨ΍ρήηϪΑH1ΩέϝΎϤΘΣ΍̶ϨόҨϡϭΩωϮϧ   2  ϡϮϠόϣβϧΎҨέ΍ϭϭ  ϝϮϬΠϣϦϴ̴ϧΎϴϣΎΑϝΎϣήϧϊҨίϮΗί΍̶ҨΎΗ nϪϧϮϤϧ̮Ҩ X 1 , X 2 ,..., X n ϢϴϨ̯νήϓ Ζγ΍ϪόϣΎΟϦϴ̴ϧΎϴϣΩέϮϣέΩήҨίνήϓϥϮϣί΁ϑΪϫΪϨηΎΑ Ωέ΍Ωέ΍ήϗΖγ΍Ϫϓήσ̮Ҩΐ̯ήϣνήϓ̮ҨϪ̯ H1 νήϓϞΑΎϘϣέΩϩΩΎγνήϓ̮Ҩ H 0 νήϓ  H0:  0 H 1:   0

 22


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 έϮσϪΑϭΪϨ̶̯ϤϧΕϭΎϔΗή̴ҨΩϪϧϮϤϧϪΑϪϧϮϤϧ̮Ҩί΍ϥϮϣί΁ϪΠϴΘϧϭΖγ΍ήΘθϴΑϥ΍ϮΗϝΎϣήϧϊҨίϮΗέΩ ΐϴΗήΗ ϪΑ Ϫ̯ ΩϮη ̶ϣ ϩΩΎϔΘγ΍ ̵ήΘϣ΍έΎ̡ ̵Ύϫ ϥϮϣί΁ ί΍ ϥΩϮΑ ϝΎϣήϧ νήϓ ΎΑ ̱έΰΑ ϊϣ΍ϮΟ έΩ ̶Ϡ̯ Ϊϧέ΍ΩϪϴ̰ΗΕΎϋϼσ΍ϦΘϓή̳έ΍ήϗ ̵ΎϬϧϮϣί΁ ΪϨϨ̯ ̶ϣ ϩΩΎϔΘγ΍ ̵ήΘϣ΍έΎ̡Ύϧ ̵Ύϫ ϥϮϣί΁ ί΍  ̮̩Ϯ̯ ϭ ϝΎϣήϧ ήϴϏ ϊҨίϮΗ ΎΑ ̵Ύϫ ΖϴόϤΟ έΩ ΐΟϮϣϥ΍ΰϴϣϥΎϤϫϪΑϪ̯ ή̴ҨΩΩή̰ҨϭέΪϨϨ̶̯Ϥϧ̶λΎΧνήϓϥ΁βϧΎҨέ΍ϭϭϪόϣΎΟ̵ΎϬόҨίϮΗϩέΎΑέΩ̵ήΘϣ΍έΎ̡Ύϧ Ζγ΍ΪҨΪΟ̵ΎϫϩΩ΍ΩϪϋϮϤΠϣΎΑtϥϮϣί΁ϥΩήΑέΎ̯ϪΑϭΕή̡ήҨΩΎϘϣϥΩή̯ΝέΎΧ ΩϮη̶Ϥϧϥ΍ϮΗΖϓ΍

ΪηΎΒϧ΢ϴΤλϪ̶̯σήηϪΑΖγ΍H0ϥΩή̯ΩέϝΎϤΘΣ΍̵έΎϣ΁ϥϮϣί΁̮ҨpowerΎҨϥ΍ϮΗ

 p  value    ή̳΍ Ζγ΍ ϝΎϤΘΣ΍ έ΍ΪϘϣ ΎҨ p  value  ί΍ ϩΩΎϔΘγ΍ ̵έΎϣ΁ νήϓ ϥϮϣί΁ ̵ΎϬηϭέ ί΍ ̶̰Ҩ ΩϮη̶ϣΩέ H 0 νήϓέ΍ΪϘϣ  Means ƵŶƃįŶƴŝƵŵŹźǀƜŤƯĨƿŹŵİĭĦƿƹŶƴģŚƿƹŵįřźŝįŵŶƗźǀƜŤƯĨƿƲǀĮƳŚǀƯƶƀƿŚƤƯįźŤƯřŹŚěƱƺƯŻō

Mean Household income in thousands

w

w w

.K

et

ab

lin

e.

co

m

ΖϴόϤΟ ί΍ ϩϭή̳ ϭΩ ϦϴΑ ϲΒγΎϨϣ ϪδϳΎϘϣ ϥ΍ϮΗϲϣ   ΖϴόϤΟ ΰϛήϤΗ κΧΎη ϥ΍ϮϨϋ ϪΑ Ϧϴ̴ϧΎϴϣ ϪδϳΎϘϣ ΎΑ Ωέϭ΁ΖγΪΑ ϪϧϮϤϧ

ΖϴόϤΟ ϩΪϨϳΎϤϧ ϭ κΧΎη ϥ΍ϮϨϋ ϪΑ Ϊϣ ϭ ϪϧΎϴϣ ϞΜϣ ΰϛήϤΗ ϱΎϫέΎϴόϣ ή̴ϳΩ Ύϳ Ϧϴ̴ϧΎϴϣ Ϫϛ ϲϳΎΠϧ΁ ί΍ ϪδϳΎϘϣ΍έΎϬϧ΁ΰϛήϤΗϱΎϫέΎϴόϣΖγ΍ήΘϬΑΖϴόϤΟϭΩΎϳϲ̳̬ϳϭϭΩϪδϳΎϘϣϱ΍ήΑˬΪϨΘδϫϩΪηϪΘΧΎϨη Ωήϛ Analyze – Compare Means – Means Dependent listΖϤδϗέΩ Ωέ΍ϭ ΍έ ϩΪη ϱήϴ̳ ϩί΍Ϊϧ΍ ϲ̳̬ϳϭ Ύϳ ήϴϐΘϣ ϱΩΪϋ ήϳΩΎϘϣ ϱ΍έ΍Ω ΪϳΎΑ ήϴϐΘϣ Ϧϳ΍ ΪϴϨϛ ΪηΎΑ  Independent list ΖϤδϗέΩϭ ϲϓήόϣ΍έ ϱΪϨΑϩϭ  ή̳ ΎϳϱΪϨΑϪΘγΩήϴϐΘϣ αΎγ΍ήΑ ϲ̳̬ϳϭ Ϧϴ̴ϧΎϴϣ έΎϛ Ϧϳ΍ ΎΑ ΪϴϨϛ ΖγΪΑ ϩϭή̳ ήϫ ϱ΍ήΑ ϱΪϨΑϪΘγΩ ήϴϐΘϣ ΪϧϮηϲϣϪδϳΎϘϣϭϩΪϣ΁  ϞϫΎΗΖϴόοϭαΎγ΍ήΑΪϣ΁έΩϪδҨΎϘϣ˱ϼΜϣ xx  ΪηΎΑ̶ϣ T  Ϧϴ̴ϧΎϴϣϩέΎϣ΁ s n ̵ΎϬϴ̳̬ϳϭÁ  Statistics ϩέΎϣ΁΍έ ϩήϴϏϭέΎϴόϣϑ΍ήΤϧ΍ˬϦϴ̴ϧΎϴϣ  ϪϧϮϤϧ̮ϳ̵ΎϬϴ̳̬ϳϭϪΑρϮΑήϣήϳΩΎϘϣ ΪϨҨϮ̶̳ϣ Parameters ήΘϣ΍έΎ̡΍έ̶Ϡλ΍ϪόϣΎΟέΩΎϬϧ΁ϝΩΎόϣ Ϣϴθ̶̯ϣϞϫΎΗΖϴόοϭαΎγ΍ήΑΰϴϧ΍έincomeήϴϐΘϣϦϴ̴ϧΎϴϣ έ΍ΩϮϤϧ   69.272 69.68  Report Household income in thousands Marital status Mean

N

Std. Deviation

Unmarried

69.2723

3224

78.32925

Married

69.6804

3176

79.12361

Total

69.4748

6400

78.71856

60 00

40 00

20 00

 0 00 Unmarried

23

Married

Marital status


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 ΎϫήϴϐΘϣ̵ΪϨΑϪҨϻ Ωή̵̯ΪϨΑϪҨϻϥ΍ϮΘϴϣΖϴδϨΟϩ΍ήϤϫϪΑ΍έΖϴόϗϮϣMeanϞϤόϟ΍έϮΘγΩί΍ϩΩΎϔΘγ΍ΎΑ  Analyze – Compare Means – Means- dependent list ………income



w

w w

.K

et

ab

lin

e.

co

m

Independent list………….Martial Next Independent list………….Genderl



Report

Household income in thousands Marital status Unmarried

Married

Total

Gender Female

Mean 72.0633

N 1533

Std. Deviation 83.02021

Male

66.7422

1691

73.75302

Total

69.2723

3224

78.32925

Female

65.7179

1645

68.15853

Male

73.9392

1530

89.27551

Total

69.6797

3175

79.13606

Female

68.7788

3178

75.74700

Male

70.1608

3221

81.56216

Total

69.4744

6399

78.72471

24


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

ab

lin

e.

co

m

 ̶ϣ box̮ҨήϴϐΘϣήϫ̵΍ήΑέ΍ΩϮϤϧωϮϧϦҨ΍έΩΖγ΍ Boxplot έ΍ΩϮϤϧϦҨήΘϬΑΎϫϦϴ̴ϧΎϴϣϪδҨΎϘϣ̵΍ήΑ Ζγ΍ήΘθϴΑΎϫϩΩ΍Ω̶̳ΪϨ̯΍ή̡ΪηΎΑήΘθϴΑboxωΎϔΗέ΍Ϫ̩ήϫϪ̯Ϊθ̯ 1  Max ήΗϻΎΑή̳΍ϭΖγ΍ϥέΎϘΘϣΪηΎΑboxςγϭϦϴ̴ϧΎϴϣή̳΍ Ζγ΍̶̴ϟϮ̩ϪϧΎθϧΪηΎΑςγϭί΍ήΗϦϴҨΎ̡ΎҨ Q3 ϥ΁˺ϩέΎϤηϪ̯Ζγ΍Εή̡ϩΩ΍Ω̮ҨϩΪϨϫΩϥΎθϧ Ζγ΍˺ϩέΎϤηcaseϪΑρϮΑήϣϪ̯ΪϫΪϴϣϥΎθϧ ΪϨҨϮ̶̳ϣoutliarΕή̡ϩΩ΍ΩϪΑ ΪηΎΑϪΘη΍Ω΍έρήηϦҨ΍Ϫ̵̯΍ϩΩ΍Ω̶Ϡ̯έϮσϪΑ Mean=median= ΩϮη̶ϣΏΎδΣΕή̡ Q2  Q  Q3  Q1 Q1  1 Q  Q3  Outliar  Q1  1 Q  2 2 Min  ΩϮΟϭΎΑ Ϊϧέ΍ΩϪϠλΎϓ QήΑ΍ήΑϪγί΍ήΘθϴΑϪ̯ΪϨҨϮ̶̳ϣ Extreme variableΰϴϧΕή̶̡ϠϴΧ̵ΎϫϩΩ΍ΩϪΑ ϢҨέϭΎϴϧΖγΪΑΖγέΩ΍έϥϮϣί΁ϪΠϴΘϧϭΩϮηϞҨΎϤΘϣΎϬϧ΁ΖϤγϪΑϦϴ̴ϧΎϴϣΖγ΍Ϧ̰ϤϣΕή̵̡ΎϫϩΩ΍Ω ϢϴϨ̯ϑάΣ΍έΕή̵̡ΎϫϩΩ΍ΩΖγ΍ήΘϬΑβ̡ ̶ϟϭ΍̶̳ΪϨ̯΍ή̶̡ϨόҨΪηΎΑϪΘη΍ΩΩϮΟϭ̶ϧΎηϮ̢Ϥϫή̳΍ήϴϐΘϣϭΩ Ϧϴ̴ϧΎϴϣϦϴΑ̵Ύϫ boxplotϪδҨΎϘϣέΩ ϡΎΠϧ΍΍έϥϮϣί΁ΩϮη̶ϣϭΪηΎΑ̶ϤϧΎϫϦϴ̴ϧΎϴϣϦϴΑ̵ΩΎҨίϑϼΘΧ΍΍άϟΖγ΍̶ϣϭΩ̶̳ΪϨ̯΍ή̡ϞϣΎη Ω΍Ω  Graph –legacy Dialogs - Boxplot – simple – variable…….income

et

Category Axis ………..Martial

.K

1200.00

250

150 800.00

2,535 2,001

w

Household income in thousands

4,929

400.00

200.00

1,812

̵ϮϨϣί΍Εή̵̡ΎϫϩΩ΍ΩϑάΣ̵΍ήΑ Data-select cases-if income<200 ϢϴϨ̶̯ϣΏΎΨΘϧ΍΍έ

3,254 5,530 4,277 4,057 1,706 4,709

Εή̡ ̶ϠϴΧ Ϫ̯ ΍έ ˻˹˹ ί΍ ήΘθϴΑ ΎΗ έ΍ΩϮϤϧ ϭ ΕΎΒγΎΤϣ έΩ ΪϨΘδϫ ΩέϭΎϴϧ

1,263

2,844

600.00

77 2,980

w w

427 1000.00

5,363 5,840 6,017 ,845 4,386 1,557 4,965 1,953 1,425 2,432 4,925 2,308 241 4,958 1,143 5,926 6,228 283 217 5,402 4,726 3,636 2,182 1,647 4,806 3,834

 ρΎϘϧ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ έ΍ΩϮϤϧ ϦҨ΍ Ωέ΍ΩΩϮΟϭ̵έΎϴδΑΕή̡

6,046 97 1,147 506 5,606 2,415 3,008 278 ,049 3,042 4,005 3,307 3,758 4,904 1,749 5,649 1,286 1,312 4,449 2,839 2,636 4,604 2,687 5,043

0.00 Unmarried

Married

  

Marital status

25


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

  One_ sample T Test  ƵŶƃƶŤųŚƴƃŹřŶƤƯĨƿƪŝŚƤƯŹŵƶƳƺưƳƲǀĮƳŚǀƯƱƺƯŻō 



co

m

t ϥϮϣί΁ ϭ ϪδϳΎϘϣ ϥΎϜϣ΍ έϮΘγΩ Ϧϳ΍ ϱ΍ήΟ΍ ΎΑ ΩϭέϲϣέΎϛϪΑϥϮϣί΁ζΠϨγϱ΍ήΑ  Ζγ΍ΎϫϩΩ΍ΩϥΩϮΑϝΎϣήϧήΑνήϓϥϮϣί΁Ϧϳ΍έΩ ϪΘη΍Ω ϱΩΪϋ ήϳΩΎϘϣ ΪϳΎΑ ϩΪη ϲϓήόϣ ήϴϐΘϣ ΪηΎΑ ΪϨηΎΑ ϪΘη΍Ω Ϣϫ ϲ̴ϟϮ̩ ϱέ΍ΪϘϣ ΎϫϩΩ΍Ω  ή̳΍ ΩήϛΪϫ΍ϮΧΖϣϭΎϘϣϩΎΒΘη΍ήΑ΍ήΑέΩϥϮϣί΁Ϧϳ΍ Ζγ΍ ϩΪη ϪΘϓή̳ ήψϧ έΩ ήϳί ϞϜη ϪΑ ϥϮϣί΁ ϲγΪΣέ΍ΪϘϣ 80ϭΖγ΍ϪϧϮϤϧϦϴ̴ϧΎϴϣέ΍ΪϘϣ

ϢϴϧίϲϣϦϴ̴ϧΎϴϣϱ΍ήΑΖϴόϤΟί΍Ϫϛ  Ζγ΍

Analyze – compare Means – One_ sample T Test / Test variable ……Income Test value …..……80

e.



w

w w

.K

et

ab

lin

 ϼΜϣΎΠϨϳ΍έΩ  test value έ΍ΪϘϣΎΑΪϳΎΑϥΎθϨϴ̴ϧΎϴϣϪϛϲϳΎϫήϴϐΘϣΎϳήϴϐΘϣ Test variablesΖϤδϗέΩ Ωήϴ̳ϲϣέ΍ήϗΩϮηϪδϳΎϘϣ   80 ΩϮΟϭ  95%ϥΎϨϴϤσ΍ϪϠλΎϓϚϳϼΜϣ ϥΎϨϴϤσ΍ϪϠλΎϓΪλέΩϒϳήόΗϥΎϜϣ΍ΰϴϧ optionsϪϤϛΩΏΎΨΘϧ΍ΎΑ Ωέ΍Ω ϞϣΎηϪϛΖγ΍ϒϠΘΨϣϱΎϫϪϧϮϤϧί΍ϲϠλ΍ϮϓΪλέΩϩΪϨϫΩϥΎθϧϥΎϨϴϤσ΍ϪϠλΎϓϢϴηΎΑϪΘη΍ΩΖϗΩ ˬΖϴόϤΟί΍ή̴ϳΩϪϧϮϤϧ ˺˹˹έΩˬΪϫΩϲϣϥΎθϧ  ̂̋ϥΎϨϴϤσ΍ϪϠλΎϓϚϳϝΎΜϣϱ΍ήΑΪϨηΎΑϲϣϦϴ̴ϧΎϴϣ ΩϮΑΪϨϫ΍ϮΧϪϠλΎϓϦϳ΍έΩΎϫϪϧϮϤϧϦϴ̴ϧΎϴϣˬϪϧϮϤϧ̂̋  Ζδϴϧ ϩΪη ϪΘϓή̳ ϪϧϮϤϧ ί΍ ̶ηΎϧ ̶ϨόΑ Ζγ΍ έ΍Ω ̶Ϩόϣ ̵ϭΎδΗ ΎҨ ϑϼΘΧ΍ ϦϴҨϮ̳ ̶ϣ ̶Θϗϭ  ϪΘ̰ϧ ΎҨϑϼΘΧ΍̶ϨόҨΪηΎΒϧέ΍Ω̶Ϩόϣή̳΍  Ҩϑ ΘΧ΍ ό  Ύ ϧέ΍Ω  ΍ΩϮΑΪϫ΍ϮΧϦϴϤϫϪΠϴΘϧΩϮηϥϮϣί΁Ϣϫ̵ή̴ҨΩϪϧϮϤϧΎΑ  ϫ Ϯ ϦϴϤ  Π ϧΩ η ΁ ϫ ή̴ Ω ϧ ϤϧΎΑ ή̳΍Ϫ̰ϠΑ ή̳ Ϫ̰ Ζγ΍ϥϮϣί΁ˬκϴΨθΗεϭέΖγ΍ΖϴόϤΟί΍̶ηΎϧ̵ϭΎδΗ                 26


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

  ˮϪϧΎҨΖγ΍έ΍Ω̶ϨόϣΪϣ΁έΩήϴϐΘϣΩέϮϣέΏ˹έ΍ΪϘϣΎΑϩΪηϩΩίαΪΣϦϴ̴ϧΎϴϣϢϴϨϴΒΑϢϴϫ΍ϮΧ̶ϣ

H 0:   80 H1:   80

H0: 800

SPSS 

H1: 800

co

m

T-TEST /TESTVAL = 80 /MISSING = ANALYSIS /VARIABLES = income /CRITERIA = CI(.95) .

One-Sample Statistics

69.4748

78.71856

Std. Error Mean .98398

lin

6400

Std. Deviation

ab

Household income in thousands

Mean

e.

N

et

One-Sample Test

w w

df

-10.696

6399

Mean Sig. (2-tailed) Difference .000

w

Household income in thousands

.K

Test Value = 80

t

            95% Confidence  Interval of the  Difference  Lower Upper   -12.4541 -8.5962     ϪΠϴΘϧ Ζγ΍6400-1=6399̶ϨόҨn-1̵Ω΍ί΁ϪΟέΩ

-10.52516

xx 69.47.48  80 10.52516 = = = -10.696 < 0 s 0.98398 0.98398 n Sig.(2-tailed) = p  value  0.000 <0.05 =    H 0 νήϓΩέ Ωέ΍ΪϧΩϮΟϭ H 0 ΪϴҨΎΗ̵΍ήΑ̶ϠϴϟΩβ̡Ζγ΍ϩΪηήΘ̰̩Ϯ̯  ί΍ p  value έ΍ΪϘϣϥϮ̩˺ T

ΩϮΧϦҨ΍Ϫ̯ΪηΎΑ̶ϤϧήϔλϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγέΩϩΪηϩΩ΍ΩϥΎθϧΩΪϋϭΩ˻ ϭϩΪη̶ϔϨϣ   80  ̶ϨόҨΖγ΍̶ϔϨϣϩίΎΑϦҨ΍ϥϮ̩ Ζγ΍έΎ̰η΁ΕϭΎϔΗϩΪϨϫΩϥΎθϧϭ Ζγ΍ H 0 νήϓϩΪϨϨ̯ΩέϞϣΎϋ Ζγ΍ήΘϤ̯́˹ί΍Ϧϴ̴ϧΎϴϣ̶ϨόҨ Ζγ΍έ΍Ω̶ϨόϣϑϼΘΧ΍˼ Ζγ΍έ΍Ω̶ϨόϣˬΪηΎΑ˻ί΍ήΘ̳έΰΑΎҨήΑ΍ήΑtϖϠτϣέΪϗ.̊

27


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

H 0:   70 H1:   70

  ϢϴϨ̶̯ϣέ΍ή̰Ηΰϴϧ̀˹ΩΪϋ̵΍ήΑ΍έϥϮϣί΁ϦҨ΍ 

H0: 700

SPSS 

ƱřźƿřżƿřƺŤǀŤƀƳř

H1: 700

  T-TEST /TESTVAL = 70 /MISSING = ANALYSIS /VARIABLES = income /CRITERIA = CI(.95) .



6400

Std. Error Mean

Std. Deviation

69.4748

78.71856

.98398

co

Household income in thousands

Mean

lin

One-Sample Test

e.

N

m

One-Sample Statistics

-.534

Sig. (2-tailed)

Mean Difference

.594

-.52516

6399

w w

.K

Household income in thousands

df

et

t

ab

Test Value = 70 95% Confidence Interval of the Difference Lower Upper -2.4541

    

     1.4038    ϪΠϴΘϧ

Sig.(2-tailed) = p  value  0.594 !0.05 =

w

 Ωέ΍ΪϧΩϮΟϭ H 0 Ωέ̵΍ήΑ̶ϠϴϟΩβ̡Ζγ΍ϩΪηήΘ̳έΰΑ  ί΍ p  value έ΍ΪϘϣϥϮ̩˺

ΩϮΧϦҨ΍Ϫ̯ΪηΎΑ̶ϣήϔλϞϣΎη  95% Confidence Interval of the Difference ϥϮΘγέΩϩΪηϩΩ΍ΩϥΎθϧΩΪϋϭΩ˻ Ζγ΍ϪϧϮϤϧςγϮΗ H 0 νήϓϩΪϨϨ̯ΪϴҨΎΗϞϣΎϋ Ζδϴϧέ΍Ω̶ϨόϣϑϼΘΧ΍˼ Ζγ΍έ΍Ω̶ϨόϣˬΪηΎΑ˻ί΍ήΘ̳έΰΑΎҨήΑ΍ήΑtϖϠτϣέΪϗ.̊  

 ήϴϴϐΗ΍έ  ΎҨϝϭ΍ωϮϧ̵ΎτΧέ΍ΪϘϣϥ΍ϮΘϴϣ OptionΖϤδϗέΩ One_ sample T TestϩήΠϨ̡έΩϪΘ̰ϧ ΪϧϮη̶ϣΩέΎϫϪϴοήϓήΘθϴΑϢҨ΍ά̴Α˺΍έ  ̶ϨόҨϢҨήΒΑϻΎΑ̂̂ΎΗ΍έϥΎϨϴϤσ΍ΐҨήοή̳΍Ω΍Ω       28


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 ϝΎΜϣ ΪҨέϭ΁ΖγΪΑ΍έweightήϴϐΘϣΎҨϥίϭϦϴ̴ϧΎϴϣϭΪϴϨ̯ίΎΑ΍έspss sample data.savϞҨΎϓ  Analyze- report – frequency Ζγ΍̀̋ϥίϭϦϴ̴ϧΎϴϣϪ̰ϨҨ΍νήϓΎΑΪϴϫΩϡΎΠϧ΍̶ϧϮϣί΁  H0: 750 H 0:   75  SPSS  1:  75  0 H   H1:   75  

m

T-TEST /TESTVAL = 75 /MISSING = ANALYSIS /VARIABLES = weight wieght_after /CRITERIA = CI(.95) .

co lin

11 0 68.3182

et

ab

Mean

Valid Missing

e.

Statistics weight N

 

w

weight

t -1.220

w w

.K

One-Sample Test

df

10

Test Value = 75

Sig. (2-tailed) .250

Mean Difference -6.68182

 95% Confidence Interval of the Difference Lower Upper -18.8805 5.5169

     

ϪΠϴΘϧ Sig.(2-tailed) = p  value  0.250 !0.05 =

 Ωέ΍ΪϧΩϮΟϭ H 0 Ωέ̵΍ήΑ̶ϠϴϟΩβ̡Ζγ΍ϩΪηήΘ̳έΰΑ  ί΍ p  value έ΍ΪϘϣϥϮ̩˺

ΩϮΧϦҨ΍Ϫ̯ΪηΎΑ̶ϣήϔλϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγέΩϩΪηϩΩ΍ΩϥΎθϧΩΪϋϭΩ˻ Ζγ΍ϪϧϮϤϧςγϮΗ H 0 νήϓϩΪϨϨ̯ΪϴҨΎΗϞϣΎϋ Ζδϴϧέ΍Ω̶ϨόϣϑϼΘΧ΍˼ Ζγ΍έ΍Ω̶ϨόϣˬΪηΎΑ˻ί΍ήΘ̳έΰΑΎҨήΑ΍ήΑtϖϠτϣέΪϗ.̊

    29


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 ΪҨέϭ΁ΖγΪΑ΍έweight -afterήϴϐΘϣΎҨϢҨ̫έί΍ΪόΑϥίϭϦϴ̴ϧΎϴϣ  Analyze – compare Means – One_ sample T Test / Test variable ……weight , weight - after   Test value …..……75

Ζγ΍̀̋ϢҨ̫έί΍ΪόΑϥίϭϦϴ̴ϧΎϴϣϪ̰ϨҨ΍νήϓΎΑΪϴϫΩϡΎΠϧ΍̶ϧϮϣί΁ T-TEST /TESTVAL = 75 /MISSING = ANALYSIS /VARIABLES = wieght_after weight /CRITERIA = CI(.95) . One-Sample Statistics

11 11

Std. Deviation 18.15802 14.29240

Std. Error Mean 5.47485 4.30932

m

weight wieght_after

Mean 68.3182 63.5455

Mean Difference -7.05000 -11.45455

ab

9 10

Sig. (2-tailed) .273 .024

.K

et

weight wieght_after

df

95% Confidence Interval of the Difference Lower Upper -20.7111 6.6111 -21.0563 -1.8528

lin

Test Value = 75

e.

One-Sample Test

t -1.167 -2.658

Sig.(2-tailed) = p  value  0.024 0.05 =



co

N

  ϪΠϴΘϧ

w w

 H 0 νήϓΩέ Ωέ΍ΪϧΩϮΟϭ H 0 ΪϴҨΎΗ̵΍ήΑ̶ϠϴϟΩβ̡Ζγ΍ϩΪηήΘ̰̩Ϯ̯  ί΍ weight-afterήϴϐΘϣ)

p  value έ΍ΪϘϣϥϮ̩˺

w

ΩϮΧϦҨ΍Ϫ̯ΪηΎΑ̶ϤϧήϔλϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγέΩϩΪηϩΩ΍ΩϥΎθϧΩΪϋϭΩ˻ Ζγ΍έΎ̰η΁ΕϭΎϔΗϩΪϨϫΩϥΎθϧϭΖγ΍ H 0 νήϓΩέϞϣΎϋ ΪηΎΑϡή̳ϮϠϴ̯̀̋Ϊϧ΍ϮΘϴϤϧϢҨ̫έί΍ΪόΑϥίϭϦϴ̴ϧΎϴϣβ̡Ζγ΍έ΍Ω̶ϨόϣϑϼΘΧ΍˼ Ζγ΍έ΍Ω̶ϨόϣˬΪηΎΑ˻ί΍ήΘ̳έΰΑΎҨήΑ΍ήΑtϖϠτϣέΪϗ.̊ 

 exclude cases analysis by ϪϨҨΰ̳ optionέΩή̳΍ϪΘ̰ϧ έΩ aly ysis   ϢϴηΎΑ Ϣ Α ϪΘη΍Ω missiing ϭ ϢϴϧΰΑ Ϣ ϧΰΑ ̮ϴΗ ϴΗ ΍έ ana ϭΪϨ̶̯ϣΏΎδΣϪϧΎ̳΍ΪΟ΍έϡ΍Ϊ̯ήϫήϴϐΘϣϭΩϪδҨΎϘϣ ϪΘϓή̳ήψϧέΩϥϭΪΑ΍έϪΘϓέέΎϛϪΑϱΎϫήϴϐΘϣϱ΍ήΑϥϮϣί΁  ήψ  ϥϭΪ   Θ  ϛ  Ύ ϣ ΍ήΑϥϮϣί ΩϭέϲϣέΎϛϪΑή̴ϳΩϱΎϫήϴϐΘϣϱ΍ήΑϩΪθϤ̳ήϳΩΎϘϣ ̮ϴΗ΍έ es listtwise e̶ϨόҨϡϭΩϪϨҨΰ̳ή̳΍̶ϟϭ  ϴ ΍έexcllude case ̶Ϩ Ҩ ϭΩ ΰ̳ή̳΍̶ϟ ̶ϣϑάΣ΍έΩέ΍Ω missingϪ̵̯ caseΎҨΩέϮϣϞ̯ϢϴϧΰΑ έΎϛϪΑϱΎϫήϴϐΘϣϪϤϫϱ΍ήΑϩΪθϤ̳έ΍ΪϘϣΎΑΩέϮϣ    Ύ ήϴϐΘϣϪ ϫ ΍ήΑϩ Ϥ έ΍Ϊ  ΑΩέϮ  ΪϨ̯ έΩ Ϫ̩ ΪηΎΑ ̵΍ ϩΩ΍Ω ϥΩή̰ϧ ̠ҨΎΗήΛ΍ έΩ Ϫ̩ missing ϦҨ΍ .Ϊη Ϊϫ΍ϮΨϧ ϪΘϓή̳ ήψϧ έΩ ϥϮϣί΁ έΩ ϪΘϓέ ΪϨ̶̯Ϥϧ̶ϗήϓΪηΎΑϩΪηϒҨήόΗ g value e Ϊ ̶Ϥ  Ϊ Αϩ ϒ ήόΗmissing 30


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‫ﮐﺘﺎﺑﺨﺎﻧﻪ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

ŶŝįŹįŚƣō ŵŚŤſřSPSS1

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ƱřźƿřżƿřƺŤǀŤƀƳř 

 ( Independent-Samples T Test ƵŵŹŚƿƵƹźĭƹŵƲǀŝźǀƜŤƯĨƿƲǀĮƳŚǀƯƱƺƯŻō 

ϢϴϨϛϲϣϩΩΎϔΘγ΍εϭέϦϳ΍ί΍ϩϭή̳ϭΩϦϴΑέΩΖϴόϤΟϲ̳̬ϳϭϚϳϦϴ̴ϧΎϴϣϥΩϮΑήΑ΍ήΑϥϮϣί΁ϱ΍ήΑ  ϲϤϛ έ΍ΪϘϣ Ϛϳ ήϴϐΘϣ ϭ Ζγ΍ ϩΪη ϊϳίϮΗ ϝΎϣήϧ ΕέϮλ ϪΑ ΖϴόϤΟ ϲ̳̬ϳϭ Ϫϛ Ζγ΍ Ϧϳ΍ήΑ νήϓ Ζγ΍ ϱΩΪϋ

Ζγ΍ϩΪηΏΎΨΘϧ΍ϲϓΩΎμΗϭϞϘΘδϣΕέϮλϪΑϪϧϮϤϧϦϴϨ̪Ϥϫ Ζγ΍έ΍ΩέϮΧήΑΖϴϤϫ΍ί΍ϥϮϣί΁Ϧϳ΍έΩΕή̡ρΎϘϧΩϮΟϭϡΪϋϭϥέΎϘΗ ΖδϴϧαΎδΣϲ̴ϟϮ̩ϪΑΖΒδϧϥϮϣί΁Ϧϳ΍ ΪϫΩϲϣήϴϴϐΗ΍έϥϮϣί΁ϲΟϭήΧ  ˬϩϭή̳ϭΩέΩΎϫϥ΁ϥΩϮΒϧήΑ΍ήΑΎΑ  ΎϫβϧΎϳέ΍ϭϥΩϮΑήΑ΍ήΑ 

Analyze -

 ΪϴϨϛϲσ΍έήϳίήϴδϣέϮΘγΩϦϳ΍ϪΑϲγήΘγΩϱ΍ήΑ Compare Means - Independent-Samples T Test/Test variables…….weight

w

w w

.K

et

ab

lin

e.

co

 test variableΖϤδϗέΩ ΍έ ΪϧϮη ϥϮϣί΁ ΪϳΎΑ Ϫϛ ϲϳΎϫήϴϐΘϣ Ύϳ ήϴϐΘϣ ΪϴϨϛΩέ΍ϭ Grouping VariableΖϤδϗέΩ ΪϴϨϛκΨθϣ΍έϱΪϨΑϩϭή̳  ήϴϐΘϣ ϭΩ ϦϴΑ ϪδϳΎϘϣ Ϊϧ΍ϮΗϲϣ ςϘϓ ϥϮϣί΁ Ϧϳ΍ Ωέϭ΁ΖγΪΑ΍έϩϭή̳ ήϴϐΘϣ ϱ΍ήΑ define group ϪϤϛΩ ΏΎΨΘϧ΍ ΎΑ ϭΩ ϒϳήόΗ ϱ΍ήΑ ήψϧ ΩέϮϣ έ΍ΪϘϣ ϱΪϨΑϩϭή̳  ̵ΪϨΑϩϭή̳ήϴϐΘϣή̳΍̶ΘΣΪϴϨϛΩέ΍ϭ΍έϩϭή̳ ϥϮϣί΁ ϦҨ΍ ΪηΎΑ ϪΘη΍Ω ϩϭή̳ ϭΩ ί΍ ήΘθϴΑ ΪηΪϫ΍ϮΧϪΒγΎΤϣϩϭή̳ϭΩέΩςϘϓ έ΍ΪϘϣϱϭΎδϣϭήΘθϴΑϭήΘϤϛΕέϮλϪΑΎϫϩϭή̳ϚϴϜϔΗϥΎϜϣ΍ΰϴϧ  cut pointέ΍ΪϘϣϢϴψϨΗΎΑϦϴϨ̪Ϥϫ Ζδϫΰϴϧcut point   H0:m1 m2 0 H0: m1  m2 SPSS   H1:m1 m2 0 H1: m1  m2  κΨθϣ΍έϝϭ΍ϪΘγΩˬϱΪϨΑϩϭή̳ήϴϐΘϣϱ΍ήΑ  Group1έ΍ΪϘϣ ΪϨϛϲϣ  κΨθϣ΍έϡϭΩϪΘγΩˬϱΪϨΑϩϭή̳ήϴϐΘϣϱ΍ήΑ  Group2έ΍ΪϘϣ ΪϨϛϲϣ  Cut έ΍ΪϘϣϦϴϴόΗΎΑϥ΍ϮΘϴϣΩϮΑ̵ΩΪϋˬ̵ΪϨΑϩΩέήϴϐΘϣή̳΍ Ωή̯ϒϳήόΗήϳίΕέϮλϪΑ΍έϩϭή̳ϭΩPoint cut pointί΍ϱϭΎδϣϭήΘθϴΑήϳΩΎϘϣ˺ϩϭή̳ cut pointί΍ήΘϤϛήϳΩΎϘϣ˻ϩϭή̳    31 www.ircdvd.com

‫ﮔﺮﻭﻩ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

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m

Grouping variables……gender


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

  ΪϴϨ̶̯γέήΑϥ΍ΩήϣϭϥΎϧίϩϭή̳ϭΩέΩ΍έϥίϭήϴϐΘϣϦϴ̴ϧΎϴϣ

H0: m1  m2 H1: m1  m2

SPSS 



H0:m1 m2 0

ϥ΍Ωήϣϩϭή̳έΩϥίϭήϴϐΘϣϦϴ̴ϧΎϴϣ m1

H1:m1 m2 0

ϥΎϧίϩϭή̳έΩϥίϭήϴϐΘϣϦϴ̴��Ύϴϣ m2 

Group Statistics

Std. Deviation

4

75.7500

19.12023

6

62.7500

18.86730

Std. Error Mean

9.56012

lin

female

Mean

e.

N

7.70254

ab

weight

gender male

co

m

T-TEST GROUPS = gender(1 2) /MISSING = ANALYSIS /VARIABLES = weight /CRITERIA = CI(.95) .

Independent Samples Test

.009

.928

w w

Equal variances assumed Equal variances not assumed

t

df

t-test for Equality of Means

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference Lower Upper

1.062

8

.319

13.00000

12.24027

-15.22611

41.22611

1.059

6.512

.327

13.00000

12.27701

-16.47694

42.47694

w

weight

Sig.

.K

F

et

Levene's Test for Equality of Variances

           

ϪΠϴΘϧ Sig = p  value  0.928 > 0.05 =

̶ϤϧΩέΎϫβϧΎҨέ΍ϭ̵ήΑ΍ήΑβ̡Ζγ΍ϩΪη ήΘ̳έΰΑ  ί΍ Ζγ΍βϧΎҨέ΍ϭϪΑρϮΑήϣϪ̯  weightήϴϐΘϣ) p  value έ΍ΪϘϣϥϮ̩˺ Ωή̯Ϣϴϫ΍ϮΧ̶γέήΑ΍έϡϭΩήτγΕέϮμϨҨ΍ήϴϏέΩϢϴϨ̶̯ϣϩΎ̴ϧϝϭ΍ήτγέΩϦϴ̴ϧΎϴϣϪΑρϮΑήϣ p  value ϪΑ΍άϟΩϮη Sig(2-tailed) = p  value  0.319 > 0.05 =

 

ΩϮΧϦҨ΍Ϫ̯ΪηΎΑ̶ϣήϔλϞϣΎη  95% Confidence Interval of the Difference ϥϮΘγέΩϩΪηϩΩ΍ΩϥΎθϧΩΪϋϭΩ˻ Ζγ΍ϪϧϮϤϧςγϮΗ H 0 νήϓΪϴҨΎΗϞϣΎϋ Ζγ΍ήΑ΍ήΑϥΎϧίϭϥ΍Ωήϣϩϭή̳ϭΩέΩϥίϭϦϴ̴ϧΎϴϣβ̡Ζδϴϧέ΍Ω̶ϨόϣϑϼΘΧ΍˼ Ζγ΍έ΍Ω̶ϨόϣˬΪηΎΑ˻ί΍ήΘ̳έΰΑΎҨήΑ΍ήΑtϖϠτϣέΪϗ.̊

32


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

ΪϴϨ̶̯γέήΑϝΎγ˼˹ϦϴҨΎ̡ϭϝΎγ˼˹̵ϻΎΑ̶Ϩγϩϭή̳ϭΩέΩ΍έϥίϭήϴϐΘϣϦϴ̴ϧΎϴϣ computeΎҨrecodeϩ΍έί΍ϥ΍ϮΘϴϣϝΎγ˼˹ϦϴҨΎ̡ϭϝΎγ˼˹̵ϻΎΑϩϭή̳ϭΩϪΑϥίϭήϴϐΘϣ̵ΪϨΑϩϭή̵̳΍ήΑ ϪϤ̳Ωϭ Independent-Samples T TestϩήΠϨ̡έΩϪ̯Ζγ΍ϦҨ΍ϩ΍έϦҨήΗϩΩΎγ̶ϟϭΩϮϤϧϩΩΎϔΘγ΍ Ωή̯Ωέ΍ϭ΍έ˼˹ΩΪϋϭΩϮϤϧϩΩΎϔΘγ΍cutpointϪϨҨΰ̳ί΍option

H0: m1  m2 H1: m1  m2

H0:m1 m2 0

SPSS 

ϝΎγ˼˹ί΍ήΗϦϴҨΎ̶̡Ϩγϩϭή̳έΩϥίϭήϴϐΘϣϦϴ̴ϧΎϴϣ m1

H1:m1 m2 0

ϝΎγ˼˹ί΍ήΗϻΎΑ̶Ϩγϩϭή̳έΩϥίϭήϴϐΘϣϦϴ̴ϧΎϴϣ m2



co

m

T-TEST GROUPS = age(30) /MISSING = ANALYSIS /VARIABLES = weight /CRITERIA = CI(.95) .

e.

Group Statistics

Std. Deviation 18.36534

Std. Error Mean 6.94145

3

52.5000

10.85127

6.26498

ab

< 30.00

lin

7

Mean 74.5714

N

et

weight

age >= 30.00

Independent Samples Test

w w



F

Equal variances assumed Equal variances not assumed

1.334

w

weight



.K

Levene's Test for Equality of Variances

Sig.

.281

t-test for Equality of Means

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference Lower Upper

1.903

8

.093

22.07143

11.59642

-4.66997

48.81283

2.360

6.606

.052

22.07143

9.35060

-.30937

44.45223

ϪΠϴΘϧ ̶ϤϧΩέΎϫβϧΎҨέ΍ϭ̵ήΑ΍ήΑβ̡Ζγ΍ϩΪη ήΘ̳έΰΑ  ί΍Ζγ΍βϧΎҨέ΍ϭϪΑρϮΑήϣϪ̯  weightήϴϐΘϣ) p  value έ΍ΪϘϣϥϮ̩˺ Ωή̯Ϣϴϫ΍ϮΧ̶γέήΑ΍έϡϭΩήτγΕέϮμϨҨ΍ήϴϏέΩϢϴϨ̶̯ϣϩΎ̴ϧϝϭ΍ήτγέΩϦϴ̴ϧΎϴϣϪΑρϮΑήϣ p  value ϪΑ΍άϟΩϮη Sig(2-tailed) = p  value  0.093 > 0.05 =

 

ΩϮΧϦҨ΍Ϫ̯ΪηΎΑ̶ϣήϔλϞϣΎη  95% Confidence Interval of the Difference ϥϮΘγέΩϩΪηϩΩ΍ΩϥΎθϧΩΪϋϭΩ˻ Ζγ΍ϪϧϮϤϧςγϮΗ H 0 νήϓΪϴҨΎΗϞϣΎϋ Ζγ΍ήΑ΍ήΑϝΎγ˼˹ί΍ήΗϦϴҨΎ̡ϭϝΎγ˼˹ί΍ήΗϻΎΑ̶Ϩγϩϭή̳ϭΩέΩϥίϭϦϴ̴ϧΎϴϣβ̡Ζδϴϧέ΍Ω̶ϨόϣϑϼΘΧ΍˼ Ζγ΍έ΍Ω̶ϨόϣˬΪηΎΑ˻ί΍ήΘ̳έΰΑΎҨήΑ΍ήΑtϖϠτϣέΪϗ.̊

33


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

Ϫ̯ ̶ϧΎδ̯ ϭ Ϊϧέ΍Ω ϪϴϟΎϋ ΕϼϴμΤΗ Ϫ̯ ̶ϧΎδ̯ ϩϭή̳ ϭΩέΩ  Demo ϞҨΎϓ έΩ ΍έ Ϊϣ΁έΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ ΪϴϨ̶̯γέήΑΪϧ΍ϩΩή̰ϧϞϴϤ̰Η΍έϥΎθΗϼϴμΤΗ 

ϩΪθϧϞϴϤ̰ΗΕϼϴμΤΗϩϭή̳έΩincomeΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m1

H0: m1  m2 H1: m1  m2

SPSS 

H0:m1 m2 0

ϪϴϟΎϋΕϼϴμΤΗϩϭή̳έΩincomeΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m2

H1:m1 m2 0

 

.

m

T-TEST GROUPS = ed(1 5) /MISSING = ANALYSIS /VARIABLES = income /CRITERIA = CI(.95) .

Mean

1390

Post-undergraduate degree

59.8662 87.1699

Std. Error Mean

61.30036

1.64420

94.79998

5.00335

ab

359

Std. Deviation

e.

N

lin

Household income in thousands

Level of education Did not complete high school

co

Group Statistics

et

Independent Samples Test

Levene's Test for Equality of Variances

w w

.K

t-test for Equality of Means

F

Equal variances assumed Equal variances not assumed

35.128

t

.000

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

-6.636

1747

.000

-27.30373

4.11419

-35.37298

-19.23448

-5.184

438.180

.000

-27.30373

5.26659

-37.65464

-16.95282

w

Household income in thousands

Sig.

95% Confidence Interval of the Difference Lower Upper

 ϪΠϴΘϧ Sig = p  value  0.000  0.05 =

Ωέ Ύϫ βϧΎҨέ΍ϭ ̵ήΑ΍ήΑ β̡ Ζγ΍ ϩΪη ήΘ̰̩Ϯ̯  ί΍ Ζγ΍ βϧΎҨέ΍ϭ ϪΑ ρϮΑήϣ Ϫ̯  income ήϴϐΘϣ) p  value έ΍ΪϘϣ ϥϮ̩˺ ΩϮη̶ϣΩέ H 0 νήϓβ̡Ζγ΍  ί΍ήΘ̰̩Ϯ̯ϢϴϨ̶̯ϣϩΎ̴ϧϡϭΩήτγέΩϦϴ̴ϧΎϴϣϪΑρϮΑήϣ p  value ϪΑ΍άϟΩϮη̶ϣ Sig(2-tailed) = p  value  0.000  0.05 =

 

ΩϮΧϦҨ΍Ϫ̯ΪηΎΑ̶ϤϧήϔλϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγέΩϩΪηϩΩ΍ΩϥΎθϧΩΪϋϭΩ˻ Ζγ΍έΎ̰η΁ΕϭΎϔΗϩΪϨϫΩϥΎθϧϭΖγ΍ H 0 νήϓΩέϞϣΎϋ ΖδϴϧήΑ΍ήΑϪϴϟΎϋϭϩΪθϧϞϴϤ̰ΗΕϼϴμΤΗϩϭή̳ϭΩέΩΪϣ΁έΩϦϴ̴ϧΎϴϣβ̡Ζγ΍έ΍Ω̶ϨόϣϑϼΘΧ΍˼ Ζγ΍έ΍Ω̶ϨόϣˬΪηΎΑ˻ί΍ήΘ̳έΰΑΎҨήΑ΍ήΑtϖϠτϣέΪϗ.̊

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 ( Paired-Samples T Test ŢƠūźǀƜŤƯƹŵƲǀĮƳŚǀƯƶƀƿŚƤƯƱƺƯŻō ϢϴϨϛϲϣϩΩΎϔΘγ΍ϥϮϣί΁Ϧϳ΍ί΍ΪϧϮηϪδϳΎϘϣΪϳΎΑϪϧϮϤϧέΩήϴϐΘϣϭΩϦϴ̴ϧΎϴϣή̳΍  ϪϛΪϧϮηϲϣϪΘϓή̳ήψϧέΩ   x,y ΐΗήϣΝϭίΕέϮλϪΑϩΪηϱήϴ̳ϩί΍Ϊϧ΍ϱΎϫϪμΨθϣϥϮϣί΁Ϧϳ΍έΩ Ζγ΍ϪϧϮϤϧέΩϡϭΩϲ̳̬ϳϭyϭϝϭ΍ϲ̳̬ϳϭxϥ΁έΩ ΍ΪΘΑ΍ έΩ ϪϧϮϤϧ Ω΍ήϓ΍ ϪΑ ϢϴϨϛ κΨθϣ ϥϮΧ έΎθϓ ζϫΎϛ έΩ ΍έ ϭέ΍Ω Ϛϳ ήΛ΍ Ϣϴϫ΍ϮΧϲϣ ϝΎΜϣ ϱ΍ήΑ ϥϮΧ έΎθϓ ΰϴϧ ϲόϗ΍ϭ ϭέ΍Ω ϥΪϧ΍ΩέϮΧ ί΍ β̢γ ϢϴϨϛϲϣ ϱήϴ̳ϩί΍Ϊϧ΍ ΍έ ϥϮΧ έΎθϓ ϩΩ΍Ω ΎϤϧϭέ΍Ω ϪδϳΎϘϣϲόϗ΍ϭϱϭέ΍Ωί΍ΪόΑϭϞΒϗ΍έϥϮΧέΎθϓϦϴ̴ϧΎϴϣΪϫ΍ϮΧϲϣϥϮϣί  ΁ΪηΪϫ΍ϮΧϱήϴ̳ϩί΍  Ϊϧ΍ ΪϨϛ ΪϨΘδϫϱΩΪϋήϳΩΎϘϣΖγ΍Ϧϳ΍ήΑνήϓ  Ζγ΍ϝΎϣήϧϊϳίϮΗϱ΍έ΍ΩΎϬϧ΁ΕϭΎϔΗΎϳ Ϊϧέ΍ΩϝΎϣήϧϊϳίϮΗήϴϐΘϣϭΩήϫΎϫϩΩ΍Ω  Ϊϧ΍ϩΪηΏΎΨΘϧ΍ϲϓΩΎμΗϭϞϘΘδϣΕέϮλϪΑΎϫ  ϪϧϮϤϧ έϮσϪΑήϴϐΘϣϚϳέΩϩΪθϤ̳έ΍ΪϘϣΩϮΟϭΕέϮλέΩΪηΎΑήΑ΍ήΑΪϳΎΑήϴϐΘϣϭΩήϫϱ΍ήΑΎϫϪϧϮϤϧ  Ω΍ΪόΗ ΪηΪϫ΍ϮΧϪΘϓή̳ϩΪϳΩΎϧΩέϮϣϥ΁ϞϣΎϛ Ωέ΍ΪϧϲϟΎϜη΍ήϴϐΘϣϭΩβϧΎϳέ΍ϭέ΍ΪϘϣέΩΕϭΎϔΗ

co

e.

Compare Means - Paired-Samples T Test...

H0:m1 m2 0 H1:m1 m2 0

w

w w

.K

H1: m1  m2

SPSS 

ab

H0: m1  m2

ϝϭ΍ήϴϐΘϣϦϴ̴ϧΎϴϣ m1 ϡϭΩήϴϐΘϣϦϴ̴ϧΎϴϣ m2

et

Analyze -

lin

ΪϴϨϛϲσ΍έήϳίϞΣ΍ήϣϥϮϣί΁Ϧϳ΍ϪΑϲγήΘγΩϱ΍ήΑ

ϪΑϭϩΩήϛΏΎΨΘϧ΍̠̩ΖϤγΖϤδϗέΩ΍έήϴϐΘϣϭΩήϫ .ΪϴϫΩϝΎϘΘϧ΍pair VariableΖϤδϗ                  35

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m




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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

Analyze -

H0: m1  m2 H1: m1  m2

ƱřźƿřżƿřƺŤǀŤƀƳř

  ΪϴϨ̯ϪδҨΎϘϣϢϫΎΑ΍έweight-after ϭweightήϴϐΘϣϭΩ  Compare Means - Paired-Samples T Test...

SPSS 

H0:m1 m2 0

weightϥίϭήϴϐΘϣϦϴ̴ϧΎϴϣ m1

H1:m1 m2 0

weight-afterϢҨ̫έί΍ΪόΑϥίϭήϴϐΘϣϦϴ̴ϧΎϴϣ m2

 

Paired Samples Correlations

weight & wieght_after

Correlation .941

Sig. .000

lin

10

e.

N Pair 1

co

m

T-TEST PAIRS = weight WITH wieght_after (PAIRED) /CRITERIA = CI(.95) /MISSING = ANALYSIS.

ab

Paired Samples Test

.K

weight - wieght_after

Std. Deviation 7.37884

Std. Error Mean 2.33339

95% Confidence Interval of the Difference Lower Upper .37150 10.92850

t 2.421

df 9

Sig. (2-tailed) .039



w w

Pair 1

Mean 5.65000

et

Paired Differences

w

ϪΠϴΘϧ  ήϴϐΘϣϭΩ̶ϨόҨΪηΎΑήΘ̰ҨΩΰϧ 1ΩΪϋϪΑΩΪϋϦҨ΍Ϫ̩ήϫϪ̯ΪϫΪϴϣϥΎθϧ΍έ 0.941ΩΪϋ correlationΎҨ̶̴ΘδΒϤϫΐҨήο ˺ ΪϧΩϮΑ̲ϨϫΎϤϫϢϫΎΑ˱ϼϣΎ̯ϭΖγ΍ϪΘη΍ΩΩϮΟϭϢҨ̫έί΍β̡ϥίϭζϫΎ̯Ωέ΍ϮϣϡΎϤΗέΩ̶ϨόҨΪϧέ΍ΩϢϫ̵ϭέ̵ήΘθϴΑήΛ΍ ΩϮη̶ϣΩέ H 0 νήϓˬ  ί΍ p  value ϥΩϮΑήΘ̰̩Ϯ̯ϪΑϪΟϮΗΎΑ˻ Sig(2-tailed) = p  value  0.039  0.05 =

 

ΩϮΧϦҨ΍Ϫ̯ΪηΎΑ̶ϤϧήϔλϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγέΩϩΪηϩΩ΍ΩϥΎθϧΩΪϋϭΩ˼ Ζγ΍έΎ̰η΁ΕϭΎϔΗϩΪϨϫΩϥΎθϧϭΖγ΍ H 0 νήϓΩέϞϣΎϋ

ΖδϴϧήΑ΍ήΑϢҨ̫έί΍ΪόΑϥίϭϭϥίϭήϴϐΘϣΖϔΟϦϴ̴ϧΎϴϣβ̡Ζγ΍έ΍Ω̶ϨόϣϑϼΘΧ΍̊ Ζγ΍έ΍Ω̶ϨόϣˬΪηΎΑ˻ί΍ήΘ̳έΰΑΎҨήΑ΍ήΑtϖϠτϣέΪϗ.̋

      36


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   One-Way Anova ƵƹźĭƲƿŶƴģŹŵźǀƜŤƯĨƿƲǀĮƳŚǀƯƶƀƿŚƤƯƱƺƯŻō

co

e.

lin

ab

et

.K

w w

w

ϡΎΠϧ΍΍έΎϫϲϳΎΗϭΩϪΑρϮΑήϣϥϮϣί΁Ϊϴϫ  ΍ϮΧϲϣή̳΍  ΪϴϨϛΏΎΨΘϧ΍΍έPost HocϪϨϳΰ̳ΪϴϫΩ ϥΩϮΑ ήΑ΍ήΑ ΕέϮλ έΩ Ϊϴϧ΍ϮΗϲϣ ΍έ ϥϮϣί΁ ωϮϧ ΪϨϧΎϣ ϲϟϭ΍ έΩΎϛ ϱΎϫϪϨϳΰ̳ ί΍ ϲϜϳ ΎϫβϧΎϳέ΍ϭ ΪϴϨϛΏΎΨΘϧ΍tukey-Bunfferoni- LSD ΪϴϧΰΑ΍έContinue  ϪϤϛΩβ̢γ ϱΎϫϦϴ̴  ϧΎϴϣ ί΍ ϲτΧ ϲΒϴϛήΗ Ϊϴϫ΍ϮΧϲϣ ή̳΍ Contrast ϪϤϛΩ ί΍ ΪϴϨϛ ϪδϳΎϘϣ Ϣϫ ΎΑ ΍έ Ύϫϩϭή̳  ΪϴϨϛϩΩΎϔΘγ΍ ΐϳ΍ήοωϮϤΠϣΖγ΍ήΘϬΑϪϛΪϴηΎΑϪΘη΍ΩϪΟϮΗ ΪηΎΑήϔλήΑ΍ήΑϲτΧΐϴϛήΗ 37 www.ircdvd.com

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m

 ΩϮΟϭϩϭή̳ϦϳΪϨ̩ϦϴΑέΩκΧΎηϚϳέ΍ΪϘϣϪδϳΎϘϣϥΎϜϣ΍ ϪϓήσϚϳβϧΎϳέ΍ϭΰϴϟΎϧ΁ϝϭΪΟί΍ϩΩΎϔΘγ΍ΎΑ Ωέ΍Ω ϲϫϭή̳ ϥϭέΩ ϱΎτΧ ΕΎόΑήϣ Ϧϴ̴ϧΎϴϣ έ΍ΪϘϣ ΖΒδϧ ή̳΍ βϧΎϳέ΍ϭ ΰϴϟΎϧ΁ ϝϭΪΟ ϱΎϫϩέΎϣ΁ ϪΑ ϪΟϮΗ ΎΑ ϲϫϭή̳ ϦϴΑ ϱΎτΧΕΎόΑήϣϦϴ̴ϧΎϴϣέ΍ΪϘϣΎΑ ϩϭή̳Ϧϴ̴ϧΎϴϣί΍ϩϭή̳ήϫήϳΩΎϘϣϑϼΘΧ΍ΕΎόΑήϣωϮϤΠϣ

ζϘϧϩΪϨϫΩϥΎθϧΪηΎΑϱΩΎϳίϑϼΘΧ΍ϱ΍έ΍Ω ϞϛϦϴ̴ϧΎϴϣί΍ϩϭή̳ήϫϦϴ̴ϧΎϴϣϑϼΘΧ΍ΕΎόΑήϣωϮϤΠϣ

ΪϫΩϲϣϥΎθϧ΍έϩϭή̳Ϧϴ̴ϧΎϴϣήϴϴϐΗέΩϞϣΎϋ   Ϫϴϟϭ΍ϱΎϫνήϓ ΪηΎΑϪΘη΍Ω΢ϴΤλήϳΩΎϘϣΪϳΎΑέϮΘϛΎϓήϴϐΘϣ ΪηΎΑϪΘγϮϴ̡ήϳΩΎϘϣϱ΍έ΍ΩΪϳΎΑϪΘδΑ΍ϭήϴϐΘϣ ΪϨηΎΑϲϓΩΎμΗϝΎϣήϧϊϳίϮΗϱ΍έ΍ΩΪϳΎΑϩϭή̳ήϫέΩϪϧϮϤϧ ΪηΎΑϥέΎϘΗϱ΍έ΍ΩϊϳίϮΗΪϳΎΑϲϟϭΖγ΍ϡϭΎϘϣϥΩϮΑϝΎϣήϧρήηϪΑΖΒδϧβϧΎϳέ΍ϭΰϴϟΎϧ΁ϥϮϣί΁ ϩέΎϣ΁ ί΍ ϩΩΎϔΘγ΍ ΎΑ ΍έ ρήη Ϧϳ΍  ΪϨηΎΑ ϩΪη ΏΎΨΘϧ΍ ήΑ΍ήΑ βϧΎϳέ΍ϭ ΎΑ ϲΘϴόϤΟ ί΍ ΪϳΎΑ Ύϫϩϭή̳   Ωήϛϱήϴ̳ϩί΍Ϊϧ΍ϥ΍ϮΗϲϣ  Levens  Ωέ΍ΩΩϮΟϭΎϫϩϭή̳ϦϴΑέΩϦϴ̴ϧΎϴϣέ΍ΪϘϣέΩήϴϴϐΗϱήϴ̳ϩί΍Ϊϧ  ΍ϥΎϜϣ΍ϝϭΪΟϦϳ΍ί΍ϩΩΎϔΘγ΍ΎΑ ΪϴϨϛϲσ΍έήϳίϞΣ΍ήϣέϮΘγΩϦϳ΍ϪΑϲγήΘγΩϱ΍ήΑ  Analyze - Compare Means - One-Way ANOVA... Ωέ΍ϭ  Dependent List ϞΤϣ έΩ ΍έ ϪΘδΑ΍ϭ ήϴϐΘϣ ΪϴϨϛ ΍έ ϪΘδΑ΍ϭ ήϴϐΘϣ έ΍ΪϘϣ Ϫϛ ϱήϴϐΘϣ  ϞϣΎϋ ήϴϐΘϣ ϥϮϣί΁ Ϊϴϫ΍ϮΧϲϣ ϥ΁ ϒϠΘΨϣ ϱΎϫϩϭή̳  ϪΑ ΖΒδϧ ΪϴϨϛΩέ΍ϭFactorΖϤδϗέΩ΍έ ΪϴϨϛ Ωέ΍ϭ Ζδϴϟ έΩ ΍έ ϪΘδΑ΍ϭ ήϴϐΘϣ ϦϳΪϨ̩ ϥ΍ϮΗϲϣ  ήϴϐΘϣ Ϛϳ ί΍ ςϘϓ έϮΘϛΎϓ ήϴϐΘϣ ϱ΍ήΑ ϲϟϭ ΪϴϨϛ ΩήϛϩΩΎϔΘγ΍ϥ΍ϮΗϲϣ  έϮΘϛΎϓήϴϐΘϣΡϮτγέΩήϴϐΘϣήϫϦϴ̴ϧΎϴϣέΎϛϦϳ΍ΎΑ ΪϧϮηϲϣϪδϳΎϘϣ 


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co

m

CoefficientΖϤδϗέΩ΍έϦϴ̴ϧΎϴϣήϫΐϳ΍ήοΐϴΗήΗϪΑ ΪϴϧΰΑ΍έAddϪϤϛΩϭϩΩήϛΩέ΍ϭ Coefficient ΖϤδϗέΩΐϳ΍ήοωϮϤΠϣϪϛΪϴϨϛΖϗΩ ΪηΎΑήϔλήΑ΍ήΑΪϳΎΑϭΖγ΍ϩΪηϪΒγΎΤϣTotal ϪϨϳΰ̳Ϊϳέ΍ΩΝΎϴΘΣ΍ΎϫϦϴ̴ϧΎϴϣϦϴΑϲτΧϪτΑ΍έϚϳή̳΍  ΪϴϨϛΏΎΨΘϧ΍΍έPolynomial ΪϴϧΰΑ΍έContinueϪϤϛΩ

e.

źǀƜŤƯŶƴģƲǀĮƳŚǀƯƶƀƿŚƤƯƱƺƯŻō

w

w w

.K

et

ab

lin

ΩϮΑΪϫ΍ϮΧήϳίΕέϮλϪΑϥϮϣί΁νήϓ H0: m1=m2=m3 H1: m1#m2#m3  ϡΪϋΖϠϋϪϛΩήϛκΨθϣϲϳΎΗϭΩϱΎϫϥϮϣί΁ί΍ϩΩΎϔΘγ΍ΎΑϥ΍ϮΗ   ϲϣΩϮηΩέήϔλνήϓϪϛϲΗέϮλέΩ Ζγ΍ϩΩϮΑΝ��ίϡ΍Ϊϛϱ΍ήΑϱϭΎδΗ  ΪϨγΎϨηϲϣΰϴϧ  Post Hocϥ΍ϮϨϋϪΑ΍έϥϮϣί΁Ϧϳ΍ Ωέ΍ΩΩϮΟϭ Contrast ΎϫϦϴ̴ϧΎϴϣί΍ϲΒϴϛήΗϥϮϣί΁ϥΎϜϣ΍ϦϴϨ̪Ϥϫ 

              38


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 ˮΩέ΍Ω̵ήΛ΍Ϫ̶̩ϠϴμΤΗϒϠΘΨϣΡϮτγήΑΪϣ΁έΩϦϴ̴ϧΎϴϣϪ̯ΪϴϨ̯ϪδҨΎϘϣDemoϞҨΎϓέΩ  Analyze - Compare Means - One-Way Anova

H 0 : m1  m 2  m3  m 4  m5

m

H 1 : m1  m 2  m3  m 4  m5

    did not completed high school ϝϭ΍ϩϭή̳έΩΪϣ΁έΩ ήϴϐΘϣϦϴ̴ϧΎϴϣ m1 high sckool degree ϡϭΩϩϭή̳έΩΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m2 some college ϡϮγϩϭή̳έΩΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m3 college degree ϡέΎϬ̩ϩϭή̳έΩΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m4 post-undergraduate degree ϢΠϨ̡ϩϭή̳έΩΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m5

lin

e.

co

GET FILE='C:\Program Files\SPSS\Tutorial\sample_files\demo.sav'. DATASET NAME DataSet1 WINDOW=FRONT. ONEWAY income BY ed /STATISTICS HOMOGENEITY /MISSING ANALYSIS .

ab

Test of Homogeneity of Variances

df1

df2 6395

4

Sig. .000

.K

Levene Statistic 14.766

et

Household income in thousands



w w

ANOVA

w

Household income in thousands

Sum of Squares

Between Groups

df

Mean Square

376079.699

4

94019.925

Within Groups

39276042.251

6395

6141.680

Total

39652121.950

6399

ΩϮη̶ϣΩέ H 0 νήϓˬ  Sig = p  value  0.000  0.05 =

F 15.309

Sig. .000

ϪΠϴΘϧ ί΍ p  value ϥΩϮΑήΘ̰̩Ϯ̯ϪΑϪΟϮΗΎΑ˺

ήΑ΍ήΑ ΕϼϴμΤΗ̵ΎϬϫϭή̳ϦϴΑέΩΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣβ̡ Ζγ΍ΕϼϴμΤΗ̵ΪϨΑϩϭή̳ ί΍̶ηΎϧϭ Ζγ΍έ΍Ω̶ϨόϣϑϼΘΧ΍˻ Ζδϴϧ between Groups - ϞϛϦϴ̴ϧΎϴϣί΍ϩϭή̳ήϫϦϴ̴ϧΎϴϣϑϼΘΧ΍ΕΎόΑήϣωϮϤΠϣ ̶ϫϭή̳ϦϴΑ̵ΎτΧΕΎόΑήϣΩΎҨίΕϭΎϔΗ˼ ϩΪϨϫΩϥΎθϧ within Groups ± ϩϭή̳Ϧϴ̴ϧΎϴϣί΍ϩϭή̳ήϫήϳΩΎϘϣϑϼΘΧ΍ΕΎόΑήϣωϮϤΠϣ ϲϫϭή̳ϥϭέΩϱΎτΧΕΎόΑήϣΎΑ Ζγ΍ϩϭή̳Ϧϴ̴ϧΎϴϣήϴϴϐΗέΩ factor ϞϣΎϋζϘϧ

39


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

βϧΎҨέ΍ϭϪ̯ΪϫΪϴϣϥΎθϧ Test of Homogeneity of VariancesϝϭΪΟέΩ ΎϬδϧΎҨέ΍ϭϪδҨΎϘϣϥϮϣί΁ί΍ϩΪϣ΁ΖγΪΑΞҨΎΘϧ̊  p  value  0.000 = Sig<  ϭΪϧέ΍Ω̵έ΍Ω̶ϨόϣϑϼΘΧ΍ΕϼϴμΤΗήϴϐΘϣϩϭή̳̋έΩΪϣ΁έΩήϴϐΘϣ̵Ύϫ ήϴϐΘϣϩϭή̳ ̋έΩΪϣ΁έΩήϴϐΘϣ̵ΎϫϦϴ̴ϧΎϴϣϦϴΑϪ̯ΪϫΪϴϣϥΎθϧ ANOVAϝϭΪΟέΩ βϧΎҨέ΍ϭΰϴϟΎϧ΁ί΍ϩΪϣ΁ΖγΪΑΞҨΎΘϧ̋ ϢϫΎΑϭΩϪΑϭΩ Post HOCϪϨҨΰ̳ί΍ϩΩΎϔΘγ΍ΎΑϭϢϴηΎΑΎϬϓϼΘΧ΍ϝΎΒϧΩϪΑ ΪҨΎΑβ̡Ωέ΍ΩΩϮΟϭ̵έ΍Ω̶ϨόϣϑϼΘΧ΍ΕϼϴμΤΗ ϢϴϨ̯ϪδҨΎϘϣ 

ab

lin

e.

co

m

Ωέ΍ΩΩϮΟϭΖϟΎΣϭΩPost HOCϩήΠϨ̡έΩ ΪϨηΎΑϪΘη΍Ϊϧ̶ΗϭΎϔΗϊϣ΍ϮΟβϧΎϳέ΍ϭϪ̶̯ΘϟΎΣέΩϩΩΎϔΘγ΍ΩέϮϣ̵ΎϬϧϮϣί΁ϪΑρϮΑήϣϻΎΑΖϤδϗ  Equal Variances Assumed  ΪϨηΎΑΕϭΎϔΘϣϊϣ΍ϮΟβϧΎϳέ΍ϭϪ̶̯ΘϟΎΣέΩϩΩΎϔΘγ΍ΩέϮϣ̵ΎϬϧϮϣί΁ϪΑρϮΑήϣϦϴϳΎ̡ΖϤδϗ  Equal Variances Not Assumed   έϮσϪΑΪϧέ΍Ϊϧ̵έ΍Ω̶ϨόϣϑϼΘΧ΍ϢϫΎΑΎϬϫϭή̳βϧΎϳέ΍ϭϪ̶̯ΘϟΎΣέΩ ΎϬδϧΎҨέ΍ϭ̵ήΑ΍ήΑ ϻΎΑΖϤδϗέΩ ΩϮη̶ϣΏΎΨΘϧ΍̵έΎϣ΁ϞϴϠΤΗϭϪϳΰΠΗ̵΍ήΑ΍έ DunnettˬDuncanˬTukey ϝϭ΍ΪΘϣϥϮϣί΁ϪγϪϧϮϤϧ ϩϭή̳ ̋  ΎϬϫϭή̳ ί΍ ̶̰ϳ ϥΎϴϣ ί΍ Ϫ̯ Ω΍Ω έ΍ήϗ ϪΟϮΗ ΩέϮϣ ΪϳΎΑ ΍έ ϪΘ̰ϧ Ϧϳ΍ Dunnett ϥϮϣί΁ ΩέϮϣ έΩ Ϧϳ΍ΪϨΠϨδΑϥ΁ΎΑ΍έΎϬϫϭή̳ήϳΎγΎΗϢϳήϴ̶̳ϣήψϧέΩ ΪϫΎη ϝήΘϨ̯ϩϭή̳ϥ΍ϮϨϋϪΑ΍έ̶̰ϳ ̶ϠϴμΤΗ ϩϭή̳ϥ΍ϮϨϋϪΑΎϬϫϭή̳Ϧϳ΍ί΍ϡ΍Ϊ̯ήϫΏΎΨΘϧ΍ΪηΎΑ Last ήΧ΁ϩϭή̳Ύϳ First ϝϭ΍ϩϭή̳ϥ΍ϮΗ̶ϣϩϭή̳ Control ϪϨϳΰ̳ DunnettϥϮϣί΁ϥΩή̯ϝΎόϓΎΑέΎ̯Ϧϳ΍̵΍ήΑΪϨ̶̯ϤϧΩΎΠϳ΍̵ήϴϴϐΗΞϳΎΘϧέΩήΧ΁Ύϳϝϭ΍ ήΑ̮ϴϠ̯ΎΑΪόΑϪϠΣήϣέΩϢϴϨ̶̯ϣΏΎΨΘϧ΍΍έϝήΘϨ̯ϩϭή̳ϥ΁̵ϭήΑϭέϊΑήϣέΩϭϩΪηϝΎόϓ Category ΍έ ήψϧ ΩέϮϣ ̵ΎϬϴΟϭήΧ Ϣϴϧ΍ϮΗ ̶ϣ Ok ̵ϭέ ήΑ ̮ϴϠ̯ ΎΑ ϭ Ϣϳϭέ ̶ϣ ̶ϠΒϗ ϩήΠϨ̡ ϪΑ Continue ̵ϭέ ϢϴϨ̯ϩΪϫΎθϣ

et

ˬ Games Howellˬ Dunnet’s T3 ˬ Tamhane’s T2̵ΎϬϧϮϣί΁ ΎϬδϧΎҨέ΍ϭ̵ήΑ΍ήΑΎϧ ϦϴҨΎ̡ΖϤδϗέΩ ΪϧϮη̶ϣϩΩΎϔΘγ΍Dunnet’s C

w

w w

.K

΍έΎϬδϧΎϳέ΍ϭ̵ήΑ΍ήΑϪ̯ Hoνήϓή̴ϳΩϥΎϴΑϪΑˬΪθϧϪΘϓήϳά̡ΎϬδϧΎϳέ΍ϭ̶Ϩ̴ϤϫνήϓϥϮ̩ϝΎΜϣϦϳ΍έΩ ϢϴϨ̶̯ϣϩΩΎϔΘγ΍̶ϨϴҨΎ̵̡ΎϬϧϮϣί΁ί΍Ζγ΍ϩΪηΩέΪϨ̶̯ϣΡήτϣ  έΩΪϨΘδϫ̵ϭΎδϣ˱ΎΒҨήϘΗΎҨ̵ϭΎδϣϪϧϮϤϧ̵Ύϫϩί΍Ϊϧ΍Ϫ̶̯όϗϮϣΎΗ FϩέΎϣ΁ˬ post hocΞҨΎΘϧέΩϪΘ̰ϧ ΞҨΎΘϧϭΖγ΍ϥ΍ϮΗΪϗΎϓ̵ϭΎδϣΎϧϪϧϮϤϧϩί΍Ϊϧ΍ΎΑ̶ϟϭΪϨ̶̯ϣΖϣϭΎϘϣΰϴϧ̵ϭΎδϣΎϧ̵ΎϫβϧΎҨέ΍ϭήΑ΍ήΑ ΪϫΩ̶ϣ΢ϴΤλΎϧ ΞҨΎΘϧϦҨ΍ϪΑϪ̯ϢϴΘδϫϝΩϭΩΎϣΪηΎΑ ˹˹̮̋ҨΩΰϧ sigˬFϩέΎϣ΁ΩέϮϣέΩϭΪϧΩϮΒϧήΑ΍ήΑΎϫβϧΎҨέ΍ϭή̳΍ ϮϫΩή̯ ΏΎΨΘϧ΍ ΰϴϧ ΍έ Welch,Brown-Forsythe ̵Ύϫ ϪϨҨΰ̳ option ϪϤ̳Ω έΩ ΍άϟ ˮϪϧ ΎҨ ϢϴϨ̰Α ΩΎϤΘϋ΍ ϢϴϨ̯΍ήΟ΍΍έANOVAϩέΎΑϭΩϭϩΩή̯ϑάΣ΍έΕή̡ήҨΩΎϘϣΪҨΎΑΪϴҨΎΗΕέϮλέΩϢϴϨ̶̯ϣ̶γέήΑ             40


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř   

Post Hoc Tests Multiple Comparisons Dependent Variable: Household income in thousands



Did not complete high school

m1

.002

-17.9587

-2.5781

3.01158

.000

-27.2233

-10.3447

Post-undergraduate degree-m5

-27.30373(*)

5.26659

.000

-42.1229

-12.4846

Did not complete high school-m1 Some college-m3 College degree-m4

6.34094 -3.92743 -12.44306(*)

2.33139 2.74970 3.01632

.064 .811 .000

-.1905 -11.6318 -20.8952

12.8724 3.7769 -3.9909

Post-undergraduate degree-m5

-20.96279(*)

5.26930

.001

-35.7894

-6.1362

10.26837(*)

2.74450

.002

2.5781

17.9587

3.92743

2.74970

.811

-3.7769

11.6318

m2  m5 m5  m3

-8.51563

3.34591

.105

-17.8907

.8594

Post-undergraduate degree-m5 Did not complete high school-m1

-17.03536(*)

5.46465

.019

-32.4016

-1.6691

18.78400(*)

3.01158

.000

10.3447

27.2233

High school degree-m2

12.44306(*)

3.01632

.000

3.9909

20.8952

Some college-m3

8.51563

3.34591

.105

-.8594

17.8907

Post-undergraduate degree-m5

-8.51973

5.60355

.749

-24.2704

7.2309

27.30373(*) 20.96279(*) 17.03536(*)

5.26659 5.26930 5.46465

.000 .001 .019

12.4846 6.1362 1.6691

42.1229 35.7894 32.4016

8.51973

5.60355

.749

-7.2309

24.2704

et

w

m5

.1905

2.74450

.K

Post-undergraduate degree

-12.8724

-18.78400(*)

w w

m4

.064

-10.26837(*)

College degree-m4

College degree

Lower Bound

College degree-m4

ab

m3

m2  m4

2.33139

Upper Bound

Some college-m3

High school degree-m2

m1  m 5

-6.34094

High school degree-m2

Did not complete high school-m1

Some college

m1  m 4

Sig.

lin

m2

m1  m 3

Std. Error

e.

High school degree

Mean Difference (IJ)

m

Tamhon's

(J) Level of education

co

(I) Level of education

95% Confidence Interval

Did not complete high school-m1 High school degree-m2 Some college-m3 College degree-m4

    m1  m 2 ϥϮϣί΁έΎϬ̩ήϫϪΠϴΘϧ m2ϭm1ϞΜϣΖγ΍ήΑ΍ήΑϢϫήγΖθ̡ϱΎϫϩϭή̳έΩΪϣ΁έΩϦϴ̴ϧΎϴϣ˺ m 2  m3 m3ϭm1ϞΜϣΖδϴϧήΑ΍ήΑϪϠλΎϓΎΑϱΎϬϫϭή̳έΩϲϟϭ m4  m3   m 4  m5       

41


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř       

High school degree

m2 Some college

2.33139

Some college-m3

-10.26837(*)

College degree-m4 Post-undergraduate degree-m5

Lower Bound

.064

-12.8721

.1902

2.74450

.002

-17.9585

-2.5782

-18.78400(*)

3.01158

.000

-27.2230

-10.3450

-27.30373(*)

5.26659

.000

-42.1194

-12.4881

Did not complete high school-m1 Some college-m3 College degree-m4

6.34094 -3.92743 -12.44306(*)

2.33139 2.74970 3.01632

.064 .810 .000

-.1902 -11.6317 -20.8950

12.8721 3.7768 -3.9911

Post-undergraduate degree-m5

-20.96279(*)

5.26930

.001

-35.7859

-6.1397

Did not complete high school-m1

10.26837(*)

High school degree-m2

2.74450

.002

2.5782

17.9585

3.92743

2.74970

.810

-3.7768

11.6317

-8.51563

3.34591

.104

-17.8905

.8593

-17.03536(*)

5.46465

.019

-32.3985

-1.6723

18.78400(*)

3.01158

.000

10.3450

27.2230

12.44306(*)

3.01632

.000

3.9911

20.8950

Some college-m3

8.51563

3.34591

.104

-.8593

17.8905

Post-undergraduate degree-m5

-8.51973

5.60355

.747

-24.2675

7.2280

27.30373(*) 20.96279(*) 17.03536(*)

5.26659 5.26930 5.46465

.000 .001 .019

12.4881 6.1397 1.6723

42.1194 35.7859 32.3985

8.51973

5.60355

.747

-7.2280

24.2675

High school degree-m2

m3

lin

College degree-m4

Post-undergraduate degree-m5 Did not complete high school-m1

ab

College degree

Post-undergraduate degree

Did not complete high school-m1

.K

m4

et

High school degree-m2

High school degree-m2 Some college-m3 College degree-m4

w w

m5

m

m1

-6.34094

Upper Bound

co

Did not complete high school

Sig.

                  

w

Dunnett T3

Std. Error

(J) Level of education

e.

(I) Level of education

95% Confidence Interval

Mean Difference (IJ)

42


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř       

(J) Level of education

Did not complete high school

High school degree-m2 Some college-m3

m1

College degree-m4 Post-undergraduate degree-m5 Did not complete high school-m1 Some college-m3 College degree-m4

m2

-12.7076

.025

-10.26837(*) -18.78400(*) -27.30373(*)

2.74450 3.01158 5.26659

-17.7647 -27.0099 -41.7380

-2.772 -10.558 -12.869

6.34094

2.33139

-.0257

12.707

-3.92743 -12.44306(*)

2.74970 3.01632

-11.4370 -20.6810

3.582 -4.205

-20.96279(*)

5.26930

-35.4039

-6.521

10.26837(*) 3.92743

2.74450 2.74970

2.7720 -3.5821

17.764 11.437

-8.51563

3.34591

-17.6548

.623

-17.03536(*)

5.46465

-32.0089

-2.061

18.78400(*)

3.01158

10.5581

27.009

12.44306(*) 8.51563 -8.51973

3.01632 3.34591 5.60355

4.2051 -.6236 -23.8715

20.681 17.654 6.832

Did not complete high school-m1

27.30373(*)

5.26659

12.8695

41.738

High school degree-m2

20.96279(*)

5.26930

6.5217

35.403

Some college-m3

17.03536(*)

5.46465

2.0618

32.008

8.51973

5.60355

-6.8320

23.871

Post-undergraduate degree-m5 Did not complete high school-m1 High school degree-m2 College degree-m4

e.

Some college

lin

m3

Post-undergraduate degree-m5

Did not complete high school-m1

ab

College degree

High school degree-m2

m4

.K

et

Some college-m3 Post-undergraduate degree-m5

Post-undergraduate degree

m5

Sig.

95% Confidence Interval Upper Upper Bound Bound

2.33139

m

High school degree

Std. Error

-6.34094

co

Dunnett C

(I) Level of education

Mean Difference (IJ)

w w

College degree-m4

* The mean difference is significant at the .05 level.

w

                  

43


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř      

m1

-6.34094

2.33139

.051

-12.7040

.0221

Some college-m3

-10.26837(*)

2.74450

.002

-17.7602

-2.7766

College degree-m4

-18.78400(*) -27.30373(*)

3.01158 5.26659

.000 .000

-27.0053 -41.7298

-10.5627 -12.8776

6.34094

2.33139

.051

-.0221

12.7040

-3.92743

2.74970

High school degree-m2

Post-undergraduate degree-m5 Did not complete high school-m1 Some college-m3

High school degree

m2 Some college

-11.4330

3.5782

-12.44306(*)

3.01632

.000

-20.6770

-4.2091

Post-undergraduate degree-m5

-20.96279(*)

5.26930

.001

-35.3961

-6.5295

Did not complete high school-m1 High school degree-m2 College degree-m4 Post-undergraduate degree-m5

10.26837(*) 3.92743 -8.51563 -17.03536(*)

2.74450 2.74970 3.34591 5.46465

.002 .609 .081 .016

2.7766 -3.5782 -17.6488 -31.9958

17.7602 11.4330 .6175 -2.0749

Did not complete high school-m1

18.78400(*)

3.01158

.000

10.5627

27.0053

High school degree-m2

12.44306(*)

3.01632

.000

4.2091

20.6770

Some college-m3

8.51563

3.34591

.081

-.6175

17.6488

Post-undergraduate degree-m5

-8.51973

5.60355

.550

-23.8554

6.8160

Did not complete high school-m1 High school degree-m2

27.30373(*) 20.96279(*)

5.26659 5.26930

.000 .001

12.8776 6.5295

41.7298 35.3961

Some college-m3

17.03536(*) 8.51973

5.46465 5.60355

.016 .550

2.0749 -6.8160

31.9958 23.8554

m4

et

Post-undergraduate degree

ab

College degree

lin

e.

m3

.609

College degree-m4

m

Did not complete high school

Sig.

(J) Level of education

.K

m5

College degree-m4

w w

                     

w

GamesHowell

95% Confidence Interval Upper Upper Bound Bound

Std. Error

co

(I) Level of education

Mean Difference (IJ)

44


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‫ﮐﺘﺎﺑﺨﺎﻧﻪ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

ŶŝįŹįŚƣō ŵŚŤſřSPSS1

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ƱřźƿřżƿřƺŤǀŤƀƳř 

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̵ήΘϣ΍έΎ̡ΎϧϥϮϣί΁ί΍ϩΩΎϔΘγ΍Ϟ΋ϻΩ ΪηΎΒϧϝΎϣήϧΎϫϩΩ΍ΩϊҨίϮΗ ΪηΎΑΩΎҨί̶̳ΪϨ̯΍ή̡ϑϼΘΧ΍ ΪηΎΑϢ̯ϪϧϮϤϧϩί΍Ϊϧ΍  ˮϢϴϨ̶̯ϤϧϩΩΎϔΘγ΍̵ήΘϣ΍έΎ̡ΎϧϥϮϣί΁ί΍ϡίϻςҨ΍ήηϦΘδϧ΍ΩϥϭΪΑ΍ΪΘΑ΍ί΍΍ή̩ Ζγ΍Ϣ̯ΪηΎΑϝΎϣήϧΎϫϩΩ΍ΩϊҨίϮΗϪ̶̯τҨ΍ήηέΩϥϮϣί΁ϦҨ΍ϥ΍ϮΗ΍ήҨί ήϔλνήϓΩέ̵΍ήΑϭΖγ΍̶ҨΎϋΩ΍νήϓˬϞΑΎϘϣνήϓΎϣ΍ΪϨ̶̯ϣκΨθϣ΍έΖϴόϤΟϊҨίϮΗήϔλνήϓ ϢϴϨ̯Ωέ΍έήϔλνήϓϪϧϮϤϧ̮ҨϪ΋΍έ΍ΎΑϪ̯Ζγ΍ϦҨ΍ϥϮϣί΁ϑΪϫϭΖγ΍ϩΪηΎϋΩ΍ ΩέήϔλϪϴοήϓϪ̯ϢҨ΍ϩΩ΍ΩϥΎθϧϪϧϮϤϧ̮ҨΎΑςϘϓϥϮ̩Ζγ΍΢ϴΤλϞΑΎϘϣνήϓϢϴҨϮ̶̳ϤϧΖϗϭ̨ϴϫ ΖδϴϧϞΑΎϘϣϪϴοήϓΕΎΒΛ΍ϞϴϟΩϦҨ΍̶ϟϭΪη ϥϮϣί΁̵΍ήΑΎϣ̶Ϡλ΍ϑΪϫΪҨ΂ϴϣΖγΪΑϪϧϮϤϧί΍ϭΖγ΍ήϔλνήϓΩέ̵΍ήΑέ΍ΪϘϣϦҨήΘϤ̯ p  value ̵΍ήΑΖγ΍ϩΪη ϡΎΠϧ΍̵΍ϩΩϮϬϴΑέΎ̯ϥϮϣί΁ϦҨ΍ΎΑϪϧή̳ϭ Ζγ΍ H 0 ϪϴοήϓΩέϭ p  value ϥΪη̮̩Ϯ̯ ΍έ κΧΎηϭ ϢҨήϴ̴Α ̶ϟϮϤόϣ ϪϧϮϤϧ ϢΒϨ̯ ΏΎΨΘϧ΍ ΖγέΩ ΍έ Ϫϴοήϓ ϝϭ΍ί΍ Ζγ΍ ήΘϬΑέΎ̰ϨҨ΍ ί΍ ̵ήϴ̳ϮϠΟ  ΪϨ̯ϞϤΤΗΪϧ΍ϮΘϴϣϖϘΤϣϪ̯Ζγ΍̶ҨΎτΧϢϫ  ϢϴϨ̯ΏΎΨΘϧ΍΍έϪϴϘΑϭϪϧϮϤϧϢΠΣΪόΑϢϴϨ̯Ν΍ήΨΘγ΍  ϥϮϣί΁ϥ΍ϮΗ ϢϴϤμΗ ϢҨ΍ ϩΩϮΑ ΎτΧ ϥϭΪΑ ΩϮη ςϠϏ Ϣϫ ϪϧϮϤϧ  ϥϮϣί΁ ςγϮΗ ϭ ΪηΎΑ ςϠϏ ήϔλ νήϓ Ύ˱όϗ΍ϭ ή̳΍ ϢҨ΍ϩΪηΎτΧέΎ̩ΩΕέϮμϨҨ΍ήϴϏέΩϪϧή̳ϭ ΖγέΩ     Ζγ΍ϩΩϮΑ΢ϴΤλΖϴόϤΟέΩήϔλνήϓϪ̶̯σήηϪΑϪϧϮϤϧςγϮΗήϔλνήϓΩέϝϭ΍ωϮϧ̵ΎτΧ   Ζγ΍ϩΩϮΑςϠϏΖϴόϤΟέΩήϔλνήϓϪ̶̯σήηϪΑϪϧϮϤϧςγϮΗήϔλνήϓϝϮΒϗϡϭΩωϮϧ̵ΎτΧ  1    ϥΎϨϴϤσ΍΢τγ  1    ϥϮϣί΁ϥ΍ϮΗ H 0 ΩέϡΪϋ H 0 Ωέ   ϝϭ΍ωϮϧ̵ΎτΧϭ̵έ΍Ω̶Ϩόϣ΢τγ  1        ϡϭΩωϮϧ̵ΎτΧ ΢ϴΤλ H 0   ςϠϏ H 0    1     ΪΘϓ΍̶ϣϥΎϨϴϤσ΍ϪϠλΎϓέΩ x ϥ΁̵ΎΗ̂˾Ϧϴ̴ϧΎϴϣϢҨήϴ̴ΑϪϧϮϤϧ˺˹˹ή̳΍̶ϨόҨ̂˾ϥΎϨϴϤσ΍΢τγ έΩ ϭ ˹˹˺ ̶̰ηΰ̡ έΩΪϨηΎΑ ϪΘη΍Ω ̵ήΘϤ̯ ϝϭ΍ ωϮϧ ̵ΎτΧ Ϫ̯ ϢϴϨ̯ ϩΩΎϔΘγ΍ ΪҨΎΑ ̶ҨΎϬϧϮϣί΁ ί΍  ˹˹˾ ̶ΘόϨλ ϥ΁ϥΩϮΑςϠϏρήηϪΑήϔλνήϓΩέ̶ϨόҨΪηΎΑϦҨήΘθϴΑϥ΁ 1   Ϫ̯Ζγ΍̶ϧϮϣί΁ϥϮϣί΁ϦҨήΗϥ΍ϮΗή̡


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

w

w w

.K

et

ab

lin

e.

co

m

             

                          



 46


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

  ϢϴϨ̶̯ϣϩΩΎϔΘγ΍ήҨίέϮΘγΩί΍SPSSέΩ̵ήΘϣ΍έΎ̡Ύϧ̵ΎϫϥϮϣί΁ϡΎΠϧ΍̵΍ήΑ Analyze – Nonparametric Tests – 1-sample k-s…

Analyze – Nonparametric Tests – 2 Independent samples

co

m

 Ϫ̯ΪϫΪϴϣϥΎθϧ  Kolmogorov- Smirnov Z  ϑϮϧήϴϤγ΍ ±ϑϭή̳ϮϤϟϮ̯εϭέί΍ϩΩΎϔΘγ΍ΎΑϥϮϣί΁ϦҨ΍ ̶ҨΎϤϧΎҨϥϮγ΍Ϯ̡ˬΖΧ΍ϮϨ̰ҨˬϝΎϣήϧˮΖγ΍̶ϋϮϧϪ̩ϩΪηΏΎΨΘϧ΍ήϴϐΘϣϊҨίϮΗ ϪΒΗέϑϼΘΧ΍ϭΩέϭ΁̶ϣΖγΪΑ΍έϪϧΎϴϣˬΪϨ̶̯ϣϩΩΎϔΘγ΍ϪϧΎϴϣϭΎϫϩΩ΍Ω̵ΪϨΑϪΒΗέί΍̵ήΘϣ΍έΎ̡ΎϧϥϮϣί΁ Most  έΩ ϥϮϣί΁ ϪΠϴΘϧ έΩ ΪηΎΑ ϪΘη΍Ω ϑϼΘΧ΍ ̶ϠϴΧ ή̳΍ ϭϭ ΪϨ̯ ̶ϣ ϪΒγΎΤϣ Ύϫ ϩΩ΍Ω ΎΑ ΍έ ϪϧΎϴϣ ϑϼΘΧ΍έ΍ΪϘϣϭΩϦҨήΘ̳έΰΑϖϠτϣέ΍ΪϘϣ Absoluteϥ΍ϮϨϋΎΑϑϼΘΧ΍έ΍ΪϘϣϦϴϟϭ΍ ΪδҨϮϧ̶ϣ Extreme  Ωέ΍ΩϡίϻΪηΎΑ̶ϣϥ΁ήҨίέΩϩΪη̟Ύ̩  ήҨί έϮΘγΩ ί΍ΪηΎΒϧ ϝΎϣήϧ ϊҨίϮΗΎҨ Ϣ̯ ϪϧϮϤϧ Ω΍ΪόΗ ̶Θϗϭ ϪϧϮϤϧ ϭΩ ϦϴΑ Ϧϴ̴ϧΎϴϣ ϪδҨΎϘϣ ϥϮϣί΁ ̵΍ήΑ ϢϴϨ̶̯ϣϩΩΎϔΘγ΍  

w

w w

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et

ab

lin

e.

 ϥϮϣί΁ω΍Ϯϧ΍  Kolmogorov- Smirnov Z ϑϮϧήϴϤγ΍±ϑϭή̳ϮϤϟϮ̯˺ έΩϪδҨΎϘϣ̵΍ήΑΰϴϧ΍έϊҨίϮΗϞ̰η̵ΰ̯ήϣκΧΎηήΑϩϭϼϋ ΪηΎΒϧϪϴΒηϪϧϮϤϧϭΩήϫϊҨίϮΗϪ̶̯ΗέϮλέΩΩήϴ̶̳ϣήψϧ Ζγ΍ήΘΒγΎϨϣϩέΎϣ΁ϦҨ΍   Man-Whitney U ̶ϨΘҨϭϦϣ˻ ΎΑ ϭ Ζγ΍ ΐγΎϨϣ ̵ΰ̯ήϣ ̵Ύϫ κΧΎη ϪδҨΎϘϣ ̵΍ήΑ ΪϨ̶̯ϣΏΎδΣ΍έϩέΎϣ΁ΎϫϪΒΗέϊϤΟί΍ϩΩΎϔΘγ΍   Moses extreme reactions αίϮϣ˼ ϩϭή̳ϦϴΑϪδҨΎϘϣϭΩέ΍ΩΪϴ̯ΎΗΎϬϧ΁ϪΒΗέϭ̶ҨΎϬΘϧ΍έ΍ΪϘϣήΑ ΪϫΩ̶ϣϡΎΠϧ΍̶ҨΎϬΘϧ΍έ΍ΪϘϣ̵ϭέ΍έζҨΎϣί΁ϭΪϫΎη   Wald wolfowitz runs Ϊϟ΍ϭ̊ ΪϨ̶̯ϣϪδҨΎϘϣ΍έϊҨίϮΗϞ̰ηϑϭή̳ϮϤϟϮ̯ΪϨϧΎϣϭΪϨ̶̯ϣϩΩΎϔΘγ΍ρϮϠΨϣ̵ΎϫϪΒΗέί΍  

ϢϴϨ̶̯ϣϩΩΎϔΘγ΍ήҨίέϮΘγΩί΍ϩϭή̳ΪϨ̩ϦϴΑϦϴ̴ϧΎϴϣϪδҨΎϘϣϥϮϣί΁̵΍ήΑ 

Analyze – Nonparametric Tests – K Independent samples  

Ύϫ ϪϧϮϤϧ ή̳΍ ̶ϟϭ ΪϧΩϮΑ ϞϘΘδϣ Ύϫ ϪϧϮϤϧ ΎΠϧ΁ έΩ ̶ϟϭ ΩϮΑ Ϣϫ ϝΎϣήϧ ΖϟΎΣ έΩ Ύϫ ϥϮϣί΁ ϦҨ΍ ϪΑΎθϣ ϞϘΘδϣ̵ΎϫϪϧϮϤϧήϔϧϭΩ̵΍ήΑϭέ΍ΩήϴΛΎΗζҨΎϣί΁˱ϼΜϣϢϴϨ̶̯ϣϩΩΎϔΘγ΍ήҨίΕ΍έϮΘγΩί΍ΪϨηΎΑςΒΗήϣ ΪϧήҨά̶̡ϣήϴΛΎΗϢϫί΍ϭΖγ΍ςΒΗήϣϭέ΍Ωί΍ΪόΑϭϞΒϗήϔϧ̮Ҩ̵΍ήΑΎϣ΍Ζγ΍   47


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř   ΎϫΖϔΟϦϴ̴ϧΎϴϣϪδҨΎϘϣϥϮϣί΁ 

Analyze – Nonparametric Tests –2 Related samples 

.K

et

ab

lin

e.

co

m

ϥϮϣί΁ω΍Ϯϧ΍ Ύϫ ϩΩ΍Ω Ϫ̯ Ζγ΍ ̶Θϗϭ Wilcoxon ˺ ϥϮϣί΁ ϦҨ΍ έΩ ϭ ϥΎϣί ϞΜϣ ΪϨηΎΑ ϪΘγϮϴ̡ Ύϫ ϪΒΗέ ϑϼΘΧ΍ ΖϬΟ ϭ Ύϫ ϪΒΗέ ϑϼΘΧ΍ ϦҨ΍ ί΍ ϩΩΎϔΘγ΍  ΩϮη ̶ϣ ϪΘϓή̳ ήψϧέΩ  Ζγ΍΢Οέ΍ϥϮϣί΁ Ζ˰ϬΟς˰Ϙϓ̶˰ϟϭΖγ΍̶ҨϻΎΑϞΜϣSign˻ ΩϮη̶ϣϪΘϓή̳ήψϧέΩΎϫϪΒΗέϑϼΘΧ΍ Ζ˰γέΩΕέϮ˰λϪΑΎϫϩΩ΍Ωή̳΍McNemar˼ Ϧ˰˰Ҩ΍ί΍Ζ˰˰γ΍ή˰˰ΘϬΑ ˺ϭ˹ ΪϨΘδ˰˰ϫς˰˰ϠϏϭ ϥϮϣί΁Ϧϳ΍ί΍ϻϮϤόϣΪϴϨϛϩΩΎϔΘγ΍ϥϮϣί΁ ΩϮηϲϣϪΘϓή̳έΎϛϪΑέΎΘϓέϚϳί΍ΪόΑϭϞΒϗϪδϳΎϘϣϱ΍ήΑ  ϩϭή˰̳ˬΎ˰ϫϩΩ΍Ω  Ϊ˰ϴϨϛϩΩΎϔΘ˰γ΍εϭέϦ˰ϳ΍ί΍ McNemarεϭέί΍ϲ˰ϤϴϤόΗ ΪϨηΎΑϲϳΎΗΪϨ̩ϱΎϫϩΩ΍Ωή̳΍ ΪϨΘδϫϱΪϨΑ    ϲϳΎΗΪϨ̩ϱΎϫϪϧϮϤϧϦϴ̴ϧΎϴϣϪδϳΎϘϣϥϮϣί΁   Analyze – Nonparametric Tests – K Related samples 

ϥϮϣί΁ω΍Ϯϧ΍

w w



w

ϱήΘϣ΍έΎ̡ ϱΎϬηϭέ ΪϨϧΎϣ ϦϣΪϳήϓ ϥϮϣί΁ ˺ ΍έ ήϴϐΘϣ ΪϨ̩ ϊϳίϮΗ  Ϧϴ̴ϧΎϴϣ ϪδϳΎϘϣ ϱέ΍ά̳ ϪΒΗέ ί΍ ϩΩΎϔΘγ΍ ΎΑ ΪϨϛϲϣ ϥϮϣί΁ ϥϮϣί΁ ΎϫϪΒΗέ ϪδϳΎϘϣ ϭ ϩϭή̳ ήϫ ϱ΍ήΑ ΩϮηϲϣϡΎΠϧ΍ ϖϓ΍ϮΗ ί΍ ϩΩΎϔΘγ΍ ΎΑ ϝ΍ΪϨϛ ϥϮϣί΁ ˻ ϡΎΠϧ΍ ΍έ ϥϮϣί΁ Ϧϳ΍ ϱ΍ϪΒΗέ ϲ̴ΘδΒϤϫ

ΪϫΩϲϣ  ϦϣΪϳήϓ ϥϮϣί΁ ΪϨϧΎϣ ΰϴϧ ϥ΍ήϛΎϛ ϥϮϣί΁ ˼  ˺ϭ˹  ϲϳΎΗϭΩ ϱΎϫϩΩ΍Ω ΎΑ ϲϟϭ ϩΩήϛ ϞϤϋ ϪδϳΎϘϣ ϱ΍ήΑ ΪϫΩϲϣ ϡΎΠϧ΍ ΍έ ΕΎΒγΎΤϣ Ζγ΍ΐγΎϨϣςϠϏϭΖγέΩ       

48


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř  

m

ΖΒΛϪϴϧΎΛΐδΣήΑΪϧέ΍ά̴ΑΩϮΧ̵ΎΟέΩ΍έΐγΎϨϣϪότϗΎΗΪϨϨ̰ϴϣϑήλϪ̪Α ˺˹Ϫ̯΍έ̶ϧΎϣίϝΎΜϣ ϢҨ΍ϩΩή̯ ΪϴҨΎϤϧϪΒγΎΤϣ΍έ˯Ύϴη΍κϴΨθΗϥΎϣί̵΍ήΑ̶ϔϴλϮΗ̵ΎϫέΎϣ΁˺ ŦƬŨƯƵźƿřŵƖŝźƯ  ̶̳ΪϨ̯΍ή̡ϭΰ̯ήϤΗ̵ΎϫέΎϴόϣ  ççåèååçëå ΪϴϨ̯ϢγέϥΎϣίϪγϦҨ΍̵΍ήΑ̵΍ϪδҨΎϘϣέ΍ΩϮϤϧ̮Ҩ˻ çêåçîåèåå ˮΖγ΍ϝΎϣήϧϊҨίϮΗ̵΍έ΍ΩϞ̰ηϪγήϫ̵΍ήΑκϴΨθΗϥΎϣίΎҨ΁˼ ϥΎϣί Ϧϴ̴ϧΎϴϣ ϦϴΑ ΪϴϫΩ ϥΎθϧ Boxplot έ΍ΩϮϤϧ ί΍ ϩΩΎϔΘγ΍ ΎΑ ˽ çëåçíåçîå Ωέ΍ΩΩϮΟϭ̵έ΍Ω̶ϨόϣϑϼΘΧ΍ϩϭή̳ϪγϦҨ΍έΩκϴΨθΗ çèåèéåæîå ϭ̵ήΘϣ΍έΎ̡ ΪϴϨ̯ϥϮϣί΁΍έϻΎΑνήϓ̵έΎϣ΁̵ΎϫϥϮϣί΁ί΍ϩΩΎϔΘγ΍ΎΑ̋ æîåèååçêå  ̵ήΘϣ΍έΎ̡Ύϧ  ççåçìåçéå  çêåèçåçìå   çíåçîåçëå         

co

çìåèéåçêå

ab

lin

e.

çéåèååçêå

w

w w

.K

et

Ώ΍ϮΟ Ωέ΍ΩΩϮΟϭϩ΍έϭΩΎϫήϴϐΘϣΖϴϴόΗϭΎϫϩΩ΍ΩΖΒΛ̵΍ήΑ έΩϪ̯ϥΎϣί̶ϣϭΩϭ ΚϠΜϣˬϩήҨ΍ΩˬϊΑήϣ ϞϣΎηϞ̰ηήϴϐΘϣ̶ϟϭ΍ϢҨήϴ̴ΑήψϧέΩήϴϐΘϣϭΩϝϭ΍ΖϟΎΣ ϢҨέ΍ΩΩέϮϣ˼˹ΕέϮμϨҨ΍ ϢҨέ΍ΩΩέϮϣ˺˹ΖϟΎΣϦҨ΍έΩϪ̯ΚϠΜϣˬϩήҨ΍ΩˬϊΑήϣϢҨήϴ̴ΑήψϧέΩήϴϐΘϣϪγϡϭΩΖϟΎΣ  Ωή̯ϩΩΎϔΘγ΍ϥ΍ϮΗ̶ϣϩ΍έϭΩί΍̶ϔϴλϮΗ̵ΎϫέΎϣ΁̵΍ήΑ˺ Analyze – Descriptive statistic- descriptive Analyze – Descriptive statistic- Explore objects ΖϤδϗ Ϫγ έΩ ΍έ ϥΎϣί ήϴϐΘϣ ϥ΍ϮΘϴϣ Explore ί΍ ϩΩΎϔΘγ΍ ΎΑ Ϫ̯ Ζγ΍ ϦҨ΍ ϩ΍έϭΩ ϦҨ΍ ϕήϓ ΩϮϤϧϩΪϫΎθϣϪϧΎ̳΍ΪΟ  Descriptive Statistics N

Range

Minimum

Maximum

Mean

Std. Deviation

Variance

square

10

110.00

190.00

300.00

256.0000

29.88868

893.333

triangle

10

90.00

190.00

280.00

241.0000

26.85351

721.111

circle

10

70.00

270.00

340.00

303.0000

23.59378

556.667

Valid N (listwise)

10

49


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

:̵΍ϪδҨΎϘϣέ΍ΩϮϤϧ˻ Graph – Legacy Dialogs- Bar – simple - Define –other statistic- variable ……time Category Axis……objects 400.00

200.00

m

Mean time

300.00

e.

co

100.00

0.00 circle

triangle

ab

lin

square

et

Graph – Legacy Dialogs - Histogram-display normal curve-

.K

2

1

0

w

3

Frequency

Frequency

3

variable…...time Panel…..….objects

3

w w

4

ϝΎϣήϧϊҨίϮΗΩϮΟϭ ˼

2

2

1

1

0 3  S

2

0 150.00

200.00

250.00

300.00

350.00

1

0 150.00

50

200.00

250.00

300.00

350.00


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

Analyze – Nonparametric Tests – 1-sample k-s…

 ̵ΪϨΑϩϭή̳ϥϭΪΑ ϝϭ΍ΖϟΎΣϒϟ΍

One-Sample Kolmogorov-Smirnov Test  time N

29

Normal Parameters(a,b) Most Extreme Differences

Mean

267.2414

Std. Deviation

37.88107

Absolute

.090

Positive

.090

Negative

-.083

Kolmogorov-Smirnov Z

.485

Asymp. Sig. (2-tailed)

.973

a Test distribution is Normal.b Calculated from data.

co

Sig = p  value  0.973 ! 0.05 =

m

ΩϮη̶ϤϧΩέ ϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ  H 0 νήϓˬ 

 ̵ΪϨΑϩϭή̳ΎΑ ϡϭΩΖϟΎΣΏ

e.

Data- split files – variable ….objects Analyze – Nonparametric Tests – 1-sample k-s… One-Sample Kolmogorov-Smirnov Test

lin

time

N

10

Mean Normal Parameters(a,b)

Std. Deviation Absolute Positive

31.62278

et

Most Extreme Differences

256.6667

ab

objects square

circle

N

Normal Parameters(a,b)

w

Most Extreme Differences

Triangle

.194 .125 -.194 .655 .784 10

w w

Asymp. Sig. (2-tailed)

.K

Negative Kolmogorov-Smirnov Z

Mean

303.0000

Std. Deviation

23.59378

Absolute

.251

Positive

.251

Negative

-.142

Kolmogorov-Smirnov Z

.792

Asymp. Sig. (2-tailed)

.556

N Normal Parameters(a,b) Most Extreme Differences

ϪΠϴΘϧ ί΍ p  value ϥΩϮΑήΘ̳έΰΑϪΑϪΟϮΗΎΑ˺

10 Mean

241.0000

Std. Deviation

26.85351

Absolute

.131

Positive

.083

Negative

-.131

Kolmogorov-Smirnov Z

.415

Asymp. Sig. (2-tailed)

.995

a Test distribution is Normal. b Calculated from data.

51


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř 

 ϊΑήϣϩϭή̳έΩ ΩϮη̶ϤϧΩέ ϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ  H 0 νήϓˬ  Sig = p  value  0.784 ! 0.05 =

ϪΠϴΘϧ ί΍ p  value ϥΩϮΑήΘ̳έΰΑϪΑϪΟϮΗΎΑ˺

 ϩήҨ΍Ωϩϭή̳έΩ ΩϮη̶ϤϧΩέ ϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ  H 0 νήϓˬ  ί΍ p  value ϥΩϮΑήΘ̳έΰΑϪΑϪΟϮΗΎΑ˻ Sig = p  value  0.556 ! 0.05 =

 ΚϠΜϣϩϭή̳έΩ ΩϮη̶ϤϧΩέ ϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ  H 0 νήϓˬ  ί΍ p  value ϥΩϮΑήΘ̳έΰΑϪΑϪΟϮΗΎΑ˼ Sig = p  value  0.995 ! 0.05 =

m

   Boxplotέ΍ΩϮϤϧΎΑϩϭή̳ϪγέΩϥΎϣίϦϴ̴ϧΎϴϣϑϼΘΧ΍˽ ΪηΎΒϧsplit filesΖϟΎΣέΩϪΘ̰ϧ Graph – Legacy Dialogs Boxplots - variable……...time

e.

co

category…….objects

ab

lin

350.00

.K

250.00

200.00

w w

time

et

300.00

w

5

150.00 square

circle

                  

triangle

 ̶ϓΎ̯ έ΍ΩϮϤϧ ΎΑ ΕϭΎπϗ ̶ϟϭ ΪϫΪϴϣ ϥΎθϧ ΕϭΎϔΘϣ ϝΎ̰η΍ ϩϭή̳ Ϫγ έΩ ϥΎϣί ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ ϪΠϴΘϧ Ωή̯ϥϮϣί΁ΪҨΎΑΖδϴϧ        52


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř  ϥϮϣί΁˾  ̵ήΘϣ΍έΎ̡Ύϧϝϭ΍ΖϟΎΣ

H 0 : m1  m 2  m 3

 Square ϊΑήϣϩϭή̳έΩϥΎϣί ήϴϐΘϣϦϴ̴ϧΎϴϣ m1 H 1 : m1  m 2  m 3 circle ϩήҨ΍Ωϩϭή̳έΩΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m2 Triangle ΚϠΜϣϩϭή̳έΩΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m3  Analyze – Nonparametric Tests – K Independent samples- test variable………..... time 

Grouping variable….…object(1,3)

Kruskal-Wallis Test Ranks N

Mean Rank 10

13.20

Chi-Square

circle

10

24.40

df

triangle

10

8.90

Total

30

time 16.683

m

objects square

Asymp. Sig.

co

time

Test Statistics(a,b)

2

.000

e.

a Kruskal Wallis Test b Grouping Variable: objects

Analyze -

̵ήΘϣ΍έΎ̡ϡϭΩΖϟΎΣ

ab

Sig = p  value  0.000  0.05 =

lin

ϪΠϴΘϧ ΩϮη̶ϣΩέ ϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ  H 0 νήϓˬ  ί΍ p  value ϥΩϮΑήΘ̰̩Ϯ̯ϪΑϪΟϮΗΎΑ˺

Compare Means - One-Way Anova ANOVA

19510.000

Total

40230.000

2

Mean Square 10360.000

27

722.593

.K

Within Groups

df

F 14.337

Sig. .000

w w

Between Groups

Sum of Squares 20720.000

et

time

29

Levene Statistic .067

df1

w

Test of Homogeneity of Variances time

df2

2

Sig. 27

.936

ϪΠϴΘϧ ΩϮη̶ϣΩέ ϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ  H 0 νήϓˬ  ί΍ p  value ϥΩϮΑήΘ̰̩Ϯ̯ϪΑϪΟϮΗΎΑ˺ Sig = p  value  0.000  0.05 =

Ζγ΍Ύϴη΍̵ΪϨΑϩϭή̳ί΍̶ηΎϧϭΖγ΍έ΍Ω̶ϨόϣϑϼΘΧ΍˻ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ  Test of Homogeneity of Variances ϝϭΪΟ έΩ ΎϬδϧΎҨέ΍ϭ ϪδҨΎϘϣ ϥϮϟ ϥϮϣί΁ ί΍ ϩΪϣ΁ ΖγΪΑ ΞҨΎΘϧ ˼  p  value  0.936 = Sig!  ϭΪϧέ΍Ϊϧ̵έ΍Ω̶ϨόϣϑϼΘΧ΍Ύϴη΍ήϴϐΘϣϩϭή̳˼έΩϥΎϣίήϴϐΘϣ̵ΎϫβϧΎҨέ΍ϭ ̵ήΑ΍ήΑνήϓΎΑΎϬϨϴ̴ϧΎϴϣΎϬϓϼΘΧ΍ϝΎΒϧΩϪΑΪҨΎΑϪ̯ΪϫΪϴϣϥΎθϧ ANOVAϝϭΪΟέΩ βϧΎҨέ΍ϭΰϴϟΎϧ΁ί΍ϩΪϣ΁ΖγΪΑΞҨΎΘϧ̊ ϢϴϨ̯ϪδҨΎϘϣϢϫΎΑϭΩϪΑϭΩPost HOCϪϨҨΰ̳ί΍ϩΩΎϔΘγ΍ΎΑϭϢϴηΎΑΎϬδϧΎҨέ΍ϭ

   53


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 ϢϴϨ̶̯ϣϩΩΎϔΘγ΍postHoc έϮΘγΩί΍ϑϼΘΧ΍ΖϠϋϦΘϓΎҨ̵΍ήΑ   Multiple Comparisons Dependent Variable: time Tukey HSD

circle triangle

(J) objects circle triangle square triangle square circle

Std. Error 12.02159 12.02159 12.02159 12.02159 12.02159 12.02159

Sig. .002 .391 .002 .000 .391 .000

co

*. The mean difference is significant at the .05 level.

95% Confidence Interval Lower Bound Upper Bound -75.8065 -16.1935 -13.8065 45.8065 16.1935 75.8065 32.1935 91.8065 -45.8065 13.8065 -91.8065 -32.1935

m

(I) objects square

Mean Difference (I-J) -46.00000* 16.00000 46.00000* 62.00000* -16.00000 -62.00000*

e.

.

ϭΩϪΑϭΩϪδҨΎϘϣϥϮϣί΁ϪΠϴΘϧ ΖδϴϧήΑ΍ήΑϩήҨ΍ΩϭϊΑήϣ̵Ύϫϩϭή̳έΩϥΎϣίϦϴ̴ϧΎϴϣ˺ ΖδϴϧήΑ΍ήΑϩήҨ΍ΩϭΚϠΜϣ̵Ύϫϩϭή̳έΩϥΎϣίϦϴ̴ϧΎϴϣ˻ Ζγ΍ήΑ΍ήΑΚϠΜϣϭϊΑήϣ̵Ύϫϩϭή̳έΩςϘϓϥΎϣίϦϴ̴ϧΎϴϣ˼ ΪηΎΑ̶ϣϩήҨ΍Ωϩϭή̳ϑϼΘΧ΍ΖϠϋ̊  Square ϊΑήϣϩϭή̳έΩϥΎϣί ήϴϐΘϣϦϴ̴ϧΎϴϣ m1

lin

m1  m3

ab

m1  m2

.K

et

m2  m3

circle ϩήҨ΍Ωϩϭή̳έΩΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m2 Triangle ΚϠΜϣϩϭή̳έΩΪϣ΁έΩήϴϐΘϣϦϴ̴ϧΎϴϣ m3

w

w w

     

54


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

íìæçŒ‹Ĉ¸m   2 Chi-squareƱƺƯŻō

w

w w

.K

et

ab

lin

e.

co

m

ΩϭήϴϣέΎ̯ϪΑέϮψϨϣϭΩ̵΍ήΑϥϮϣί΁ϦҨ΍  ˮϪϧΎҨΪϨ̶̯ϣ̵ϭήϴ̶̡λΎΧϊҨίϮΗί΍έ΍ΩϮϤϧ ϊҨίϮΗϖΑΎτΗ˺  ϪΘδδ̵̳ΎϫήϴϐΘϣ ̶ϓΩΎμΗήϴϐΘϣϭΩϝϼϘΘγ΍˻  ϩΪηϩΪϫΎθϣήҨΩΎϘϣ Oi έΎψΘϧ΍ΩέϮϣήҨΩΎϘϣ Ei 2 ( O E )  i  έΎψΘϧ΍ΩέϮϣήҨΩΎϘϣϪϧϮϤϧ̵Ϯγί΍ϩΪηϩΪϫΎθϣϑϼΘΧ΍ςγϮΘϣ

2   i  Ei  H 0 : ΪϨ̶̯ϣΪϴҨΎΗ΍έΖϴόϤΟήΘϣ΍έΎ̡ϪϧϮϤϧ H 1 : ΪϨ̶̯ϤϧΪϴҨΎΗ΍έΖϴόϤΟήΘϣ΍έΎ̡ϪϧϮϤϧ  2 ΖϴόϤΟϪϧϮϤϧ  έ΍ΪϘϣϥΩϮΑϢ̯ϭΩϮη̶ϣϢ̯ΕΎϓϼΘΧ΍β̡Ζη΍Ω΍έΖϴόϤΟϊҨίϮΗϥΎϤϫϪϧϮϤϧή̳΍ ΩϮη̶ϣϪδҨΎϘϣ̵έΎϣ΁ϝϭΪΟΎΑϥ΁έ΍ΪϘϣϪΘΒϟ΍ΪϨ̶̯ϣΪϴҨΎΗ΍έ  εί΍ήΑϲϳϮϜϴϧ±ϝϼϘΘγ΍ϥϮϣί΁ ‡ ΎϬοήϓ ‡ ϲϓΩΎμΗϪϧϮϤϧ ± Ωέ΍ΪϧΖϴϤϫ΍ΎϫϩΩ΍ΩϊϳίϮΗΎϳϞϜη ± ˺Ϟϗ΍ΪΣϩϭή̳ήϫέΩϲϧ΍ϭ΍ήϓϞϗ΍ΪΣ ± ΪϨηΎΑϪΘη΍Ω˾ί΍ζϴΑϲϧ΍ϭ΍ήϓΎϫϩϭή̳˻˹ ±   ΪϨϛϲϣϪδϳΎϘϣέΎψΘϧ΍ΩέϮϣήϳΩΎϘϣΎΑ΍έϩΪηϩΪ  ϫΎθϣήϳΩΎϘϣ˻ϲΧϩέΎϣ΁ί΍ϩΩΎϔΘγ΍ΎΑϥϮϣί΁Ϧϳ΍ ϥϮϣί΁ϪΑϲγήΘγΩεϭέ - Descriptive Statistics - Crosstabs... ... Analyze  ΪϴϨϛΏΎΨΘϧ΍΍έchi squareϪϨϳΰ̳ϭΪϴϧΰΑ΍έstatisticϪϤϛΩΎϫήϴϐΘϣ ϦϴϴόΗί΍ΪόΑ  Analyze – nonparametric – chi-squareΎҨ            55


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 ˾Ζγ΍ήΑ΍ήΑ̵ή̴ҨΩΎΑΎϫϩΩ΍Ωί΍̮ҨήϫϩΪϫΎθϣϝΎϤΘΣ΍ϭΖγ΍ΖΧ΍ϮϨ̰ҨϊҨίϮΗΪϴϨ̯νήϓϝΎΜϣ ϢҨέ΍Ω̵ίΎΑΏΎΒγ΍ϩϭή̳ ˮϪϧΎҨΖγ΍ϥΎδ̰ҨΎϬϨҨ΍ϥΪҨήΧϪΑΎϫϪ̪ΑϞҨΎϤΗϢϴϧ΍ΪΑϢϴϫ΍ϮΧ̶ϣϝϭ΍ϝ΍ϮΌγ ΩϮηϡΎΠϧ΍ϝϭ΍ϩϭή̳ϪγϦϴΑϪδҨΎϘϣϢϴϫ΍ϮΧ̶ϣ±ϡϭΩϝ΍ϮΌγ ˮΖγ΍ϡϮγϭΩϪΑϡϮγ̮ҨήΗϮϴ̢ϣΎ̵̯ίΎΑϭ̲ϨϔΗϦϴΑΪҨήΧΖΒδϧΎҨ΁ϡϮγϝ΍ϮΌγ  ̵ίΎΑΏΎΒγ΍ωϮϧ ϩΪη̵έ΍ΪҨήΧέ΍ΪϘϣ

̲ϨϔΗ

ήΗϮϴ̢ϣΎ̵̯ίΎΑ

̮γϭήϋ

ϦϴηΎϣ

ϪΧή̩Ϫγ

˽˾

˻˹

˺˾

˺˾

˾

co

m

 ϝϭ΍Ώ΍ϮΟ ϢϴϨ̶̯ϣϒҨήόΗnumϭTypeoftoyϡΎϧϪΑήϴϐΘϣωϮϧϭΩ˺ ϢϴϫΩ̶ϣΪ̵̯ίΎΑΏΎΒγ΍ωϮϧ̋έΩ΍έϝϭ΍ήϴϐΘϣ˻ ϢϴϫΩ̶ϣϥίϭnumήϴϐΘϣϪΑ˼ Analyze – nonparametric – chi-square– Test variable ………Typeoftoy ̊ Ζγ΍ϩΪη̊̋ΩΪϋChi-Square(a)έ΍ΪϘϣ̋

gun

45

20.0

25.0

camputer

20

20.0

.0

doll

15

20.0

-5.0

car

15

20.0

-5.0

5

20.0

-15.0

Total

2  

(Oi  Ei ) Ei

252  0 2  52  52  152 900   45 20 20

et

bycicle

Residual

lin

Expected N

ab

Observed N

e.

typeoftoy

100

.K

Ζγ΍0.05ί΍ήΘ̰̩Ϯ̯ϭAsymp. Sig.=0.000 έ΍ΪϘϣ̌ Ζδϴϧ̶̰ҨΖϴόϤΟήΘϣ΍έΎ̡ΎΑϪϧϮϤϧϊҨίϮΗϭΪϨ̶̯ϣΩέήϔλνήϓϪϧϮϤϧ΍άϟ

Chi-Square(a) df

w w

Test Statistics typeoftoy 45.000

w

4

Asymp. Sig.

.000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 20.0.

ϡϭΩΏ΍ϮΟ  ̵΍ήΑˬUsed specified rangeϪϨҨΰ̳ˬExpected Range ΖϤδϗέΩChi-SquareϥϮϣί΁ϩήΠϨ̡έΩ Ωήϴ̳ΕέϮλ˼ΎΗ˺ϩϭή̳ϦϴΑϪδҨΎϘϣΎΗϩΩή̯ΏΎΨΘϧ΍΍έupper=3̵΍ήΑϭlower=1 ϢϴϨ̯ϥΎθΒΗήϣϝϭ΍̶ΘδҨΎΑΩϮΒϧΐΗήϣΎϣήτϧΩέϮϣ̵ΎϬϫϭή̳ή̳΍ Ω΍ΩϡΎΠϧ΍΍έϥϮϣί΁ΪόΑΩή̯ΏΎΨΘϧ΍ϝϭ΍select casesέϮΘγΩΎΑ΍έΎϫϩΩ΍Ωϥ΍ϮΘϴϣ ΪηΩέϪϧϮϤϧςγϮΗήϔλνήϓϥΎϨ̪ϤϫϥϮϣί΁ϦҨ΍ϡΎΠϧ΍ΎΑ     56


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř  

Frequencies typeoftoy Observed N

Expected N

1

gun

Category

45

26.7

2

camputer

20

26.7

-6.7

3

doll

15

26.7

-11.7

Total

Residual 18.3

80

Test Statistics typeoftoy 19.375

m

ChiSquare(a) df

2

Asymp. Sig.

e.

co

.000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 26.7.

.K

et

ab

lin

ϡϮγΏ΍ϮΟ ̌̋ ήΗϮϴ̢ϣΎ̯ ̵ίΎΑ ϭ ̲ϨϔΗ ΪҨήΧ ωϮϤΠϣ Ϫ̯ ϢϴϨ̯ ̶ϣ ΏΎδΣ έϮτϨҨ΍ ΍έ ϡϮγ ϭΩ  ϪΑ ϡϮγ ̮Ҩ ΖΒδϧ ϢϴϨ̶̯ϣϢϴδϘΗϡϮγϭΩϪΑϡϮγ̮ҨΖΒδϧϪΑ΍έΩΪϋϦҨ΍Ζγ΍ 1 65*  21.7 ˬExpected Range ΖϤδϗέΩChi-SquareϥϮϣί΁ϩήΠϨ̡έΩ 3 ̵΍ήΑϭlower=1̵΍ήΑˬUsed specified rangeϪϨҨΰ̳ 2 Ωήϴ̳ΕέϮλ˻,˺ϩϭή̳ϦϴΑϪδҨΎϘϣΎΗϩΩή̯ΏΎΨΘϧ΍΍έupper=2 65*  43.3 3 Ω΍Ϊϋ΍Expected Value ΖϤδϗέΩChi-SquareϥϮϣί΁ϩήΠϨ̡έΩ ϢϴϨ̶̯ϣΩέ΍ϭ΍έ̊˼˼ϭ˻˺̀ 

w w

Frequencies

Observed N

Expected N

45

21.7

23.3

20

43.3

-23.3

Category 1

gun

2

camputer

Total

w

typeoftoy

Residual

65

Ζγ΍0.05ί΍ήΘ̳έΰΑϭ Asymp. Sig.=0.655 έ΍ΪϘϣ ΪϨ̶̯ϤϧΩέήϔλνήϓϪϧϮϤϧ΍άϟ Test Statistics

Chi-Square(a) df

typeoftoy .200 1

Asymp. Sig.

.655 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 21.7.



57


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

Ζγ΍ϩΪηϩΩέϭ΁ϡΎϧΖΒΛέΎϣ΁ϩΪϜθϧ΍ΩϚϴϜϔΗϪΑϥΎΘγήϬηϭϥΎΘγ΍ΰϛήϣϱΎϬϫΎ̴θϧ΍ΩέΩϝΎΜϣ ̶ϨόҨ ϪϧΎҨΩέ΍ΩΖΧ΍ϮϨ̰ҨϊҨίϮΗ̶ϟϮΒϗΖΒδϧ ˮϪϧΎҨΩέ΍ΩήϴΛΎΗϮΠθϧ΍ΩΏΎΨΘϧ΍έΩϩΪ̰θϧ΍ΩωϮϧΎҨ΁ ϩΪ̰θϧ΍Ω ϦϴΑ ̶ϟϮΒϗ ΖΒδϧ ΎҨ΁ ˮΪϧϮη ̶ϣ ϢϴδϘΗ ΎϬϫΎ̴θϧ΍Ω ϦϴΑ ΖΒδϧ ̮Ҩ ϪΑ ̶ϟϮΒϗ Ω΍ΪόΗ ΏΎΨΘϧ΍ ϥΎϳϮΠθϧ΍Ω ςγϮΗ ˻˹ ήϨϫ ϭ ́˹ ϲγΪϨϬϣ ϲϨόϳ  ˮΖγ΍ ˻˹ ϪΑ ́˹ ήϨϫ ϭ ̶γΪϨϬϣ ΩϮηϲϣ   ϲγΪϨϬϣ

ήϨϫ

ΩΎμΘϗ΍

ήϳΎγ

ϥΎΘγ΍ΰϛήϣ

˺˿

˺˽

˺˼

˺˼

ϥΎΘγήϬη

˺˽

˿

˺˹

́

Width

Decimal

Label

faculty

Numeric

8

-

-

place

Numeric

8

-

-

2

frequency

Numeric

8

-

1=eng 2=art 3=eco 4=etc 1=center 2=outer -

lin

1

Value



ΎϫήϴϐΘϣΏΎΨΘϧ΍

Missing column Align

Measure

-

10

left

Scale

-

10

left

Scale

-

10

left

Scale

e.

Type

ab

Name



co

m

ϩΪ̰θϧ΍ΩωϮϧ

-

w w

.K

et

 ϢϴϫΩ̶ϣϥίϭfrequencyήϴϐΘϣϪΑ ϢϴϨ̶̯ϣϩΩΎϔΘγ΍ chi-squareϥϮϣί΁ί΍ΖΧ΍ϮϨ̰ҨϊҨίϮΗϥϮϣί΁̵΍ήΑ  Analyze – nonparametric – chi-square– Test variable ……… faculty

w

ΪϨ̶̯ϣϥϮϣί΁΍έϥ΁ϊҨίϮΗϭΪϧί̶ϣϊϤΟ΍έΎϫ̶ϟϮΒϗΩ΍ΪόΗεΩϮΟΩϮη̶ϣϩΪϫΎθϣϪ̯έϮτϧΎϤϫ ̶ϣΪϴҨΎΗ΍έΖϴόϤΟήΘϣ΍έΎ̡ϪϧϮϤϧΖϔ̳ϥ΍ϮΗ̶ϣϭΪϨ̶̯ϤϧΩέ΍έήϔλνήϓϪϧϮϤϧϪ̯Ζγ΍ϦҨ΍ϪΠϴΘϧ ΪϨ̯ facaulty

Observed N 30

Expected N 23.5

Residual 6.5

art

20

23.5

-3.5

ecoomic

23

23.5

-.5

etc

21

23.5

-2.5

Total

94

engineering

Test Statistics facaulty ChiSquare(a) df Asymp. Sig.

2.596 3 .458

a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 23.5.

58


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

50*0.8  40 50*0.2  10

ƱřźƿřżƿřƺŤǀŤƀƳř

 ˻˹ϪΆ˹ΖΒδϧϥϮϣί΁̵΍ήΑ Chi-SquareϥϮϣί΁ϩήΠϨ̡έΩ  1,2 ˬExpected Range ΖϤδϗέΩ Ω΍Ϊϋ΍Expected Value ΖϤδϗέΩ ϢϴϨ̯Ωέ΍ϭ΍έ˹˻˹́ΩϮΧ̶ϨόҨΪλέΩϪΑ΍έΎϬϧ΁ΖΒδϧΎҨϢϴϨ̶̯ϣΩέ΍ϭ΍έ˺˹ϭ̊˹ Frequencies facaulty

Category

2

Expected N

Residual

engineering

30

40.0

-10.0

art

20

10.0

10.0

Total

50

m

1

Observed N

facaulty 12.500

df

e.

Chi-Square(a)

co

Test Statistics

1

Asymp. Sig.

.K

et

ab

lin

.000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 10.0.

ΪηΩέϪϧϮϤϧςγϮΗνήϓϦҨ΍Ϫ̰ϨҨ΍ϪΠϴΘϧ    įřƶƬưūƹŵŚƿBinomialƱƺƯŻō

w

w w

̵΍ϪϠϤΟϭΩ̶ϓΩΎμΗήϴϐΘϣϒҨήόΗ  ΪηΎΑϪΘη΍Ω̶ΗϭΎϔΘϣΞҨΎΘϧΩϮηέ΍ή̰ΗΎϫέΎΑϥΎδ̰ҨςҨ΍ήηέΩή̳΍Ϫ̯Ζγ΍̶θҨΎϣί΁̶ϓΩΎμΗζҨΎϣί΁ ˮϪϧΎҨΩϮη̶ϣΪϴҨΎΗϪϧϮϤϧςγϮΗΖϴόϤΟ̵ϭέί΍ϩΪηϩΩίαΪΣP  Ω΍ΪΧέϡΪϋΎϳΩ΍ΪΧέϪϛϲϳΎΠϧ΁ί΍ΩϮηϲϣϩΪϴΠϨγΪϣ΁ζϴ̡ϚϳΩ΍ΪΧέϝΎϤΘΣ΍ΖΒδϧϥϮϣί΁Ϧϳ΍έΩ  ΩϮηϲϣϩΩΎϔΘγ΍ϥϮϣί΁ϱ΍ήΑϊϳίϮΗϦϳ΍ί΍   Binomial ϱ΍ϪϠϤΟϭΩϊϳίϮΗΪϣ΁ζϴ̡  ϥϮϣί΁Ϧϳ΍ϪΑϲγήΘγΩεϭέ Analyze - Nonparametric Tests - Binomial...  ˮΖγ΍˾˹ϪΑ˾˹ΎϬϧΎΘγήϬηϭϥΎΘγ΍ΰ̯ήϣέΩϥΎҨϮΠθϧ΍Ω̶ϟϮΒϗΖΒδϧΎҨ΁ϝΎΜϣ  1 H 0 : p   2  1 H 1 : p   2  Analyze - Nonparametric Tests - Binomial... variable…………………....place Test propertion………….0.5

59


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

Binomial Test

place

Group 1

Category center

Group 2

outer

56

Observed Prop. .60

38

.40

94

1.00

N

Total

Asymp. Sig. (2-tailed) .079(a)

Test Prop. .50

a Based on Z Approximation. ΪϨ̶̯ϤϧΩέ΍έήϔλνήϓϪϧϮϤϧϪΠϴΘϧ

lin

e.

co

m

 Chi-SquareϥϮϣί΁ί΍ϩΩΎϔΘγ΍ή̴ҨΩϩ΍έ  1,2 ˬExpected Range ΖϤδϗέΩ ϢϴϨ̶̯ϣΩέ΍ϭ΍έ 0.5ϭ0.5 Ω΍Ϊϋ΍Expected Value ΖϤδϗέΩ Ζγ΍ήΗΩϭΪΤϣ̵΍ϪϠϤΟϭΩ̶ϟϭΩέ΍Ω̵ήΘθϴΑΖϴϣϮϤϋChi-SquareϥϮϣί΁ϪΘ̰ϧ  ̀˹ΖΒδϧϪΑήҨΎγϭΩΎμΘϗ΍̵ΎϬϫϭή̳ΎΑήϨϫϭ̶γΪϨϬϣ̵ΎϬϫϭή̳ϥΎҨϮΠθϧ΍Ω̶ϟϮΒϗΖΒδϧΎҨ΁ϝΎΜϣ ˮϪϧΎҨΖδϫ˼˹ϪΑ H 0 : p ΪϨ̶̯ϣΏΎΨΘϧ΍΍έήϨϫϭ̶γΪϨϬϣϮΠθϧ΍Ω H 1 : q  ΪϨ̶̯ϣΏΎΨΘϧ΍΍έήҨΎγϭΩΎμΘϗ΍ϮΠθϧ΍Ω  Analyze - Nonparametric Tests - Binomial... variable…………………....place Test propertion………….0.7 Cutpoint ………………….2

ab



ΪϨ̯ΏΎδΣή̴ҨΩϩϭή̳̮Ҩ΍έ˻ί΍ΪόΑϭϩϭή̳̮Ҩ΍έ˻ϭ˺ϩϭή̶̳ϨόҨΖγ΍cutpoint=2̶Θϗϭ Binomial Test

Group 1

<= 2

Group 2

>2

Total

Observed Prop.

et

facaulty

N

50

.5

44

.5

.K

Category

Test Prop.

Asymp. Sig. (1-tailed)

.7

.000(a,b)

w w

94 1.0 a Alternative hypothesis states that the proportion of cases in the first group < .7. b Based on Z Approximation.

w

ΪϨ̶̯ϣΩέ΍έήϔλνήϓϪϧϮϤϧϪ̰ϨҨ΍ϪΠϴΘϧ  RunsƱƺƯŻō

 ΩϮηϲϣϥϮϣί΁Ύϫ   ϩΩ΍ΩϥΩϮΑϲϓΩΎμΗϥΎ Ϝϣ΍ΎϫϩΩ΍ΩϱΪϨΑϪΒΗέϭϥϮϣί΁Ϧϳ΍ί΍ϩΩΎϔΘγ΍ΎΑ ί΍ήΗϦϴҨΎ̶̡̰ҨϭϦϴ̴ϧΎϴϣ̵ϻΎΑ̶̰Ҩ̶ϟ΍ϮΘϣϩΩ΍ΩϭΩϪ̯ΩϮη̶ϣϞλΎΣ̶ϧΎϣίΎϫϩΩ΍ΩϥΩϮΑ̶ϓΩΎμΗ ΪηΎΑϥ΁ ϥϮϣί΁Ϧϳ΍ϪΑϲγήΘγΩεϭέ Analyze – Nonparametric Tests – Runs Ϧϳ΍ϪϛΪϴϨϛϥϮϣί΁spssΕΎϧΎϜϣ΍ΎΑϭΪϴϨϛΩΎΠϳ΍ϲϓΩΎμΗΩΪϋ˺˹˹ Rand()ϊΑΎΗ Ϟδϛ΍ί΍ϩΩΎϔΘγ΍ΎΑ ΪϨΘδϫϲϓΩΎμΗΩ΍Ϊϋ΍ έΩ data  ϲΗΎϋϼσ΍ ΪϨγ ϥΩήϛ ίΎΑ ϡΎ̴Ϩϫ έΩ Ζγ΍ ϲϓΎϛ ΪϴϨϛ έϭήϣ ΍έ Ϟδϛ΍ ί΍ ΕΎϋϼσ΍ Ωϭέϭ ϩϮΤϧ ΪϴϨϛΏΎΨΘϧ΍΍έexcelϪϨϳΰ̳file typeΖϤδϗ  60


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

ϢϴϨϴΒΑϢϴϫ΍ϮΧ̶ϣΪϧ΍ϪΘϓή̳ϩί΍Ϊϧ΍΍έϩΪηϪΘΧΎγ̵ΎϫϪϟϮϟήτϗςγϮΘϣ̵έΎ̯ΖϔϴηέΎϬ̩έΩϝΎΜϣ ΪҨΎΑΎϫϪϟϮϟήτϗΩέ΍ΩϥΪηϩήΒϴϟΎ̯ϪΑίΎϴϧϩΎ̴ΘγΩΎҨΪϨΘδϫ̶ϓΩΎμΗϥΎθϨϴ̴ϧΎϴϣϪΑΖΒδϧΩ΍Ϊϋ΍ϦҨ΍ ΪηΎΑ̊ ΎϫήτϗςγϮΘϣ 3.8 4.2 3.3 4.5  ˺Ζϔϴη ˻Ζϔϴη

˼Ζϔϴη ̊Ζϔϴη  İĮŤƀŞưƷƱƺƯŻō 

ab

lin

e.

co

m

̶ϣ ϪΒγΎΤϣ ΍έ ϥ΁ ΐҨήο ϭ Ωί΍Ωή̡ ̶ϣ ήϴϐΘϣ ΪϨ̩ ΎҨ ϭΩ ϦϴΑ ρΎΒΗέ΍ ̶γέήΑ ϪΑ ̶̴ΘδΒϤϫ ϪτΑ΍έ ήϴϐΘϣΕ΍ήϴϴϐΗΎΑήϴϐΘϣ̮ҨΕ΍ήϴϴϐΗή̳΍ΪηΎΑ̶ϔϨϣΎҨΖΒΜϣΖγ΍Ϧ̰ϤϣΎϫήϴϐΘϣϦϴΑ̶̴ΘδΒϤϫΪϨ̯ ϩ΍ήϤϫ̵ή̴ҨΩζϫΎ̯ΎΑ̶̰ҨζϫΎ̯β̰όϟΎΑΎҨ̵ή̴ҨΩζҨ΍ΰϓ΍ΎΑ̶̰ҨζҨ΍ΰϓ΍ϭΪηΎΑϩ΍ήϤϫ̵ή̴ҨΩ +1ΎΗ0ί΍Ϫ̯ΪҨήΧ̵΍ήΑΎοΎϘΗϭΪϣ΁έΩζҨ΍ΰϓ΍ϞΜϣΖγ΍ΖΒΜϣΎϬϧ΁ϦϴΑ̶̴ΘδΒϤϫϢϴҨϮ̶̳ϣΩϮθΑ Ωέ΍ΩϥΎγϮϧ ϦϴΑ̶̴ΘδΒϤϫϪ̯ΩϮη̶ϣϪΘϔ̳ΩϮηϩ΍ήϤϫ̵ή̴ҨΩήϴϐΘϣζϫΎ̯ΎΑήϴϐΘϣ̮ҨζҨ΍ΰϓ΍ϭήϴϴϐΗή̳΍ ΪϨ̶̯ϣήϴϴϐΗ-1ΎΗ0ί΍ϭΖγ΍̶ϔϨϣΎϬϧ΁ Ζγ΍ήϔλΎϬϧ΁ϦϴΑ̶̴ΘδΒϤϫΐҨήοΪηΎΑϪΘη΍ΪϧΩϮΟϭ̵΍ϪτΑ΍έήϴϐΘϣϭΩϦϴΑή̳΍ Ωέ΍ΩΩϮΟϭήϴϐΘϣϭΩϦϴΑϲτΧϪτΑ΍έΪηΎΑ̮ϳΩΰϧ-1Ύϳ1 ϪΑϲ̴ΘδΒϤϫΐϳήοή̳΍  ̶̴ΘδΒϤϫΐҨήο

w w

.K

et

Ζγ΍ήϴϐΘϣϭΩϦϴΑρΎΒΗέ΍ΩϮΟϭϩΪϨϫΩϥΎθϧΐϳήοϦϳ΍ Ζγ΍ήΘϜϳΩΰϧ˺Ύϳϭ˺ϪΑΐϳήοέ΍ΪϘϣΪηΎΑΪϳΪηήϴϐΘϣϭΩρΎΒΗέ΍Ϫ̩ήϫ ΩϮηϲϣϚϳΩΰ  ϧήϔλϪΑΐϳήοέ΍ΪϘϣήϴϐΘϣϭΩϦϴΑρΎΒΗέ΍ζϫΎϛΎΑ ΩϮΑΪϫ΍ϮΧήϔλήΑ΍ήΑϲ̴ΘδΒϤϫΐϳήοέ΍ΪϘϣΪϨηΎΑϞϘΘδϣήϴϐΘϣϭΩή̳΍ έΩΖϓή̳ϪΠϴΘϧ΍έϝϼϘΘγ΍ϥ΍ϮΗϲϣΪηΎΑήϔλήΑ΍ήΑϲ̴ΘδΒϤϫΐϳήοή̳΍ΪϨηΎΑϝΎϣήϧήϴϐΘϣϭΩή̳΍  ήϴΧΕέϮλϦϳ΍ήϴϏ

‡ ‡ ‡ ‡ ‡

w

  ̶̴ΘδΒϤϫΐҨήοϪΒγΎΤϣ

ΩϮηϲϣϩΩΎϔΘγ΍  Covarianceέ΍ΪϘϣί΍ϥϮγήϴ̡ϲ̴ΘδΒϤϫΐϳήοϪΒγΎΤϣϱ΍ήΑ αΎϴϘϣϭΪΣ΍ϭΎΑϪϛϲϳΎΠϧ΁ί΍ϲϟϭΖγ΍ΐγΎϨϣρΎΒΗέ΍ϥ΍ΰϴϣϱήϴ̳ϩί΍Ϊϧ΍ϱ΍ήΑ  Covarianceέ΍ΪϘϣ ϥϭΪΑΐҨήο̮ϳί΍̶̴ΘδΒϤϫϥ΍ΰϴϣϪδҨΎϘϣϱ΍ήΑΪϳΎΑΪϳ΁ϲϣΖγΪΑϩΪηϱήϴ̳   ϩί΍Ϊϧ΍ϪμΨθϣ ΩήϛϩΩΎϔΘγ΍ ΪλέΩ ΪΣ΍ϭ Ωέ΍Ω΍έΖϴλΎΧϦϳ΍̶̴ΘδΒϤϫΐϳήοΎϳCorrelation ΩϮηϲϣϩΩΎϔΘγ΍ϱ΍   ϪτϘϧέ΍ΩϮϤϧί΍ήϴϐΘϣϭΩρΎΒΗέ΍ϩΪϫΎθϣϱ΍ήΑϻϮϤόϣ p ( y / x )  p ( y ) ̶ϨόҨ̶ϓΩΎμΗήϴϐΘϣϭΩ Independent ϝϼϘΘγ΍ ϢϫΎΑϥΎθϧΩ΍ΪҨϭέϪ̰ϠΑϥίϭϭΪϗϞΜϣΪϨηΎΑϢϫϝϮϠόϣϭΖϠϋϪΘδΒϤϫ˱ϼϣΎ̯ήϴϐΘϣϭΩΖδϴϧϡίϻ Ωέ΍ΩρΎΒΗέ΍

‡ ‡

‡ ‡ ‡ ‡ 

61


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

̶̴ΘδΒϤϫςΧέ΍ΩϮϤϧ       

co

m

  

e.

ϥϮγήϴ̶̴̡ΘδΒϤϫΐҨήοϪΒγΎΤϣ

ab

lin

E ( xy )  E ( x ).E ( y ) Co var iance  n 1

.K

et

E ( xy )  E ( x).E ( y ) E ( xy )  E ( x).E ( y ) n 1  Corrolation    Var ( x) Var ( y ) Var ( x)Var ( y ) n 1 n 1

     

w w

   0 ϭ E ( xy )  E ( x ) E ( y ) ϩΎ̴ϧ΁ΪηΎΑϪΘη΍ΩΩϮΟϭ̶̴ΘδΒϤϫή̳΍

w

Analyze –Corrolate – Bivariate … ΪϴϨϛϲσ΍έήϳίήϴδϣέϮΘγΩϦϳ΍ϪΑϲγήΘγΩϱ΍ήΑ Ϊϳέ΍ΩΝΎϴΘΣ΍ϱΩΪϋήϴϐΘϣϭΩϪΑϞϗ΍ΪΣέϮΘγΩϦϳ΍ϱ΍ήΟ΍ϱ΍ήΑ  ϲ̴ΘδΒϤϫΐϳήοω΍Ϯϧ΍ ί΍Ϊ˰ҨΎΑ̶Ϥ˰γ΍ϭ̵΍ϪΒΗέˬ̵ΩΪϋ̵ΎϬγΎϴϘϣί΍̮Ҩήϫ̵΍ήΑ Ωή̯ϩΩΎϔΘγ΍ϥΎηΩϮΧϪΒγΎΤϣεϭέ  Covariance Correlationϲ̴ΘδΒϤϫ ϱήΘϣ΍έΎ̡ϲ̴ΘδΒϤϫΐϳήο  Pearson ±ϥϮγήϴ̡ϲ̴ΘδΒϤϫΐϳήο ϲτΧϲ̴ΘδΒϤϫ

ϝΎ˰ϣήϧϊ˰ϳίϮΗϱ΍έ΍ΩϱΩΪ˰ϋήϴϐΘϣϭΩή˰ϫϪϛΖγ΍Ϧϳ΍ήΑνήϓ ΪϨΘδϫϩήϴϐΘϣϭΩ

62


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř ϱήΘϣ΍έΎ̡Ύϧϲ̴ΘδΒϤϫΐϳήο

 SpearmanϦϣήϴ̢γ΍ϲ̴ΘδΒϤϫΐϳήο ϱ΍ϪΒΗέϲ̴ΘδΒϤϫ    Kendall’s Tau_b±ϝ΍ΪϨϛϲ̴ΘδΒϤϫΐϳήο ϲΒϴΗήΗϲ̴ΘδΒϤϫ

H0 :   0 H1 :   0

  ϲ̴ΘδΒϤϫΐϳήοϥϮϣί΁  ϲ̴ΘδΒϤϫΐϳήοέ΍ΪϘϣϱ΍ήΑϪϓήσϭΩϥϮϣί΁Ϛϳtwo tailϪϨϳΰ̳ΏΎΨΘϧ΍ΎΑΩϮηϲϣϡΎΠϧ΍

H1 :   0

H1 :   0

co

H0 :   0

e.

H0 :   0

m

One  ϪϓήτϜϳϥϮϣί΁ί΍Ζγ΍ήΘϬΑΖγ΍ήψϧέΩΰϴϧϥ΁ΖϬΟϲ̴ΘδΒϤϫΐϳήοέ΍ΪϘϣήΑϩϭϼϋή̳΍ΩϮηϩΩΎϔΘγ΍ Tail ϪϓήτϜϳϥϮϣί΁ ϪϓήτϜϳϥϮϣί΁

 

lin

ΩϮηϲϣϩΩ΍ΩϲΟϭήΧέΩϲ̴ΘδΒϤϫΐϳήοέ΍ΪϘϣϥΩϮΑέ΍ΩΎϨόϣϦϴϴόΗϥΎϜϣ΍   flag… ϪϨϳΰ̳ΏΎΨΘϧ΍ΎΑ-

w w

.K

et

ab

Ϧϴ̴ϧΎϴϣ ϲϔϴλϮΗέΎϣ΁ζϳΎϤϧϥΎϜϣ΍ optionΏΎΨΘϧ΍ΎΑΏήπϣ ϭ βϧΎϳέ΍Ϯϛ έ΍ΪϘϣ ζϳΎϤϧ ϭ Ωέ΍ΪϧΎΘγ΍ ϑ΍ήΤϧ΍ ϭ ΕέϮλ ϥΎϤϫ ΕϼοΎϔΗ ΐϳήο Ωέ΍Ω ΩϮΟϭ ΕϼοΎϔΗ Ζγ΍βϧΎϳέ΍ϮϛϪΒγΎΤϣ ϱ΍ήΑϪΒγΎΤϣExclude Cases pairwiseϪϨϳΰ̳ΏΎΨΘϧ΍ΎΑ ή̴ϳΩ έΩ ϩΪθϤ̳ ϩΩ΍Ω ϦΘϓή̳ ήψϧ έΩ ϥϭΪΑ Ύϫ ΖϔΟ ΩϮηϲϣϪΒγΎΤϣΩέϮϣήϫϱΎϫήϴϐΘϣ

w

Ϛϳ ΩϮΟϭ ΎΑ Exclude cases listwise ϪϨϳΰ̳ ΏΎΨΘϧ΍ ΎΑ  ΪηΪϫ΍ϮΧϪΘϓή̳ϩΪϳΩΎϧΩέϮϣϞϛΩέ΍ΩϲγέήΑέΩϩΪηϢ̳έ΍ΪϘϣϪϛήϴϐΘϣ

ϱ΍ήΑϱΪόΑϭΩβϳήΗΎϣϚϳΐϴΗήΗϦϳ΍ϪΑΩέ΍ΩΩϮΟϭϲγέήΑϦϳ΍έΩΰϴϧήϴϐΘϣϭΩί΍ζϴΑΏΎΨΘϧ΍ϥΎϜϣ΍ ΪηΪϫ΍ϮΧΩΎΠϳ΍ΎϫήϴϐΘϣΖϔΟΖϔΟϲ̴ΘδΒϤϫΐϳήοέ΍ΪϘϣϪΒγΎΤϣϭϲγέήΑ ϢϴϨ̶̯ϣϩΩΎϔΘγ΍ήҨίέϮΘγΩί΍̶̴ΘδΒϤϫέ΍ΩϮϤϧϢγέ̵΍ήΑ Graph – Legacy dialogs – scatter plot– simpleΎҨ Matrix

63


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

íìç걺f¨ÅĈ¸m ϭΩ ϝϼϘΘγ΍ ϥϮϣί΁ ̵΍ήΑ Ϣϫ ϭ ϊҨίϮΗ εί΍ήΑ ̵΍ήΑ ϢϬΑ Chi-square ϥϮϣί΁ Ϫ̯ Ϊη ϪΘϔ̳ ϞΒϗ ϪδϠΟ έΩ ήϴϐΘϣϭΩϦϴΑϪτΑ΍έΩϮΟϭϪϧΎθϧΩϮΑ   0 ή̳΍ (x,y)ΐΗήϣΝϭί̵΍ήΑ̶ϨόҨΩϭέ̶ϣέΎ̰Α̶ϓΩΎμΗήϴϐΘϣ ΪηΎΑ̶ϣ ϭ ϥΎΘγ΍ ΰ̯ήϣ ί΍ ϒϠΘΨϣ ̵Ύϫ ϩΪ̰θϧ΍Ω έΩ ϥΎҨϮΠθϧ΍Ω ̶ϟϮΒϗ ΖΒδϧ ϪδҨΎϘϣ ϞΒϗ ϪδϠΟ ϝΎΜϣ έΩ ̶Ϥγ΍ ήϴϐΘϣ ϭΩ ήϫ Ϫ̯ ϒϠΘΨϣ ̵Ύϫ ϪΘηέ ϭ ϩΪ̰θϧ΍Ω ϞΤϣ Ϫ̯ Ϣϴϧ΍ΪΑ Ϣϴϫ΍ϮΧ ̶ϣ  ΎϬϧΎΘγήϬη ˮϪϧΎҨΪϨϠϘΘδϣΪϨΘδϫ

H0 :   0 H1 :   0



m

Analyze –Descriptive Statistics- Crosstabs- statistic……chi-square Chi-Square Tests

outer 14

center 30

art

14

6

20

ecoomic

13

10

23

etc

13

8

21

56

38

94

engineering

Total

Value

e.

center 16

Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases

ab

facaulty

Total

lin

place

co

facaulty * place Crosstabulation Count

Asymp. Sig. (2-sided)

df

1.524(a) 1.551

3 3

.677 .671

.153

1

.696

94

et

a 0 cells (.0%) have expected count less than 5. The minimum expected count is 8.09.

w

w w

.K

ή̳΍ϭΪϧ΍ϩΩή̶̯ΜϨΧ΍έή̴ҨΪϤϫΕΎϓϼΘΧ΍̶ϨόҨΖγ΍̮̩Ϯ̯ Pearson Chi-Square=1.524έ΍ΪϘϣ̶Θϗϭ Ζγ΍ήϴϐΘϣϭΩϦϴΑϪτΑ΍έϩΪϨϫΩϥΎθϧΪηΎΑ̱έΰΑ  Ωέ ΍έ ήϔλ νήϓ ϪϧϮϤϧ Ϫ̯ ΪϫΩ ̶ϣ ϥΎθϧ ϭ ϩΩϮΑ 0.05 ί΍ ήΘ̳έΰΑ Ϫ̯ Asymp. Sig. (2-sided)=0.677 ήϴϐΘϣϭΩϦϴΑϝϼϘΘγ΍ΩϮΟϭ̶ϨόҨΪϨ̶̯Ϥϧ  Ζγ΍ΡήηϦҨ΍ϪΑϪΒγΎΤϣϝϮϣήϓˮΖγ΍ϩΪϣ΁ΖγΩϪΑΎΠ̯ί΍1.524ΩΪϋϪ̯Ϣϴϧ΍ΪΑϢϴϫ΍ϮΧ̶ϣ  outerϥϮΘγϭcenterϥϮΘγ ϩΪηϩΪϫΎθϣήҨΩΎϘϣ Oi έΎψΘϧ΍ΩέϮϣήҨΩΎϘϣ Ei (Oi  Ei ) 2  2    έΎψΘϧ΍ΩέϮϣήҨΩΎϘϣϪϧϮϤϧ̵Ϯγί΍ϩΪηϩΪϫΎθϣϑϼΘΧ΍ςγϮΘϣ

Ei   outer ϥΎΘγήϬηϥϮΘγ̵΍ήΑ center ϥΎΘγ΍ΰ̯ήϣϥϮΘγ̵΍ήΑ  30*38 30*56 E1   12.13 E1   17.87  94 94  20*38 20*56  E2  E2   8.09  11.91 94 94  23*38 23*56  E3  E3   9.30  13.7 94 94   21*38 21*56 E4  E4   8.49  12.51 94 94 64


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

  ̶ϣϩΩΎϔΘγ΍ήҨίϝϮϣήϓί΍ΕϮΘγϭήτγ̵Ύϫ̶ϧ΍ϭ΍ήϓωϮϤΠϣί΍έΎψΘϧ΍ΩέϮϣ̶ϧ΍ϭ΍ήϓϪΒγΎΤϣ̵΍ήΑ ΩϮη ήτγέΩϩΪηϩΪϫΎθϣ̶ϧ΍ϭ΍ήϓϊϤΟ A A* B Ei   ϥϮΘγέΩϩΪηϩΪϫΎθϣ̵Ύϫ̶ϧ΍ϭ΍ήϓϊϤΟ B N ϩΪηϩΪϫΎθϣ̶ϧ΍ϭ΍ήϓϊϤΟ N ϡ΍iέΎψΘϧ΍ΩέϮϣ̶ϧ΍ϭ΍ήϓ Ei

Ei

 (O  E )  i

Ei

i

(16  17.87) 2 (14  11.91) 2 (13  13.7) 2 (13  12.51) 2     0.62 17.87 11.91 13.7 12.51

(14  12.13) 2 (6  8.09)2 (10  9.30)2 (8  8.49) 2     0.91 12.13 8.09 9.30 8.49

2

ϥΎΘγ΍ΰ̯ήϣ

ϥΎΘγήϬη

e.

co

2

(Oi  Ei ) 2

m

2  

lin

Pearson Chi  Square=0.62+0.91=1.524

et

ab

ΪϨ̯ΏΎδΣ΍έΎϫ EI ̶ϨόҨέΎψΘϧ΍ΩέϮϣήҨΩΎϘϣΪϧ΍ϮΗ̶ϣSPSSέ΍ΰϓ΍ϡήϧ  Analyze –Descriptive Statistics- Crosstabs- statistic……chi-square Cell ……..….observed  , expected  

center

Count

Expected Count outer

Count

w

Expected Count Total

engineering 16

w w

place

.K

place * facaulty Crosstabulation

Count

Expected Count

facaulty art

Total

14

ecoomic 13

13

engineering 56

17.9

11.9

13.7

12.5

56.0

14

6

10

8

38

12.1

8.1

9.3

8.5

38.0

30

20

23

21

94

30.0

20.0

23.0

21.0

94.0

65

etc


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

ΪϴϨ̯ϲγέήΑ΍έ ed,carcat ϦϴηΎϣωϮϧϭΕϼϴμΤΗϥ΍ΰϴϣήϴϐΘϣϭΩϝϼϘΘγ΍demoϞϳΎϓέΩϝΎΜϣ  H0 :   0  H1 :   0   Analyze –Descriptive Statistics- Crosstabs- statistic……chi-square  correlation  Level of education * Primary vehicle price category Crosstabulation Count Total

Primary vehicle price category Standard

Luxury

Economy

Did not complete high school

483

481

426

High school degree

587

689

660

Some college

390

485

College degree

312

508

co

Economy

69

112

1841

2275

Total

6400

df

m 1355

178

359

2284

6400

Asymp. Sig. (2-sided) .000 .000

et

8 8 1

.000

.K

75.149

1360

535

w w

Likelihood Ratio Linear-by-Linear Association N of Valid Cases

Value 85.689(a) 85.520

485

ab

Chi-Square Tests

Pearson Chi-Square

1936

e.

Post-undergraduate degree

1390

lin

Level of education

w

a 0 cells (.0%) have expected count less than 5. The minimum expected count is 103.27.

Symmetric Measures

Interval by Interval

Pearson's R

Ordinal by Ordinal

Spearman Correlation

N of Valid Cases

Value .108

Asymp. Std. Error(a) .012

Approx. T(b) 8.720

Approx. Sig. .000(c)

.105

.012

8.453

.000(c)

6400

a Not assuming the null hypothesis. b Using the asymptotic standard error assuming the null hypothesis. c Based on normal approximation.

 ϥ΍ΰϴϣΩϮη̶ϣΩέήϴϐΘϣϭΩϝϼϘΘγ΍ϭΪϨ̶̯ϣΩέ΍έήϔλνήϓϪϧϮϤϧβ̡ p  value  0 ϪΠϴΘϧ ϥΎθϧ΍έ0.105ΩΪϋϪ̯Ζγ΍ΐγΎϨϣϦϣήϴ̢γ΍ΪϨΘδϫϲϤγ΍ήϴϐΘϣϭΩϥϮ̩ϝϭΪΟέΩϲ̴ΘδΒϤϫ ΪϫΩϲϣ  66


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř  ̶ҨΰΟ̶̴ΘδΒϤϫΐҨήο

w w

.K

et

ab

lin

e.

co

m

ήϴϐΘϣϚϳϝήΘϨϛΕέϮλέΩήϴϐΘϣϭΩϦϴΑϲτΧϲ̴ΘδΒϤϫϥ΍ΰϴϣζΠϨγϥΎϜϣ΍ΐϳήοϦϳ΍ί΍ϩΩΎϔΘγ΍ΎΑ Ωέ΍ΩΩϮΟϭ ϥ΁ήϴΛΎΗϑάΣΎϳ ϡϮγ  ϲ̴ΘδΒϤϫΐϳήοϪΒγΎΤϣΎΑΪϨηΎΑϪΘη΍ΩϪτΑ΍έΰϴϧϱή̴ϳΩήϴϐΘϣϪτγ΍ϭϪΑήϴϐΘϣϭΩΖγ΍ϦϜϤϣ ΪηΪϫ΍ϮΧϪΒγΎΤϣϡϮγήϴϐΘϣήΛ΍ϑάΣΎΑήϴϐΘϣϭΩϦϴΑϲτΧϪτΑ΍έϥ΍ΰϴϣϲ΋ΰΟ Ζγ΍ήϳίΕέϮλϪΑϪΒγΎΤϣεϭέ rAB  rAC rBC rABC  (1  r 2 AC )(1  r 2 BC )   Ζγ΍ϱΩΪϋήϴϐΘϣ˼Ϟϗ΍ΪΣϪΑΝΎϴΘΣ΍έϮΘγΩϦϳ΍ϡΎΠϧ΍ϱ΍ήΑ ΪϴϨϛϲσ΍έήϳίήϴδϣέϮΘγΩϦϳ΍ϪΑϲγήΘγΩϱ΍ήΑ Analyze - Correlate - Partial...  ϩΩΎγ ϲ̴ΘδΒϤϫ ΐϳήο ϝϭΪΟ ζϳΎϤϧ ϭ ϲϔϴλϮΗ ϱΎϫ ϩέΎϣ΁ ζϳΎϤϧ ϥΎϜϣ΍ Option ϪϨϳΰ̳ ΏΎΨΘϧ΍ ΎΑ Ωέ΍ΩΩϮΟϭΰϴϧ  Zero-order…

w

 

67


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

ή˰˰ψϧέΩ΍έ age,income,car Ϧϴ˰˰ηΎϣΪ˰˰ϳήΧϪ˰˰ϨϳΰϫϭΪ˰˰ϣ΁έΩˬϦ˰˰γή˰˰ϴϐΘϣϪ˰˰γdemoϞ˰˰ϳΎϓέΩϝΎ˰˰Μϣ Ϊϴθ̰ΑήϴϐΘϣϪγϦϳ΍ϱ΍ήΑϲδϳήΗΎϣέϮσϪΑ΍έscatterplotέ΍ΩϮϤϧ΍ΪΘΑ΍Ϊϳήϴ̴Α Grafh - scatterplot - matrix ϢϴϨ̯ϲϣΏΎδΣ΍έήϴϐΘϣϪγϦϴΑϲ̴ΘδΒϤϫΐϳήο Analyze - correlate- bivariate … ή˰ϴϐΘϣϭΩϦϴ˰ΑϪ˰˰τΑ΍έ΍ΩΪ˰˰ΠϣϭϪ˰Θϓή̳Ζ˰ΑΎΛ΍έincomeή˰ϴϐΘϣϲ˰ϳΰΟϲ̴Θδ˰˰ΒϤϫΐϳή˰οί΍ϩΩΎϔΘ˰γ΍Ύ˰Α Ϣϳέϭ΁ϲϣΖγΪΑ΍έage,car Correlations

Age in years

Age in years 1

Pearson Correlation

Household income in thousands .335(**)

Price of primary vehicle .376(**)

.000

.000

6400

6400

N

.335(**)

Sig. (2-tailed)

6400

Pearson Correlation

lin

.376(**)

6400 1

.000

.000

N

6400

6400

.000

6400

0.335incomeϭage̶̴ΘδΒϤϫΐҨήο 0.376.......carϭage̶̴ΘδΒϤϫΐҨήο 0.792«incomeϭcar̶̴ΘδΒϤϫΐҨήο  incomeϝήΘϨ̯ 

Correlations

w

w w

.K

et

** Correlation is significant at the 0.01 level (2-tailed).

Control Variables Household income in thousands

6400

.792(**)

Sig. (2-tailed)

ab

Price of primary vehicle

.792(**)

1

.000

N

co

6400

Pearson Correlation

e.

Household income in thousands

m

Sig. (2-tailed)

Age in years

Price of primary vehicle

Correlation

Age in years 1.000

Price of primary vehicle .193

Significance (2-tailed)

.

.000

df

0

6397

Correlation

.193

1.000

Significance (2-tailed)

.000

.

df

6397

0

 0.193.......carϭage̶̴ΘδΒϤϫΐҨήο  ή˰ϴϐΘϣϝή˰ΘϨ̯ί΍β˰̡ϲ˰ϟϭΩϮ˰Α0.376ˬage,carή˰ϴϐΘϣϭΩϦϴ˰Αϲ̴Θδ˰ΒϤϫΐϳήοϝϭ΍ΖϟΎΣέΩϪΠϴΘϧ ΪϫΩϲϣϥΎθϧ΍έκϟΎΧρΎΒΗέ΍ΩΪϋϦϳ΍Ϫ̯Ωή̯΍Ϊϴ̡ήϴϴϐΗ0.193ϪΑˬincome 68


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

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ƱřźƿřżƿřƺŤǀŤƀƳř   ŢƄĭźŝƎųƶƫŵŚƘƯ RegressionƱƺǀſźĭŹ

co

e.

lin

ab

et

.K

w w

w

69 www.ircdvd.com

‫ﮔﺮﻭﻩ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

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‫ﮐﺘﺎﺑﺨﺎﻧﻪ ﺍﻣﻴﺪ ﺍﯾﺮﺍﻥ‬

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m

 Ωέ΍ΩΩϮΟϭϩ΍έϪγήϴϐΘϣϭΩρΎΒΗέ΍ζΠϨγϱ΍ήΑ   scatterplot έ΍ΩϮϤϧϢγέ˺  Corrolation ϲ̴ΘδΒϤϫΐϳήοϱήϴ̳ϩί΍Ϊϧ΍˻  Ζθ̳ήΑςΧ ϥϮϴγή̳έϪΒγΎΤϣ˼  ΩϭέϲϣέΎ̰ΑήϴϐΘϣϭΩρΎΒΗέ΍ϝΪϣϪ΋΍έ΍ϭζΠϨγϱ΍ήΑϪ̯Ζγ΍ϱέΎϣ΁εϭέϦϳήΗΩήΑέΎ̯ή̡ϥϮϴγή̳έ Ζγ΍΍έ΍Ω΍έϩΪηϩΪϫΎθϣϱΎϫϩΩ΍ΩΎΑϲ̴ϨϫΎϤϫϦϳήΘϜϳΩΰϧϪϛϲτΧϪϟΩΎόϣ  ΐϳ΍ήοˬϲτΧϥϮϴγή̳έ ΪϨϛϲϣΩέϭ΁ήΑ  Ωέ΍ΩΩϮΟϭΰϴϧϱΪόΑήϳΩΎϘϣϲϳϮ̴θϴ̡ϥΎϜϣ΍Ζθ̳ήΑςΧϪϟΩΎόϣί΍ϩΩΎϔΘγ΍ΎΑ Ϣϳέ΍Ω Dependent ϪΘδΑ΍ϭήϴϐΘϣϚϳϭ Independent ϞϘΘδϣήϴϐΘϣϚϳϥϮϴγή̳έϪϟΩΎόϣέΩ Ζγ΍ϲϜϳί΍ζϴΑϞϘΘδϣϱΎϫήϴϐΘϣΩ΍ΪόΗϩήϴϐΘϣΪϨ̩ϥϮϴγή̳έϪϟΩΎόϣέΩ  ϝΎΜϣ έϮθϛϚϳέ΍ϮϧΎΧΪϣ΁έΩαΎγ΍ήΑβϛϮϟϱϻΎϛϚϳεϭήϓϥ΍ΰϴϣΩέϭ΁ήΑ ΕϼϴμΤΗϥ΍ΰϴϣˬϪΑήΠΗˬϦγˬαΎγ΍ήΑϩΎ̴ηϭήϓϚϳϥΎ̳ΪϨηϭήϓεϭήϓϥ΍ΰϴϣΩέϭ΁ήΑ ιϮμΨϣϱΎϬηϭέί΍ϩΩΎϔΘγ΍ϭέάΑωϮϧˬΩϮϛωϮϧˬϲ̳ΪϧέΎΑϥ΍ΰϴϣαΎγ΍ήΑϝϮμΤϣϥ΍ΰϴϣΩέϭ΁ήΑ ΕΎϓ΁ϊϓέ ήϴΗωΎϔΗέ΍αΎγ΍ήΑϕήΑύ΍ή̩ήϴΗςγϮΗΎπϓϚϳϥΪηϦηϭέΖΣΎδϣΩέϭ΁ήΑ   ϩΩΎγϲτΧϝΪϣ Y / x  a  bX      2  (Y  Yˆ ) 2 ΖγΎϣϝήΘϨ̯ΖΤΗϭϞϘΘδϣήϴϐΘϣxϥ΁έΩϪϛ ΩϮηϲϣϩΪϴϣΎϧ  constantΎϳ΍ΪΒϣί΍νήϋέ΍ΪϘϣa ΩϮηϲϣϩΪϴϣΎϧϞϘΘδϣήϴϐΘϣΐϳήοΎϳςΧΐϴηέ΍ΪϘϣ  b Ζγ΍ϥϮϴγή̳έϝΪϣϭϩΪϫΎθϣϱΎτΧe Ζγ΍  2 βϧΎϳέ΍ϭϭa+bxϦϴ̴ϧΎϴϣϱ΍έ΍ΩϭΖγ΍xϪΑϪΘδΑ΍ϭϲϓΩΎμΗήϴϐΘϣY|x Ζγ΍  2 βϧΎϳέ΍ϭϭήϔλϦϴ̴ϧΎϴϣϱ΍έ΍ΩϭΖγ΍ϲϓΩΎμΗϱΎτΧ  ϲϓΩΎμΗήϴϐΘϣ a, b,  2 ί΍ΪϨΗέΎΒϋϝΪϣϦϳ΍ϱΎϫήΘϣ΍έΎ̡ Ζγ΍ϩΪηΩέϭ΁ήΑέ΍ΪϘϣ Yˆ ϭϲόϗ΍ϭέ΍ΪϘϣY  ΕΎοϭήϔϣ ΎϫϩΪϧΎϣϲϗΎΑϊϳί  ϮΗΎϳΖγ΍ϝΎϣήϧϊϳίϮΗϚϳϪΘδΑ΍ϭήϴϐΘϣϊϳίϮΗˬϞϘΘδϣήϴϐΘϣέ΍ΪϘϣήϫϱ΍ήΑ ΪηΎΑή̴ϳΩΕ΍ΪϫΎθϣί΍ϞϘΘδϣϭϝΎϣήϧΪϳΎΑ ΪηΎΑΖΑΎΛϪΘδΑ΍ϭήϴϐΘϣβϧΎϳέ΍ϭΪϳΎΑϞϘΘδϣήϴϐΘϣήϳΩΎϘϣϪϤϫϱ΍ήΑ ΪϨηΎΑϲϓΩΎμΗΪϳΎΑΕ΍ΪϫΎθϣϪϤϫ ΪηΎΑϲτΧΪϳΎΑϞϘΘδϣϭϪΘδΑ΍ϭήϴϐΘϣϭΩρΎΒΗέ΍   


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 ̮Ϥϧ ϑήμϣ ϭ ϞϘΘδϣ ήϴϐΘϣ ϥϮΧ έΎθϓ Ϫ̯ Ϊϳήϴ̴Α ήψϧ έΩ ΍έ ̮Ϥϧ ϑήμϣ ϭ ϥϮΧέΎθϓ ήϴϐΘϣ ϭΩ ˱ϼΜϣ Ζγ΍ϪΘδΑ΍ϭήϴϐΘϣ Ζγ΍ϩΩήϛϝΪϣ΍έΕ΍ΪϫΎθϣήΘϬΑϥϮϴγή̳έϪϟΩΎόϣΪηΎΑήΘϤϛΎτΧΕΎόΑήϣςγϮΘϣϩί΍Ϊϧ΍Ϫ̩ήϫ sy ΪϧϮηϲϣϪΒγΎΤϣήϳίΕέϮλϪΑϥϮϴγή̳έΐϳ΍ήοΩέϭ΁ήΑ  ˆ ˆ

br

sx

aˆ  y  b x

ΪϧϮηϲϣϪΒγΎΤϣήҨίΕέϮλϪΑ   Residual ΎτΧέ΍ΪϘϣΩέϭ΁ήΑ    yi  yˆ  y i  aˆ  bˆxi  Ϊϧ΍ϩΪϣ΁ΖγΪΑεϭέϦϴϤϫϪΑΰϴϧΎϫΩέϭ΁ήΑϪϛ 

Ζγ΍ΎτΧΕΎόΑήϣωϮϤΠϣϥΩήϛϪϨϴϤϛϑΪϫ ΪϴϨϛϲσ΍έήϳίήϴδϣέϮΘγΩϦϳ΍ϪΑϲγήΘγΩϱ΍ήΑ

m

Analyze - Regression - Linear...

w

w w

.K

et

ab

lin

e.

co

 ΍έήϴϐΘϣϦϳΪϨ̩ϞϘΘδϣήϴϐΘϣ ϥ΍ϮϨϋϪΑϪϛϲΗέϮλέΩ ΪϨ̩ ϥϮϴγή̳έ ϪΑ ρϮΑήϣ ΕΎΒγΎΤϣ spss ΪϴϨϛ Ωέ΍ϭ ΩήϛΪϫ΍ϮΧϪΒγΎΤϣ΍έϩήϴϐΘϣ  ΪϴϨϛΩέ΍ϭ΍έϪΘδΑ΍ϭήϴϐΘϣdependentΖϤδϗέΩ ϱΎϫήϴϐΘϣ Ύϳ ήϴϐΘϣ independent ΖϤδϗ έΩ ΪϴϨϛΩέ΍ϭ΍έϞϘΘδϣ ί΍ ϱέ΍ΪϘϣ ϭ ήϴϐΘϣ selection variable ΖϤδϗ έΩ Ωέ΍ϭ Ζγ΍ ΎϤη ήψϧ ΩέϮϣ ϲγέήΑ ϱ΍ήΑ Ϫϛ ϥ΁ ήϴϐΘϣϥ΍ϮϨϋϪΑϥϮϴγή̳έέΩΪϳΎΒϧήϴϐΘϣϦϳ΍ ΪϴϨϛ  ΪηΎΑϪΘϓέέΎϛϪΑϞϘΘδϣ ϥ΍ϮϨϋ ϪΑ Ϫϛ ϱήϴϐΘϣCase lable ΖϤδϗ έΩ ΪϴϨϛκΨθϣΖγ΍ϪΘϓέέΎϛϪΑΎϫΩέϮϣϲϣΎγ΍ ϪΑ ϲϫΩ ϥίϭ ήϴϐΘϣ WLS Wieght ΖϤδϗ έΩ  ΖϓέΪϫ΍ϮΧέΎϛϪΑβϧΎϳέ΍ϭέ΍ΪϘϣϱίΎγϥΎδϜϳϱ΍ήΑϥίϭϦϳ΍ ΪϴϨϛΩέ΍ϭ΍έΎϫΩέϮϣ Ωέ΍ΩΩϮΟϭϲΟϭήΧέΩϱή̴ϳΩϱΎϫϩέΎϣ΁ήϳΩΎϘϣζϳΎϤϧϥΎ  Ϝϣ΍StatisticϪϤϛΩΏΎΨΘϧ΍ΎΑ Ωέ΍ΩΩϮΟϭϥϮϴγή̳έϪϟΩΎόϣϲγέήΑϱΎϫέ΍ΩϮϤϧζϳΎϤϧϥΎϜϣ΍Plot  ϪϤϛΩΏΎΨΘϧ΍ΎΑ Ωέ΍ΪϧΎΘγ΍ ϩΪϧΎϣϲϗΎΑϭϪΘδΑ΍ϭήϴϐΘϣί΍ϩΪηϩΩέϭ΁ήΑήϳΩΎϘϣϱίΎγϩήϴΧΫϥΎϜϣ΍ SaveϪϤϛΩΎΑ Ωέ΍ΩΩϮΟϭ ϲόϗ΍ϭ 

70


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř  

H 0 :Ωέ΍ΪϧXϪΑΖΒδϧYΕ΍ήϴϴϐΗέΩϱήϴΛΎΗϭΖδϴϧΐγΎϨϣϝΪϣ  H 1 : Ζγ΍ΐγΎϨϣϥϮϴγή̳έϝΪϣ  ςϘϓ ή̴ҨΩ έΎΒ̰Ҩ ΍έ ϝΪϣΪҨέϭ΁ ΖγΪΑ  carϭ income ̵Ύϫ ήϴϐΘϣ ̵΍ήΑ ΍έ ϥϮϴγή̳έ ̶τΧ ϪϟΩΎόϣ ϝΎΜϣ ΪϴϨ̯ΏΎδΣΎϫΩήΠϣ̵΍ήΑ  Analyze - Regression - Linear...... Dependent……………car Independent………..income

 Model Summary

.792(a)

R Square .627

Adjusted R Square .627

Std. Error of the Estimate 13.38386

m

R

a Predictors: (Constant), Household income in thousands ANOVA(b) df

Mean Square

Regression

1930513.413

1

1930513.413

Residual

1146059.754

6398

179.128

Total

et

ab

3076573.167 6399 a Predictors: (Constant), Household income in thousands b Dependent Variable: Price of primary vehicle Coefficients(a) Unstandardized Model Coefficients

(Constant)

Std. Error .223

.221

.002

w w

Household income in thousands

B 14.799

.K

1

F

e.

Sum of Squares

10777.296

Sig. .000(a)

lin

Model 1

co

Model 1

Standardized Coefficients Beta

.792

t

Sig.

B 66.319

Std. Error .000

103.814

.000

a Dependent Variable: Price of primary vehicle

w

ϪΠϴΘϧ   ΪϨ̶̯ϣΏΎδΣ΍έϥϮϴγή̳έ̵ΎτΧέ΍ΪϘϣβϧΎҨέ΍ϭΰϴϟΎϧ΁ϝϭΪΟ H0 : a  0 H0 : b  0 Ζγ΍ϩΩ΍ΩϡΎΠϧ΍΍έϥϮϣί΁ϦҨ΍ΐ΋΍ήοί΍̮Ҩήϫ̵΍ήΑ H1 : a  0 H1 : b  0 ΪϨ̶̯ϣΩέ΍έήϔλνήϓϪϧϮϤϧβ̡ p  value  0.000 ϥϮ̩ ̶ϣέ΍ή̰Η΍έϥϮϴγή̳έςΧϪΒγΎΤϣϞΣ΍ήϣΪόΑϢϴϨ̶̯ϣ΍ΪΟ΍έ martialήϴϐΘϣ split fileΎΑϝϭ΍ΎϫΩήΠϣ̵΍ήΑ  ϢϴϨ̯

(car )Y  14.799  0.221* X (income)

Coefficientsa

Marital status Unmarried

Married

Model 1

1

(Constant) Household income in thousands (Constant) Household income in thousands

Unstandardized Coefficients B Std. Error 14.755 .318 .223

.003

14.840

.314

.219

.003

Standardized Coefficients Beta .791

.794

a. Dependent Variable: Price of primary vehicle

71

t 46.468

Sig. .000

73.323

.000

47.327

.000

73.504

.000


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř

 ΍έ ϦϴηΎϣ ϪϨҨΰϫ έ΍ΪϘϣ x=73 Ϊϣ΁έΩ έ΍ΪϘϣ ̵΍ήΑ ϩΪϣ΁ ΖγΪΑ ϝΪϣ ϪΑ ϪΟϮΗ ΎΑ Ϣϴϫ΍ϮΧ ̶ϣ ΪϴϨ̯ νήϓ ΪηΎΑ̶ϤϧΎϣ̵ΎϫϩΩ΍Ω˯ΰΟϩΩ΍ΩϦҨ΍Ϫ̶̯ϟΎΣέΩϢϴϨ̯ΏΎδΣ ϢϴϨ̶̯ϣΩέ΍ϭincome ϥϮΘγέΩϒҨΩέϦҨήΧ΁ DataviewϪΤϔλέΩ΍έ̀˼ΩΪϋ˺ ˻ Analyze - Regression - Linear...... Dependent……………car 

Independent………..income Save ……predicted value…..standardized

ab

lin

e.

co

m

y=0.3208ϒҨΩέϦҨήΧ΁έΩZPRϡΎϧϪΑΪҨΪΟϥϮΘγέΩϩΪη̶ϨϴΑζϴ̡έ΍ΪϘϣϩΪϫΎθϣ˼  ΎϫϩΪϧΎϣ̶ϗΎΑϩΪϫΎθϣ̵΍ήΑ̊  Save ………Residuals ..…..standardized  ΩϮη̶ϣϩΩ΍ΩϥΎθϧDataviewϪΤϔλέΩΎϫϩΪϧΎϤϴϗΎΑί΍̶ϧϮΘγ   ΖΑΎΛέ΍ΪϘϣϥϭΪΑϥϮϴγή̳έςΧΐҨ΍ήοϪΒγΎΤϣ  Analyze - Regression - Linear......option…..Include constant equation     Standardized Coefficients

t

Sig.

Beta

B

Std. Error

B Household income in thousands

Std. Error

.314

w w

Model 1

Unstandardized Coefficients

.K

et

Coefficients(a,b)

.002

.885

151.668

.000

w

a Dependent Variable: Price of primary vehicle b Linear Regression through the Origin

              72


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ƱřźƿřżƿřƺŤǀŤƀƳř

ab

lin

e.

co

m

  Ε΍ήϴϴϐΗΚϋΎΑϥ΁̮̩Ϯ̯ήϴϴϐΗ̶ϨόҨΩέ΍Ω̵ήΘθϴΑΖϴϤϫ΍ήϴϐΘϣϡ΍Ϊ̯ϩήϴϐΘϣΪϨ̶̩τΧϝΪϣέΩϝ΍ϮΌγ ˮΩϮη̶ϣϪϟΩΎόϣ̱έΰΑ Y  a  b1 X 1  b2 X 2  ...  bk X k    νϮϋΰϴϧΐҨήοϢϴϨ̯νϮϋ΍έΎϫήϴϐΘϣΪΣ΍ϭή̳΍ϥϮ̩ΖδϴϧΎϬϧ΁ΖϴϤϫ΍ϪϧΎθϧΐҨ΍ήο̶̰̩Ϯ̯ΎҨ̶̳έΰΑ ΩϮη̶ϣ ΖΤΗ Standardized Coefficients ϥϮΘγ ,CoefficientsϝϭΪΟέΩϥ΁ΐҨήοΪηΎΑΪΣ΍ϭϥϭΪΑή̳΍ήϴϐΘϣ ΪҨ΁̶ϤϧϝΪϣέΩϪ̯Ζγ΍Ε΍ήϴϴϐΗΖϴϤϫ΍ΐҨήοϦҨ΍ΩϮη̶ϣΝέΩBetaϥ΍ϮϨϋ ή̳΍Ϫ̯Ζγ΍ϩΪηΝέΩϝϭΪΟέΩϢϫ Beta̮ҨΩέ΍ΩΩϮΟϭϞϘΘδϣήϴϐΘϣ̮ҨςϘϓϪ̯ϻΎΑϝΎΜϣέΩ˱ϼΜϣ Ϊη̶ϣϢϴδϘΗϒϠΘΨϣ̵ΎϫBetaϪΑΪηΎΑήϴϐΘϣΪϨ̩  Ϣϴϧ΍ΪΑϢϴϫ΍ϮΨϴϣΪϧ΍ϪΘϓή̳ϩί΍Ϊϧ΍΍έΪϧ΍ϩΩή̯ϑΩΎμΗϪ̶̯ϧΎ̳ΪϨϧ΍έϥϮΧϞ̰ϟ΍ϥ΍ΰϴϣϝΎΜϣ ΪϴϫΩϥΎθϧέ΍ΩϮϤϧΎΑˮϪϧΎҨΖδϫΕΎϓΩΎμΗΩ΍ΪόΗϭϞ̰ϟ΍ϥ΍ΰϴϣϦϴΑ̶σΎΒΗέ΍ΎҨ΁˺ ΪϴϨ̯ΪϴҨΎΗ΍έΕΎϴοήϓϭΪϴϨ̯ΏΎδΣ΍έήϴϐΘϣϭΩϦϴΑ̶τΧ̶̴ΘδΒϤϫΐҨήο˻ Ω΍ΪόΗϥϮΧϞ̰ϟ΍ϒϠΘΨϣΡϮτγ̵΍ήΑϭΪϴϨ̯Ωέϭ΁ήΑ΍έήϴϐΘϣϭΩϦϴΑ̶τΧϪτΑ΍έ spssί΍ϩΩΎϔΘγ΍ΎΑ˼ ΪϴϨ̯Ωέϭ΁ήΑ΍έΕΎϓΩΎμΗ ˮΪϨϟΎϣήϧϊҨίϮΗ̵΍έ΍ΩΎϫϩΪϧΎϤϴϗΎΑΎҨ΁̊ ˮΪϨΘδϫ̶ϓΩΎμΗΕ΍ΪϫΎθϣΎҨ΁̋ ϥϮΧϞ̰ϟ΍ ̋ ˺˹ ˺̋ ˻˹ ˻̋ ˼˹ ˼̋ ΕΎϓΩΎμΗΩ΍ΪόΗ ˺˹ ˺̀ ˻̌ ˼˹ ˼˻ ˼́ ̊˻  ˺Ώ΍ϮΟ  Ωέ΍ΩΩϮΟϭ̶τΧϪτΑ΍έΪγέ̶ϣήψϧϪΑϪΠϴΘϧ  Graph – Legacy dialogs – scatter plot– simple

.K

et

50 00

w w

accidentno

40 00

30 00

Ζγ΍ΕΎϓΩΎμΗΩ΍ΪόΗϪΘδΑ΍ϭήϴϐΘϣYϭϞ̰ϟ΍ϞϘΘδϣήϴϐΘϣX      ˻Ώ΍ϮΟ

w

20 00

10 00 5 00

10 00

15 00

20 00

25 00

X Axis…..alckol Y Axis…..accidentno

35 0

30 00

Correlations alckol alckol

Pearson Correlation

1

accidentno .984(**)

Sig. (2-tailed) N accidentno

Pearson Correlation Sig. (2-tailed) N

.000 7

7

.984(**)

1

.000 7

7

** Correlation is significant at the 0.01 level (2-tailed).

73

̶ϨόҨ ήϔλ νήϓ p  value  0.000  ΎΑ ΩϮη̶ϣΩέϪϧϮϤϧςγϮΗήϴϐΘϣϭΩϝϼϘΘγ΍ ΖγϻΎΑ̶̴ΘδΒϤϫϪϧΎθϧϪ̯   0.984    


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ŶŝįŹįŚƣō ŵŚŤſřSPSS1

ƱřźƿřżƿřƺŤǀŤƀƳř ˼Ώ΍ϮΟ

Analyze - Regression - Linear............ Dependent……………accidentno 

Independent………..alckol

 ANOVA(b)

Model 1

Regression Residual Total

Sum of Squares 740.571

df 1

Mean Square 740.571

24.286

5

4.857

764.857

6

F 152.471

Sig. .000(a)

m

a Predictors: (Constant), alckol b Dependent Variable: accidentno

Std. Error 1.863

1.029 a Dependent Variable: accidentno

.083

alckol

(accidentno )Y  7.286  1.029 * X ( alckol )

Std. Error .011

12.348

.000



ϪΠϴΘϧ   H0 : b  0  H1 : b  0  Ζγ΍ϩΩ΍ΩϡΎΠϧ΍΍έϥϮϣί΁ϦҨ΍ΐ΋΍ήοί΍̮Ҩήϫ̵΍ήΑβϧΎҨέ΍ϭΰϴϟΎϧ΁ϝϭΪΟέΩ ΪϨΘδϴϧήϔλΐҨ΍ήο̶ϨόҨΪϨ̶̯ϣΩέ΍έήϔλνήϓϪϧϮϤϧβ̡ p  value  0.000 ϥϮ̩

w

w w

.K

H1 : a  0

.984

B 3.912

et

H0 : a  0

Beta

Sig.

e.

B 7.286

(Constant)

t

lin

Model 1

Standardized Coefficients

ab

Unstandardized Coefficients

co

Coefficients(a)

 ΎϫϩΪϧΎϤϴϗΎΑϥϮΘγΩΎΠҨ΍̵΍ήΑ̊Ώ΍ϮΟ 

Analyze - Regression - Linear...... Dependent……………accidentnor 

Independent………..alckol Save …… Residuals …..standardized

 Runs Test 2̋Ώ΍ϮΟ RunsϥϮϣί΁ί΍ϩΩΎϔΘγ΍ΎΑ Test Value(a)

Standardized Residual .0000000

Cases < Test Value

5

Cases >= Test Value

2

Total Cases

7

Number of Runs

3

Z Asymp. Sig. (2-tailed)

-.380 .704

p  value  0.704  0.05

̶ϓΩΎμΗΎϫϩΪϧΎϤϴϗΎΑϭΩϮη̶ϤϧΩέϪϧϮϤϧςγϮΗήϔλνήϓβ̡ Ϊϧ΍  N (0,1) Standard Residual Unstandard Residual  N (0,  2 ) a Mean

74


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