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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
íë ææ æí ± µÁY Ä ¸m ƶǀƫƹř ƞƿŹŚƘţ SPSS : Statistical Package For Social Science
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ΪηΎΑ ϲϣ ήϳΩΎϘϣ ϩΪϨϫΩ ϥΎθϧ έϮϧ ϭ έΰΑ έϮϧ ˬ ϮϨϣ ΖγήϬϓ ˬ ϥϮϨϋ έϮϧ ϞϣΎη έΰϓ ϡήϧ Ϧϳ Ζ˰γ record ̮ ˰ϳ ήτγ ήϫ ϭ ΪηΎΑ ϲϣ Ρήτϣ variable Ύϳ ήϴ ϐΘϣ ϥϮϨϋ ϪΑ Ϫ̯ Ζγ field ̮ϳ ϥϮΘγ ήϫ ΪηΎΑ ϲϣ Ρήτϣ cace Ύϳ ΩέϮϣ ̮ϳ ϥϮϨϋ ϪΑ Ϫ̯ ή˰ϫ Ζ˰γ ϪΤϔ˰λ Ϧ ˰ϳ ΕΎ˰ϋϼσ ζϳή ˰ϳ ϭ SPSS Ϫ˰ϣΎϧήΑ έΎ˰̯ ϭ Ϊ˰Ϩϳ Ϯ̳ ϲ ϣ Datasheet ϩΪη ίΎΑ ϩήΠϨ̡ ϪΑ ΩέΩ ΖϤδϗϭΩ Datasheet ϢϴϨ̯ ϲϣ Ωέϭ ϥέΩ έ ήϳΩΎϘϣ Ϫ̯ Data view ˺ ϢϴϨ̯ ϲϣ ϒϳήόΗ έΎϫ ήϴϐΘϣ Ϫ̯ Variable view ˻ ή ˰ϴ ϐΘϣ Ϫ˰̯ έΎ˰Α ή˰ϫ ϭ Ζ˰γΎϬϧ αΎ ˰ϴ Ϙϣ Ύ˰Ϭϧ Ζϴ ˰λΎΧ Ϧϳ ή˰ΘϤϬϣ Ϫ̯ ΪϧέΩ ϲΗΎϴ λϮμΧ Ύϫ ήϴ ϐΘϣ SPSS έΩ ϢϴϨ̯ ϒϳήόΗ έ ϥ αΎϴϘϣ ΪϳΎΑ ΩϮη ϲϣ ϒϳήόΗ ŚƷ ŽŚǀƤƯ ƕřƺƳř
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Nominal ϱέϮλ ϲϤγ αΎϴϘϣ ˺ έΩ ΩϮ˰η ϲ Ϥ˰γ αΎ ˰ϴ Ϙϣ Ϫ˰Α Ϟϳ ΪΒΗ ϭ ϩΩΩ Ϊ̯ ϥ ϪΑ ϢϴϧϮΘϴ ϣ Ϫ̯ Ϣϴ Ϩ̯ ϲ ϣ ϲ ̳Ϊϧί ϥέΩ Ϫ̯ ϱ ήϬη ΪϨϧΎϣ ϩϭή˰̳ ΐ˰γΎϨϣ ϭ Ωή˰̯ ϱ έά˰̳ Ϊ˰̯ ϥϮΗ ϲϣ έϮσ ήϫ ϭ ΩέΪϧ ϲ ΗϭΎϔΗ ΩΪϋ ϥΩϮΑ ̱έΰΑ ϭ ̮̩Ϯ̯ αΎϴ Ϙϣ Ϧϳ ΩέΪϧ ϲϨόϣ ϥ έΩ ϊϤΟ ϭ Ϧϴ̴ϧΎϴϣϭ Ζγ ϱΪϨΑ Ordinal ϲΒϴΗήΗ ϱ ϪΒΗέ αΎϴϘϣ ˻ Ύ˰ΠΑΎΟ Ύ˰ϫ Ϊ˰̯ ή˰̳ Ϊ˰ϫΩ ϲ ˰ϣ ϥΎθ˰ϧ έ ΐ ˰ϴΗ ήΗ Ύ ˰ϳ Ζϳ Ϯϟϭ ϭ ΪηΎΑ ϲ ϣ Ζϴ Ϥϫ ϪΟέΩ αΎγ ήΑ ϱ έά̳ Ϊ̯ ΩέΪϧ ΩϮΟϭ ΎϬϫϭή̳ ϦϴΑ ̵ήϴ̳ ϩίΪϧ ϞΑΎϗ ϪϠλΎϓ ϲϟΎϋ ϭ ΪΑ ˬ ΏϮΧ ΕέΎϬϣ ΪϨϧΎϣ ΪϨ̯ ϲϣ ϕήϓΪϧϮη Scale ϲΒδϧ αΎϴϘϣ .˼ Ωήϓ Ϊϗ ϞΜϣ ΩέΩ ϲΒδϧ αΎϴϘϣ ΪϳΎϴΑ ΖγΪΑ ϱήϴ̳ ϩίΪϧ ϖϳήσ ί Ϫ̯ ϱΩΪϋ ήϫ żĩźưţ ƆųŚƃ έΎ˰̰Α ϲ Βδ˰ϧ αΎ ˰ϴ Ϙϣ ϱ ή˰Α ς˰Ϙϓ Ϫ˰̯ Ζγ Mean Ϧϴ̴ϧΎϴ ϣ ϥ Ϧϳ ήΘϓϭήόϣ ϭ ΖγΎϫ ϩΩΩ ΰ̯ήϤΗ ϪϧΎθϧ Ύ˰ϫ ϩΩΩ ή˰̳ ϲϟϭ Ωϭέ ϲ ϣ ϥ ϑήσ ϪΑ ΪηΎΑ ̱έΰΑ ήϳ ΩΎϘϣ ή̳ ϭ Ζγ αΎδΣ Ύϫ ϩΩΩ ϪΑ έΎϴδΑ Ωϭέ ϲϣ ΩϮΑ ΪϫϮΨϧ ΎϳϮ̳ Ϧϴ̴ϧΎϴϣ ΪϨηΎΑ ϩΪϨ̯ή̡ ϲϠϴΧ ή̳Ϊ ˰ϳ ϲ ˰ϣ Ζ˰γΪΑ Ϫ˰ΒΗέ ϱ ϭέ ί ϭ Ζ˰γ Ύ˰ϫ ϩΩΩ ̶ϧΎ˰ϴϣ Ϫ˰ΒΗέ Ϫ̯ Ζγ Median ϪϧΎϴ ϣ ή̴ϳ Ω κΧΎη n 1 ̱έΰ˰Α ϩΩΩ ϪΑ ϪϧΎϴϣ Ζγ ϪϧΎϴ ϣ ϲ τγϭ ΪηΎΑ Ωήϓ ή̳ ϭ Ζγ ϪϧΎϴ ϣ Ϫϴ Ηέ ΪηΎΑ Νϭί Ύϫ ϩΩΩ ΩΪόΗ 2 ΪηΎΑ ϲϤϧ αΎδΣ ̮̩Ϯ̯ Ύϳ ϲϧ ϭήϓ ϥΰϴ ϣ Ωϭέ ϲ ϣ έΎ̰Α Ϣϫ Nominal Ύϳ ϲ Ϥγ ϱ Ύϫ ϩΩΩ ϱ ήΑ Ϫ̯ Ζγ Mode Ϊϣ ή̴ϳ Ω κΧΎη ΩέΩ έ ΕΪϫΎθϣ ϦϳήΘθϴΑ ϭ ΪϫΩ ϲϣ ϥΎθϧ έ Ύϫ ϩΩΩ 1
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř ƾĭŶƴĩřźě ƆųŚƃ
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βϧΎ ˰ϳ έϭ ϥ Ϧϳ ή˰Θϓϭήόϣ ϭ Ωϭή ˰ϴ ϣ έΎ˰̰Α Scale Ύ ˰ϳ ϲ Βδ˰ϧ ϱ Ύ˰ϫ ϩΩΩ ϱ ή˰Α ϭ ΖγΎϫ ϩΩΩ ϲ ̳ΪϨ̯ή̡ ϪϧΎθϧ f i Ϫ˰̯ Ζ˰˰γ Mean = Ϧ xi xi f i ϴ̴ϧΎ˰˰˰˰ϴϣ Ζ˰˰˰˰γ ϩΩΩ ή˰˰˰˰ϫ ϲ˰˰˰˰ϧϭήϓ n fi ( xi x ) 2 variance = βϧΎ ϳ έϭ ( x x ) 0 i n ϡή̳ Ϯ˰Ϡϴ̯ Ύ˰ϫ ϩΩΩ Ϊ˰Σϭ ή˰̳ Ζ˰γ ϲ ˰̰ϳ Ύ˰ϫ ϩΩΩ ΪΣϭ ΎΑ ϥ ΪΣϭ Ϫ̯ Ζγ έΎϴ όϣ ϑήΤϧ βϧΎϳ έϭ έάΟ ϭ ϩΩΩ ϥϮ˰̩ ΪϨΘδ˰ϫ ϲ ΑϮ˰Χ ϱ Ύ˰ϫ κΧΎη έΎϴόϣ ϑήΤϧ ϭ βϧΎϳέϭ Ζγ ϡή̳ϮϠϴ̯ Ϣϫ έΎϴ όϣ ϑήΤϧ ΪηΎΑ ΪϧήΛϮϣ ϥέΩ Ύϫ R=max-min ΪηΎΑ ϲϣ Range Ύϳ ΕήϴϴϐΗ ϪϨϣΩ ϲ̳ΪϨ̯ή̡ ή̴ϳΩ κΧΎη ϲ̯έΎ̩ ήΒϧΎϴϣ ϭ ϢϴϨ̯ ϲϣ ϩΩΎϔΘγ ϥ ίΪϨηΎΑ ϩΪϨ̯ή̡ ϲϠϴΧ Ύϫ ϩΩΩ ή̳ Ϫ̯ Ζγ ̭έΎ̩ ή̴ϳΩ κΧΎη R Q3 Q1 Ϫ˰ϧΎϴ ϣ ϥΎϤϫ ϡϭΩ ̭έΎ̩ ϭ ΪϨΘδϫ ήΘθϴ Α ϥί Ύϫ ϩΩΩ ˻̋ ϭ ήΘϤ̯ ϥ ί Ύϫ ϩΩΩ ̀̋ ϲϨόϳ ϡϮγ ̭έΎ̩ Ζγ ΪηΎΑ ϲϣ ϝΎϣήϧ ϪόϣΎΟ ϥ ΪϨηΎΑ ̮ϳΩΰϧ Ϣϫ ϪΑ Ύϫ ϩΩΩ Ϧϴ̴ϧΎϴϣ ϭ ϪϧΎϴϣ ή̳
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˺ϝΎΜϣ κΧΎ˰η β̢˰γ Ϊ ˰ϴ Ϩ̯ Ωέϭ ϪΤϔλ έΩ έ ήϳ ί ΕΎϋϼσ ϭ ϩΩή̯ ϒϳ ήόΗ έ ϲ Ϡϣ κϟΎΧΎϧ ΪϣέΩ ϭ έϮθ̯ ϡΎϧ ϱ Ύϫ ήϴϐΘϣ ΪϴϳΎϤϧ ϪΒγΎΤϣ έ ϲ̳ΪϨ̯ή̡ ϭ ϊϳίϮΗ ˬϱέΎϣ ϒϠΘΨϣ ϱΎϫ ϲϠϣ κϟΎΧΎϧ ϡέΩ ˺̋ ˻˹ ̋ ˺˹ ˺˼
έϮθ̯ ϡΎϧ ϥήϳ Ϫϴ̯ήΗ ϥΎΘδϧΎϐϓ ϕήϋ ϥΎΘδ̯Ύ̡
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:ϢϴϨ̯ ϲϣ ϒϳήόΗ variable view έΩ έΎϫ ήϴϐΘϣ country ϡΎϧ ϪΑ ΎϫέϮθ̯ ϡΎϧ ϝϭ ήϴϐΘϣ ΩϮθϧ ̠ϳΎΗ ϲγέΎϓ ϭ ΩέΪϧ ϲϟΎ̰η ϩήϴΗ ςΧ ϲϟϭ ΩϮη ϩΩΎϔΘγ ϪϠλΎϓ ί ΪϳΎΒϧ ήϴϐΘϣ Ϣγ ̠ϳΎΗ έΩ ϢϴϨ̯ ϲϣ ΏΎΨΘϧ STRING ήϴϐΘϣ ωϮϧ Ύϳ Type έΩ name of countryˬ ˱ϼΜϣ ΩΩ ήϴϐΘϣ ΩέϮϣ έΩ ϲϓΎ̯ ΕΎΤϴοϮΗ ϥϮΘϴϣ label έΩ ϱ Ύ˰ϫ ή ˰ϴϐΘϣ ϱ Ϊ˰ϨΑ Ϊ˰̯ ιϮμ˰Ψϣ ϭ Ϣϴ ˰ϧί ϲ ˰Ϥϧ ϱΰ ˰ϴ ̩ ϩΪ˰η ϒϳ ήόΗ ϲ Ϥγ ήϴ ϐΘϣ ϥϮ̩ value ΖϤδϗ έΩ ΪϨηΎΑ ϱΩΪϋ Ϫ̯ Ζγ ϲϫϭή̳ έΩ ˱ϼΜϣΩϮ˰˰η ϲ ˰Ϥϧ Ϫ˰˰Θϓή̳ ή˰˰ψϧ έΩ ΕΎΒ˰γΎΤϣ έΩ Ϫ˰˰̯ ΩϮ˰˰η ϲ˰ ϓήόϣ ϱΩΪ˰ϋ ϲ˰ Ϩόϳ missing ΖϤδ˰˰ϗ έΩ Ζϗϭ ήϫ Ϫ̯ ϢϴϨ̰ϴϣ ϲϓήόϣ έ ˺˹˹˹ ΩΪϋ Ύϣ Ζγ ϩΪθϧ ή̡ Ύϳ ΖγΎϧϮΧΎϧ Ύϫ ϪϨϳ ΰ̳ ϲ ΧήΑ Ύϫ ϪϣΎϨθγή̡ ΩϮθϧ ΏΎδΣ ϭ ΪηΎΑ ϥ ϲϨόϣ ϪΑ Ϊη ΝέΩ ϢϴϨ̯ ϲϣ ϒϳήόΗ ˺˹ έ ϥϮΘγ ϱΎϨϬ̡ ςγϭ Ύϳ ̠̩ ˬ Ζγέ ϢϴϨ̰ϴϣ ΏΎΨΘϧ έ Align ϢϴϨ̯ ϲϣ ΏΎΨΘϧ ϲϤγ ϭ Nominal έ ήϴϐΘϣ αΎϴϘϣ Ύϳ Measure 2
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
GNP Ύϳ ϲϠϣ κϟΎΧΎϧ ΪϣέΩ ϥΰϴϣ ϡϭΩ ήϴϐΘϣ ΩέΩ ϱΩΪϋ ήϳΩΎϘϣ ϪΑ ιΎμΘΧ Ϫ̯ ϢϴϨ̯ ϲϣ ΏΎΨΘϧ Numeric ήϴϐΘϣ ωϮϧ Ύϳ Type έΩ Gross National Productˬ ˱ϼΜϣ ΩΩ ήϴϐΘϣ ΩέϮϣ έΩ ϲϓΎ̯ ΕΎΤϴοϮΗ ϥϮΘϴϣ label έΩ Ϣϴϧί ϲϤϧ ϱΰϴ̩ value ΖϤδϗ έΩ ϢϴϨ̯ ϲϣ ϒϳήόΗ ˺˹ έ ϥϮΘγ ϱΎϨϬ̡ ςγϭ Ύϳ ̠̩ ˬ Ζγέ ϢϴϨ̰ϴϣ ΏΎΨΘϧ έ Align ϢϴϨ̯ ϲϣ ΏΎΨΘϧ ϲΒδϧ ϭ Scale έ ήϴϐΘϣ αΎϴϘϣ Ύϳ Measure ΩέϮΧ ϲϣ ΰϴϤϣ Ϣϗέ˻ ΎΗ ϢϴϧΰΑ ˻ ή̳ έ Decimal
2
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Type
country String GNP Numeric
Width
Decimal
Label
Value
8 8
2
country GNP
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Missing column 10 10
-
Align left left
Measure Nominal Scale
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Ύϫ κΧΎη ϪΒγΎΤϣ ϝϭ εϭέ
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e.
co
Analyze – Report –Case Summaries ϩή˰ΠϨ̡ ί ζ˰Ϡϓ ς˰γϮΗ έ ϱ έΎ˰ϣ ϱ Ύ˰ϫ κΧΎ˰η Ϣϴ ˰ψϨΗ Ζ˰ϬΟ ή˰ψϧ ΩέϮϣ ήϴϐΘϣ Ϫ̯ ΩϮ η ϲ ϣ ίΎΑ ϱ ϩήΠϨ̡ κΧΎ˰η statistic Ϫ˰Ϥ̳Ω ϥΩί Ύ˰Α β̢˰γ ϭ Ϣϴ ˰ϫΩ ϲ ˰ϣ ϝΎ˰ϘΘϧ Variable Ζ˰γέ ΖϤγ ϩήΠϨ̡ ϪΑ ΩϮΟϮϣ ϭ kutosis ϲ̳Ϊϴ θ˰̯ ˬ skewness ϲ ̴ϟϮ̩ ˬ βϧΎϳ έϭ ˬϦϴ̴ϧΎϴ ϣ ˬ ϪϧΎϴ ϣ ΪϨϧΎϣ έ ήψϧ ΩέϮϣ ϱ έΎϣ ϱΎϫ ϢϴϨ̯ ϲϣ ΏΎΨΘϧ Ϫ˰̩ήϫ ϭ Ϊ ˰ϳ ΪϨΒϧ έ ϩή˰ΠϨ̡ Ϧϳ Ζγ ήΘϬΑ ΩϮη ϲ ϣ ήϫΎχ output1 ϡΎϧ ϪΑ ϪϧΎ̳ΪΟ ϩήΠϨ̡ έΩ spss ϲΟϭήΧ Ϊη ΪϫϮΧ ϪϓΎο ϩήΠϨ̡ ϦϴϤϫ έΩ ΪϴϨ̯ ϪϓΎχ Ύϫ κΧΎη ϪΒγΎΤϣ ϡϭΩ εϭέ Analyze – Descriptive statistics - frequencies ϢϴϨ̯ Ϣγέ ΰϴϧ έ ϲϧϭήϓ ϝϭΪΟ ϢϴϧϮΗ ϲϣ ΪϨΘδϫ ΎϬ̯Ϊλ Percentile ϭ Ύϫ ̮ϫΩ Cutpoints ˬ ΎϬ̯έΎ̩ Quartiles ϞϣΎη Percentile Values ϲ ˰ϣ ϊ ˰ϳίϮΗ έΎ ˰ϴόϣ Distribution ˬ ϲ ̳Ϊ˰Ϩ̯ή̡ έΎϴόϣ Dispersion ˬ ΰ̯ήϤΗ ϱΎϫ κΧΎη Central Tendency ΪϨηΎΑ ϢϴϨ̯ ΏΎδΣ έ ϲ̳Ϊϴθ̯ ϭ ϲ̴ϟϮ̩ ϢϴϧϮΗ ϲϣ ΪϨηΎΑ ΕϭΎϔΘϣ ϭ ϩΪϨ̯ή̡ Ύϫ ϩΩΩ ή̳ ̮ ˰ϴΗ έ Display frequency table Ϫ˰Ϩϳΰ̳ Ϊ ˰ϳΎΑ Ϣϴ ϫϮ˰ΨΑ έ ϲ ˰ϧϭήϓ ϝϭΪ˰Ο ϭ ΪϨ˰ηΎΑ ϱέή̰Η Ύϫ ϩΩΩ ή̳ ϢϴϧΰΑ ƾĭŶǀƄĩ ƩŚƯźƳ ŹŵřƺưƳ ΪηΎΑ ϲϣ kutosis>0 ϩΪϴθ̯ ΖϟΎΣ έΩ ϭ kutosis<0 ϲΨ̡ ΖϟΎΣ έΩ Ζγ Ϊϣ!ϪϧΎϴϣ!Ϧϴ̴ϧΎϴϣ ϭ skewness<0 ̠̩ ϪΑ ϲ̴ϟϮ̩ ΖϟΎΣ έΩ Ϧϴ̴ϧΎ ˰ϴϣ! Ϫ˰ϧΎϴϣ! Ϊ˰ϣ ϭ skewness>0 Ζ˰γέ Ϫ˰Α ϲ ̴ϟϮ˰̩ Ζ˰ϟΎΣ έΩ Ζγ 3
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Ʊřźƿř żƿř ƺŤǀŤƀƳř
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
Ύϫ ήϴϐΘϣ ΏΎΨΘϧ
e.
Name
1
co
m
˻ϝΎΜϣ ϱ Ύ˰ϫ κΧΎ˰η β̢˰γ ΪϴϨ̯ Ωέϭ ϪΤϔλ έΩ έ ήϳ ί ΕΎϋϼσ ϭ ϩΩή̯ ϒϳ ήόΗ έ ϥΎ̰ϣ ϭ ϥΎϣί ˬ Ϧϴ ηΎϣ ωϮϧ ϱ Ύϫ ήϴϐΘϣ έ ϲϨϴ ˰ηΎϣ ωϮ˰ϧ Ϫ˰̩ ςγϮΗ ϭ ϥΎϣί ϡΪ̯ έΩ ΩΩήΗ ϥΰϴ ϣ ϪΠϴΘϧ Ϊϴϳ ΎϤϧ ϪΒγΎΤϣ έ ϲ ̳ΪϨ̯ή̡ ϭ ϊϳ ίϮΗ ˬϱ έΎϣ ϒϠΘΨϣ ΪϴϳΎϤϧ κΨθϣ ΖΧϮγ ϥΰϴϣ ϥΎϣί ϥΎ̰ϣ ϦϴηΎϣ ωϮϧ ̌ ̋ ˺˻ Ρήσ ϞΧΩ ϲμΨη ˺̋ ˺˼ Ρήσ ΝέΎΧ ϲμΨη ˺̊ ˺̊ Ρήσ ϞΧΩ ϲμΨη ˺̊ ˺˼ Ρήσ ΝέΎΧ ϲδ̯ΎΗ ˻˹ ˺̊ Ρήσ ϞΧΩ ϲδ̯ΎΗ ˻̌ ˺̋ Ρήσ ΝέΎΧ αϮΑϮΗ ˼˹ ˺̌ Ρήσ ϞΧΩ Ζϧϭ ˼˹ ˺̌ Ρήσ ΝέΎΧ αϮΑϮΗ ̋˹ ˺̀ Ρήσ ϞΧΩ ϲδ̯ΎΗ ̀ ˺̋ Ρήσ ΝέΎΧ ϲμΨη ́ ˺˼ Ρήσ ϞΧΩ Ζϧϭ
w
w w
ΩέΩ ΖϴϤϫ ϲϧϭήϓ ϝϭΪΟ ϭ Ζδϴϧ ϢϬϣ ϱέΎϣ ϱΎϫ κΧΎη Ϊϧ ϩΪη ϱΪϨΑ Ϊ̯ Ϫ̯ ϲϳΎϫ ήϴϐΘϣ ϱήΑ valid percent ϭ percent ϥϮΘ ˰γ ϭΩ Ϧϴ ˰Α ϲ ˰ϧϭήϓ ϝϭΪ˰Ο έΩ Ϣϳ Ωή˰̯ ϲ ˰ϣ ϒ ˰ϳήόΗ missing ή̳ ΎΠϨϳ έΩ Ύϣ Ϊη ϲϣ ΩΎΠϳ ΕϭΎϔΗ Ύ˰˰ϫ ϩΩΩ ή˰̳ Ϫ˰̯ Ζ˰γ ϲ ˰όϤΠΗ Ϊ˰λέΩ ϩΪ˰˰ϨϫΩ ϥΎθ˰ϧ ϲ ˰ϧϭήϓ ϝϭΪ˰Ο έΩ Cumulative percent ϥϮΘ˰γ ΩέΪϧ ϲϨόϣ ϥϮΘγ Ϧϳ ΪηΎΑ Nominal ϢϴϫϮΧ ϲϣ Ύϣ Ύϣ Ζγ ϩΪη έή̰Η έΎΑ ̮ϳ ΩέϮϣ ήϫ ˱ΎΒϳήϘΗ ϭ ΪϫΪϴϤϧ ϥΎθϧ ϱΰϴ ̩ ΖΧϮγ ϲ ϧϭήϓ ϝϭΪΟ ϢϴϨ̯ ϲϣ ϲϓήόϣ έ Gfuel ϡΎϧ ϪΑ ϱΪϳΪΟ ήϴϐΘϣ άϟ ϢϴΠϨδΑ ϒϠΘΨϣ ϱΎϫ ϪϠλΎϓ έΩ Transform-Record-Into Different Variable ϲϣ έ old and new value ϪϤ̳Ω β̢γ ˬϩΩί έ change ϭ ϢϴϨ̯ ϲϣ ΝέΩ έ ΪϳΪΟήϴϐΘϣ ΐδ̩ήΑ ϭ ϡΎϧ ΖϤδϗ έΩ Ϣϴϧί ̮ ˰ϳ Ϫ˰Α ̮ ˰ϳ ήχΎ˰ϨΗ ϭ Ϣϴ ˰ϫΩ ΖΒδ˰ϧ Ϊ ˰ϳΪΟ ή ˰ϴϐΘϣ Ϣϳ Ϊ˰ϗ ήϴϐΘϣ έΪϘϣ ήϫ ϪΑ ϢϴϫϮΧ ϲϣ ή̳ Old value Ϊ ˰ϳ ΪΟ Ξ˰ϧέ ̮ ˰ϳ Ϣϳ Ϊ˰ϗ ή ˰ϴ ϐΘϣ έΪ˰Ϙϣ ή˰ϫ Ϫ˰Α Ϣϴ ϫϮ˰Χ ϲϣ ή̳ ϭ Ϣϴϧί ϲϣ ̮ϴ Η έ value ϪϨϳ ΰ̳ Ϣϴ Ϩ̯ έήϗήΑ ΎΠϨϳ έΩ Ϫ̯ ϢϴϨ̯ ϲϣ ϩΩΎϔΘγ Range ϪϨϳΰ̳ ί ϢϴϨ̯ ϒϳήόΗ Range<10 1 10<Range<20 2 20<Range<30 3 Range>30 4
4
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Ʊřźƿř żƿř ƺŤǀŤƀƳř
Ϣϴ ˰Ϩ̯ ϲϣ ϒϳήόΗ value έΩ Ϣθη ϥϮΘγ έΩ Gfuel ήϴϐΘϣ ϒϳήόΗ ΖϤδϗ έΩ β̢γ ϭ Ϣϴ ϧί ϲ ϣ έ add ϭ ˮΖδϴ̩ ̊ ϭ ˺ˬ˻ˬ˼ ΩΪϋ 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˰Η Ϣϴ ˰Ϩ̯ ϲ ˰ϣ ϪΒ˰γΎΤϣ fuelήϴϐΘϣ ϱήΑ έ ϱέΎϣ ϱΎϫ κΧΎη ϭ Gfuel ήϴϐΘϣ ϱήΑ έ ϲϧϭήϓ ϝϭΪΟ ϝΎΣ ΪηΎΑ ϪΘηΩ ϲϨόϣ
co
ƾūƹźų ƹ ŪƿŚŤƳ Śƿ Ţſř ƮƸƯ šŚƗLjƏř ŵƹŹƹ ƾƬƇř ƶŰƠƇ ƵŶƳźǀĭ ƁŹřżĭŵ źƟ ƽřźŝ æƩřƹŶſ
e.
Ʈǀƴĩ ũŹŵ ƾƬƇř ƶŰƠƇ Źŵ řŹŚƷ Ƶŵřŵ ƮǀƳřƺţ ƾƯ ŹƺƐģ ƮǀƃŚŝ ƶŤƃřŵ řŹ ƾƳřƹřźƟ ƩƹŶū źĭř çƩřƺŘſ
.K
et
ab
lin
ŚƸƷƹźĭ ŵřŶƘţ źƔƳ Żř ƹ ƶƬƇŚƟ ƩƺƏ źƔƳ Żř Ţƀǀģ ŚƷ Ƶŵřŵ ƽřźŝ ŮǀŰƇ ƽŶƴŝ ƵŵŹ ƶƤƿźƏ èƩřƺŘſ
w w
w
5
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Ʊřźƿř żƿř ƺŤǀŤƀƳř
íë ææ çê ± ¹Á{ Ä ¸m ƾƳřƹřźƟ ƩƹŶū X ŚƷ źǀƜŤƯ
f i ƾƳřƹřźƟ
ƮƬĜƿŵ æ
fi
f
ƾŞƀƳ ƾƳřƹřźƟ Śƿ ƩŚưŤůř
ƾƘưŬţ ƾƳřƹřźƟ
ƾƘưŬţ ƾŞƀƳ ƾƳřƹřźƟ
êå
å ç
i
ƮƬĜƿŵ ơƺƟ ç žƳŚƀǀƫ è …………….…… ……………………………………….. žƳŚƀǀƫ ơƺƟ é řźŤĩŵ ê
e.
co
m
Ζγ ϝΎϤΘΣ ϥΎϤϫ Ϫ̯ Ζγ ΕϻΎΣ Ϟ̯ ϪΑ ΏϮϠτϣ ΕϻΎΣ ΩΪόΗ ϩΪϨϫΩ ϥΎθϧ ϲΒδϧ ϲϧϭήϓ ϖ˰Βσ ή˰̳ ά˰ϟ ϥ Ϊ˰λέΩ ̶˰όϤΠΗ ̶Βδϧ ̶ϧϭήϓ ϭ ΪηΎΑ ̶ϣ ΩϮΧ ί ϞΒϗ ΕϻΎΣ ωϮϤΠϣ ̶όϤΠΗ ̶ϧϭήϓ ΪϨΘδϫ βϧΎδϴϟ ήҨί ΕϼϴμΤΗ ̵έΩ ˻˹ ΎҨ ήϔϧ ̋˹ Ϫ̯ ΩϮη ̶ϣ κΨθϣ βϧΎδϴϟ ̵ήΑϻΎΑ ϝϭΪΟ Ζγ ϩΪη Ωέϭ missing Ϫ̯ Ζγ ̶ҨΎϫ ϩΩΩ ϪΑ ρϮΑήϣ Invalid Percent ϥϮΘγ
w w
.K
et
ab
lin
˼ϝΎΜϣ έ ̶Ϡ˰λ ϩΩΩ ϝϭΪ˰Ο ϝΎΣ ΪηΎΑ ̶ϣ ̶ϧϭήϓ ϝϭΪΟ ϦҨ Ϫ̯ Ζγ ήҨί Ρήη ϪΑ ϩέΩ ̮Ҩ ϥΎϨ̯έΎ̯ ΕϼϴμΤΗ Ζδϴϟ ΪϴϨ̯ ΩΎΠҨ ̭έΪϣ Ϊ̯ ΩΪόΗ ̶ϠϴμΤΗ ƮƬĜƿŵ æ ˺̋ ƮƬĜƿŵ ơƺƟ ç ˺̀ žƳŚƀǀƫ è ˺˹ žƳŚƀǀƫ ơƺƟ é ̋ řźŤĩŵ ê ˼
1 2
Name
madrak frequency
w
Ϣϴ˰Ϩ̯ ϒҨήόΗ έ ̶ϧϭήϓ ϡΎϧ ϪΑ ̵ήϴϐΘϣ ϢҨέϮΒΠϣ Ύϣ Ζγ ̶ϠϴμΤΗ ̭έΪϣ ϥ ϭ ϢҨέΩ ήϴϐΘϣ ̮Ҩ Ύϣ ΎΠϨҨ έΩ ϢϴϨ̯ Ωέϭ ϝϭΪΟ έΩ έ ΕΎϋϼσ ΎΗ Ύϫ ήϴϐΘϣ ΏΎΨΘϧ Type
Width
Decimal
Numeric Numeric
8 8
-
Label Value -
-
Missing -
column 10 10
Align left left
Measure Scale Scale
έή̰Η έΎΒ̰Ҩ ϡΪ̯ ήϫ ϥϮ̩ Ϊϧί ̶ϣ ̮Ҩ έ ϪϤϫ ϢϴϫΩ Ϟϴ̰θΗ ̶ϧϭήϓ ϝϭΪΟ madrak ήϴϐΘϣ ̵ήΑ ϥϻ ή̳ ήϴϐΘϣ ϪΑ έ ϥίϭ ϢϴϨ̯ ϒҨήόΗ έΩ ϥίϭ ήϴϐΘϣ ΪҨΎΑ β̡ ϢҨέΩ ϢϠ̢ҨΩ ήϔϧ ˺̋ Ύϣ Ϫ̯ ̶ΗέϮλ έΩ Ζγ ϩΪη ΖγέΩ ϪΠϴΘϧ ϭ ϢҨήϴ̳ ̶ϣ madrak ήϴϐΘϣ αΎγ ήΑ έ ̶ϧϭήϓ ϝϭΪΟ ̶ϟϭ ϢϴϫΩ ̶ϣ ΖΒδϧ ̶ϧϭήϓ ΩϮη ̶ϣ ϩΩΩ ϥΎθϧ Data – Weight Case Ϫ̯ ϢϴϤϬϓ ̶ϣ ϪΤϔλ ϦϴҨΎ̡ Ζγέ ΖϤγ έΩ Weight On ϪϧΎθϧ ΎΑ ϢϴϨ̯ ίΎΑ έ ϪΤϔλ ϦҨ Ϫ̯ ϥΎϣί ήϫ ΪϧέΩ ϥίϭ Ύϫ ϩΩΩ 6
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̊ϝΎΜϣ ΏΎδ˰Σ έ Ϊ˰ϣ Ζϴδ˰ϨΟ ή˰ϴϐΘϣ ̵ή˰Α ϭ Ϧϴ̴ϧΎ˰ϴϣ ϥίϭ ϭ Ϧ˰γ ήϴϐΘϣ ̵ήΑ ϢҨέΩ έ ϥίϭ ϭ Ϧγ ˬΖϴδϨΟ ήϴϐΘϣ Ϫγ ΪϴϨ̯
Ύϫ ήϴϐΘϣ ΏΎΨΘϧ
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˰Ο ̶ϟϭ Ζγ ϩΪη έή̰Η έΎΒ̰Ҩ ϩΩΩ ήϫ Ϫ̯ ΩϮη ̶ϣ κΨθϣ ̶ϧϭήϓ ϝϭΪΟ ϥΪη κΨθϣ ί β̡ έ Ύ˰ϫ ϩΩΩ ΪҨΎΑ Ϟ̰θϣ ϦҨ ϊϓέ ̵ήΑ ΪηΎΑ ̋ ί ήΘϤ̯ ϥ ̵Ύϫ ϩΩΩ ˻˹ ήΜ̯ΪΣ Ϫ̯ Ζγ ΏϮΧ ̶ϧϭήϓ ΩϮη ήΘθϴΑ ̶ϧϭήϓ ΎΗ ϢϴϨ̯ ̵ΪϨΑ ϩΩέ įŶƴŝ ƵŵŹ Ʃƹř ƁƹŹ Max Min 50 20 6 ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ ϝϮϣήϓ ϦҨ ί Ϧγ ήϴϐΘϣ ̵ΪϨΑ ϩΩέ ̵ήΑ 5 5 ϪϨҨΰ̳ ί ̵ΪϨΑ ϩΩέ ΩΎΠҨ ̵ήΑ ΪҨ ̶ϣ ΖγΩ ϪΑ ̌ ϩίΎΑ ϝϮσ ϭ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ ̋ έ ϩΩέ ΩΪόΗ Transform – recode- into different variable
ƵŵŹ ƵŶƴƿŚưƳ
İƳřƹřźƟ
.K
įŶƴŝ ƵŵŹ
et
ab
ϢϴϨ̯ ̶ϣ ϒҨήόΗ Age group ϡΎϧ ϪΑ έ ΪҨΪΟ ήϴϐΘϣ ̋̊ ̊́ ̊̀ ̊˺ ̊˹ ˼̊ ˼˼ ˻̀ ˻˹ ˻̌ ̶ϣ ϩΩΎϔΘγ Ζγ ϩΪη ̵ΪϨΑ ϩΩέ Ϫ̯ Age group ΪҨΪΟ ήϴϐΘϣ ί ̶ϧϭήϓ ϝϭΪΟ ϪΒγΎΤϣ ̵ήΑ ϪΘ̰ϧ ϩΩΎϔΘ˰γAge ̶˰ϨόҨ ̶˰ϟϭ ή˰ϴϐΘϣ ί Ϧϴ̴ϧΎ˰ϴϣ Ϟ˰Μϣ ̵έΎ˰ϣ ̵Ύ˰ϫ κΧΎ˰η ϪϴϘΑ ϪΒγΎΤϣ ̵ήΑ ̶ϟϭ ϢϴϨ̯ ϢϴϨ̯ ϦϴϴόΗ ϩΪϨҨΎϤϧ ϩΩέ ήϫ ̵ήΑ ή̴ϣ ΩέΪϧ ̶Ϩόϣ ϩΪη ̵ΪϨΑ ϩΩέ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ ήҨί Ωή̯ ϢϴϫϮΧ 20 26 23 2 ˻̀ ˼˼ 27 33 30 2 fi xi Ζγ ϡ i ϩΩέ ϩΪϨҨΎϤϧ x ˬϦϴ̴ϧΎϴϣ ϝϮϣήϓ ϖΒσ ϭ X i fi
˻˹ ˻̌
w
w w
ϢϴϨ̯ ̶ϣ κΨθϣ value ΖϤδϗ έΩ έ ϥ Variable view ΖϤδϗ έΩ Age group ΪҨΪΟ ήϴϐΘϣ ϒҨήόΗ ΎΑ
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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
Ϫ˰Α ̶˰ϧϭήϓ ϝϭΪ˰Ο Ϊ˰η Ϊ˰ϫϮΧ ήΘϬΑ ̶ϠΒϗ ϝϭΪΟ ί Ϫ̯ ϢϴϨ̯ ̶ϣ Ϣγέ ̶ϧϭήϓ ϝϭΪΟ ΪҨΪΟ ήϴϐΘϣ ̵ήΑ Ζ˰γΪΑ ̵ήΘϬΑ ̶ϧϭήϓ ϝϭΪΟ ΎΗ Ωή̯ ήΘ̳έΰΑ έ ϩΩέ ϝϮσ ϭ ήΘϤ̯ έ Ύϫ ϩΩέ ϥϮΘϴϣ ΩέΩ ̶̴ΘδΑ Ύϣ ̵Ύϫ ϩΩέ 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
įŶƴŝ ƵŵŹ ƭƹŵ ƁƹŹ
e.
Transform - visual Bander Śƿ visual Binning
w
w w
.K
et
ab
lin
ΩϮη ̶Ϥϧ ϡΎΠϧ Nominal ήϴϐΘϣ ̵ήΑ ̵ΪϨΑ ϩΩέ Ϫ̯ ΪϴηΎΑ ϪΘηΩ ΖϗΩ visual BinningΪҨΪΟ ϩήΠϨ̡ έΩ Ϣϴϧΰϴϣ continue ϭ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ έ weight ήϴϐΘϣ ϩΪη ίΎΑ ϩήΠϨ̡ έΩ ϞΤϣ ϭ ρΎϘϧ Make cutpoints ϪϤ̳Ω ϥΩί ΎΑ ϝΎΣ gweight ϢϴϨ̯ ̶ϣ Ωέϭ έ ΪҨΪΟ ήϴϐΘϣ ΐδ̩ήΑ ϭ Ϣγ ϢҨίΎγ ̶ϣ έ Ζδ̰η ΩέΩ ΩϮΟϭ Ζδ̰η ρΎϘϧ ϦϴϴόΗ ̵ήΑ ϩέ Ϫγ Equal Width Intervals ˺ Ϫ˰̯ ϢϴϫΪ˰ϴϣ Ζ˰γ ̊˹ Ύ˰ΠϨҨ έΩ ϭ ΪηΎΑ Ύϣ min έ ϝϭ ϪτϘϧ ̵Ϊ˰˰όΑ Ϣϴ˰Ϩ̯ Ωέϭ έ ˼̂ ̋ ΩΪ˰ϋ ̶̴Θ˰γϮϴ̡ ̵ή˰Α Ζ˰˰γ ή˰ΘϬΑ ϩΩέ ϝϮ˰σ ̶ϣϮ˰γ ϭ Ϣϴ˰Ϩ̯ ̶˰ϣ Ωέϭ ˼ Ϫ̯ Ζγ Ύϫ ϩΩέ ΩΪόΗ ΩΪ˰όΗ Ϊ˰ϴϫΪΑ έ ϩΩέ ϝϮ˰σ ή˰̳ Ϊδ˰ҨϮϧ ̶˰ϣ εΩϮΧ Ϫ̯ Ζγ ΖϤδϗ έΩ έ ϩΩέΩΪϋ ϦҨήΧ ϭΪϫΩ ̶ϣ εΩϮΧ έ ϩΩέ .ΪϫΪϴϣ ϥΎθϧ εΩϮΧ έ Last cutpoint location Equal Percentiles Based on Scanned Cases 2 έ Ωήϴ̴ϴϣ max-min ϪϠλΎϓ ί ϩΩέ ήϫ Ϫ̯ ̵ΪλέΩ ϭ ϩΩέ ΩΪόΗ ϢϴϫΪϴϣ Cutpoints at Mean and Selected Standard Deviation ˼ Based on Scanned Cases
ΪϨ̰ϴϣ ϦϴϴόΗ έΎϫ ϩίΎΑ έΎϴόϣ ϑήΤϧ Ϫγ ΎҨ ϭΩˬ̮Ҩ αΎγ ήΑ έ ϩΩέ n+1 ΩΪ˰όΗ Ζδ˰̰η Ϫ˰τϘϧ n ΩΪ˰όΗ ΩϮη ϩΩΎϔΘγ Ϫ̯ ϪϨҨΰ̳ Ϫγ ϦҨ ϡΪ̯ ήϫ ί ϝΎΣήϫ ϪΑ ϪΘ̰ϧ ΖηΩ ΪϫϮΧ ΪϧϮη ̶ϣ ήϫΎχ Ϣϫ Ύϫ ϩΩέ ϩήΠϨ̡ ϥΎϤϫ έΩ Make Label ϪϤ̳Ω ϥΩί ΎΑ 8
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
( έΎ˰ϴόϣ ϑή˰Τϧ ϭ) έΎ˰ϴόϣ ϑή˰Τϧ
Ϧϴ̴ϧΎ˰ϴϣ ήҨΩΎ˰Ϙϣ Ύ˰Α ̶˰ϨΤϨϣ ϦҨ ωΎϔΗέ ΩέΩ ρΎΒΗέ ϭ έΪ˰Ϙϣ ̮˰Ҩ ϝϮ˰Σ Ύ˰ϫ ϩΩΩ Ϣ̯ήΗ ϥΰϴϣ Ζγ Ϧϴ̴ϧΎϴϣ ί Ύϫ ϩΩΩ ̶̳ΪϨ̯ή̡ ϥΰϴϣ
lin
e.
co
m
ϥίϮ˰ϣ ζ˰ϧΩ Εή˰Ϥϧ ̶˰γέΩ ϥΎ˰ΤΘϣ ̮˰Ҩ έΩ ϝΎ˰Μϣ ̵ ϥϮϨϋ ϪΑ ΖϤ˰γ Ϫ˰Α Ϫ˰̩ ή˰ϫ ϭ Ϊ˰ηΎΑ ̶˰ϣ ή Θθ˰ϴΑ Ϧϴ̴ϧΎ˰ϴϣ ϑή˰σ ΐϠϏ έ Εή˰Ϥϧ Ϧ˰Ҩ Ϫ˰̯ ̵Ωή˰ϓ ΩΪ˰όΗ ϢҨϭή˰Α ζϴ˰̡ ϦϴҨΎ̡ ΎҨ ϻΎΑ ΕήϤϧ ̮˰Ҩ Ύ˰Α ϥϮΗ ̶ϣ ΖϟϮϬδΑ έ έΎΘϓέ ϦҨ ΩϮη ̶ϣ ήΘϤ̯ Ϊϧ ϪΘϓή̳ .Ωή̯ ϝΪϣ ϝΎϣήϧ ϊҨίϮΗ
ab
ŶƿŶū źǀƜŤƯ įƹŹ źŝ ƍƹźƄƯ šŚŞſŚŰƯ ƭŚŬƳř ƹ ŶƿŶū źǀƜŤƯ ŵŚŬƿř ƁƹŹ
w w
.K
et
ϢҨΩή̯ ̶ϣ ϒҨήόΗ έ ̵ΪҨΪΟ ήϴϐΘϣ Transform – recode- into different variable εϭέ ί ϩΩΎϔΘγ ΎΑ ϢҨέϭΎϴΑ ΖγΪΑ έ ̵ΪҨΪΟ ήϴϐΘϣ ϪΒγΎΤϣ εϭέ ΎΑ ϢϴϫϮΧ ̶ϣ ϝΎΣ ̶ϟϭ Transform – Compute Numeric ΖϤδ˰˰ϗ έΩ έ ϝϮ˰˰ϣήϓ ϭ Ϣϴ˰Ϩ̯ ̶˰˰ϣ ΝέΩ έ Ϊ˰˰ҨΪΟ ή˰˰ϴϐΘϣ ϡΎ˰˰ϧ Target Variabele ΖϤδ˰˰ϗ έΩ ΏΎδΣ ϡή̳ ΐδΣ ήΑ ̵ή̴ҨΩ ϥϮΘγ έΩ έ Weight ήϴϐΘϣ ϢϴϫϮΧ ̶ϣ ή̳ ˱ϼΜϣ ϢϴδҨϮϧ ̶ϣ Expression ϢϴϨ̯ ΏΎΨΘϧ έ ιΎΧ ̶όΑΎΗ ΎҨ Weight 1000 ϢϴδΑϮϨΑ έ ϝϮϣήϓ ϦҨ ̶ϧϮΗ ̶ϣ ϢϴϨ̯
w
ΩϮ˰η ̶Ϥϧ ήΟ ϝϮϣήϓ ϩΩΩ ήϴϴϐΗ ΎΑSPSS έΩ Ϫ̯ Ζγ ϦҨ Excel ΎΑ SPSS έΩ ̶δҨϮϧ ϝϮϣήϓ ϕήϓ ϪΘ̰ϧ ή˰ϴϴϐΗ έ ϩΩΩ ϩΎ˰̳ ή˰ϫ Excel έΩ ̶˰ϟϭ ΩϮ˰η ή˰Ο ϝϮ˰ϣήϓ ˱ΩΪ˰Πϣ ΖδҨΎΑ ̶ϣ ϭ ΩΩή̳ ̶Ϥϧ ίϭέ ϪΑ ϪΠϴΘϧ ϭ ΪϨ̯ ̶ϣ ήϴϴϐΗ ΰϴϧ ϪΠϴΘϧ ϢϴϫΩ Ϊ˰Σϭ ή˰ϴϴϐΗ ˱ϻΎΜϣ ΩϮη ϡΎΠϧ ̶λΎΧ ̵Ύϫ ϩΩΩ ̵ήΑ ςϘϓ ϭ ̶λΎΧ ςҨήη ΖΤΗ ϝϮϣήϓ ϦҨ ϢϴϫϮΨΑ ή̳ ˱ϼΜ˰ϣ Ϣϴ˰Ϩ̯ ϩΩΎϔΘ˰γ Compute ϩή˰ΠϨ̡ ϦϴҨΎ˰̡ if Ϫ˰Ϥ̳Ω ί ΪҨΎΑ ΩϮη ϡΎΠϧ ΎϬϤ ϧΎΧ ̵ήΑ ςϘϓ ϡή̳ ϪΑ ϥίϭ Gender =1& Age>30 ΎҨ Gender =1 Ϫ̯ ϢҨέΰ̴Α ρήη ̵ή˰Α ϭ ϢҨ έά˰̴Α ˺ ̶˰λΎΧ ̵Ύ˰ϫ ϩΩΩ ̵ήΑ ϢϴϨ̯ ϒҨήόΗ select ϡΎϧ ϪΑ ήϴϐΘϣ ̮Ҩ ϢϴϧϮΗ ̶ϣ Ϫ̰ϨҨ ή̴ҨΩ ϩέ Ύϫήϔλ ̵ήΑ ΎҨ ϢϴϨ̯ ΏΎδΣ ΎϬ̰Ҩ ̵ήΑ έ ϝϮϣήϓ ΪόΑ ˹ ή̴ҨΩ ̵Ύϫ ϩΩΩ
9
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř SPSS Źŵ ƵŶƃ řźūř šřŹƺŤſŵ ƕřƺƳř ŢŞŧ ƁƹŹ
̶ϣ ΝέΩ Ύϣ ϥΎϣήϓ ϭ ϩΪη ίΎΑ syntax ϩήΠϨ̡ ˬ ΕέϮΘγΩ ί ̵ ϩήΠϨ̡ ήϫ έΩ pasteϪϤ̳Ω ί ϩΩΎϔΘγ ΎΑ ϊϗϮϣ Ζγ ήΘϬΑ Ωή̯ ήΟ ˱ΩΪΠϣ έ Ϧϴϣήϓ Run έϮΘγΩ ΎΑ ϥϮΗ ̶ϣ ϩήΠϨ̡ ϦҨ ΩΪΠϣ ϥΩή̯ίΎΑ ΎΑ ̶ΘΣ ΩϮη ΩϮη ΝέΩ ϥ έΩ ΕέϮΘγΩ ΎΗ ΪηΎΑ ίΎΑ Ϣϫ syntax ϪΤϔλ spss ϪΤϔλ ϥΩή̯ ίΎΑ řżŬƯ įŚƷ İūƹźų ƱŵŹƹō ŢſŶŝ ƹ ŚƷ Ƶŵřŵ ĨǀĪƠţ
w
w w
.K
et
ab
lin
e.
co
m
Data view ϪΤϔ˰λ έΩ Ϊ˰ҨΪΟ ή˰ϴϐΘϣ Compute ϩή˰ΠϨ̡ ί ϩΩΎϔΘ˰γ ΎΑ Ϫ̯ ΖδϨҨ ̶ϠΒϗ ϩέ ΎΑ ϩέ ϦҨ ϕήϓ ϥΩή˰̯ ή˰ΘϠϴϓ ωϮ˰ϧ ̮˰Ҩ Ϧ˰Ҩ ϭ ΩϮ˰η ̶ϣ ϞλΎΣ ΕήϴϴϐΗ output ϭ ̶ΟϭήΧ έΩ ϩέ ϦҨ ΎΑ ̶ϟϭ ΩϮθϴϣ ΩΎΠҨ Ζγ ϝϭ ε ϭέ Data – Select Cases Ϫ˰Α Ϊ˰ηΎΑ ϝΎ˰όϓ All Cases ή˰̳ Ϫ˰̯ ΩϮ˰η ̶ϣ ίΎΑ ϭήΑϭέ ϩήΠϨ̡ if condition is ϥΩή˰̯ ϝΎ˰όϓ Ύ˰Α ϭ Ζ˰γ ή˰ΘϠϴϓ ϡΪ˰ϋ ̵Ύ˰Ϩόϣ ̶˰ϣ ρή˰η ˱ϼΜ˰ϣ Ϣϴ˰Ϩ̯ ̶ϣ ΝέΩ έ ήψϧ ΩέϮϣ ρήη satisfied ϡΎ˰Πϧ Ύ˰Α ϝΎ˰Σ ή˰̳ Ϫ˰̯ Ϊ˰Ϩ̯ ϪΒ˰γΎΤϣ έ ΎϫΩήϣ ςϘϓ ϢҨέά̳ ϭ Ϊ˰Ϩ̰ϴϣ ϪΒ˰γΎΤϣ ΎϫΩήϣ ̵ήΑ ςϘϓ ϢҨήϴ̴Α Ϧϴ̴ϧΎϴϣ ή̳ ήΘϠϴϓ ϭ Ϊθ˰˰̯ ̶˰˰ϣ ς˰˰Χ έΎ˰˰Ϭϧί ̵Ύ˰˰ϫ ΩέϮ˰˰̯έ Ϣ˰˰ϫ Data view έΩ ̵ή˰˰Α ϥΪ˰˰η ΏΎ˰˰ΨΘϧ ϥ έΩ Ϫ˰˰̯ Ϊ˰˰Ϩ̯ ̶˰˰ϣ ΩΎ˰˰ΠҨ ̶ϧϮΘ˰˰γ ΪϫΩ ̶ϣ ϥΎθϧ έ ΕΎΒγΎΤϣ ΪϨ̯ ̶ϣ ΏΎΨΘϧ ̶ϓΩΎμΗ ϪϧϮϤϧ ̮Ҩ Random of cases ΏΎΨΘϧ ΎΑ ϪΤϔ˰λ ϦϴҨΎ˰̡ έΩ ϭ ΩϮ˰η ̶˰ϣ ϡΎΠϧ ήΘϠϴϓ ok ϥΩίΎΑ ϭ use filter available έΩ ήΘϠϴϓ ΩέϮϣ ήϴϐΘϣ ΏΎΨΘϧ ΎΑ Ϊη ΪϫϮΧ ϩΩΩ ζҨΎϤϧ filter on ΰϴϧ ϡϭΩ ε ϭέ ΪόΑ ϢҨέΩ ̶ϣ ήΑ All Cases ϪϨҨΰ̳ ϥΩί ϭ Data – Select Cases εϭέ ΎΑ έ ̶ϠΒϗ ̵Ύϫ ήΘϠϴϓ ϝϭ Data – Split Files Ζγ ήΘϠϴϓ ϡΪϋ ϝϭ ϪϨҨΰ̳ ̶˰˰ΟϭήΧ έΩ ̵ Ϫδ˰˰ҨΎϘϣ Ζ˰˰ϟΎΣ έΩ έ ϝϭΪ˰˰Ο ϡϭΩ Ϫ˰˰ϨҨΰ̳ ΪϫΩ ̶ϣ ϥΎθϧ Ϊη ΪϫϮΧ ϩΩΩ ϥΎθϧ ϪϧΎ̳ΪΟ ˱ϼϣΎ̯ ϡϮγ ϪϨҨΰ̳ ϥΎθ˰ϧ Ύ˰Ϭϧί ϭ Ύ˰ϫΩήϣ ΖϤδ˰ϗϭΩ Ϫ˰Α έ gender ήϴϐΘϣ Ϫ̯ ΩΩ ΪϫϮΧ
10
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
ΪϴϨ̯ ίΎΑ ήҨί ήϴδϣ ί έ Demo ϞҨΎϓ 5ϝΎΜϣ My computer – win(programming) - program files - spssEVAL – Tutorial – sample files- Demo ϭ Ύ˰ϫ ϢϧΎ˰Χ Ϊ˰ϣέΩ ϥΰϴϣ Ϧϴ̴ϧΎϴϣ β̢γ ΪҨέϭ ΖγΩ ϪΑ Ύϫ ήϴϐΘϣ ΩΪόΗ ˬ Ύϫ case ΩΪόΗ ΩέϮϣ έΩ έ ϡίϻ ΕΎϋϼσ ή˰Α ̶˰ϧϭήϓ ϝϭΪ˰Ο Ϊ˰ҨέϭΎϴΑ ΖγΪΑ ϦϴηΎϣ ωϮϧ αΎγ ήΑ έ ΪϣέΩ ̶ϧϭήϓ ϝϭΪΟ ϭ ΪϴϨ̯ ΏΎδΣ ϪϧΎ̳ΪΟ έ ϥΎҨΎϗ ΪҨέϭΎϴΑ ΖγΪΑ ϩΪη ̵ΪϨΑ ϩΩέ ΪϣέΩ αΎγ
ƪƿŚƟ Ĩƿ Żř šŚƗLjƏř Ÿųř File – display data files information ϝϭ εϭέ Analyze-Report – Case Summaries ϡϭΩ εϭέ ϪϧΎ̳ΪΟ Ϟ̰η ϪΑ ΎϬϧί ϭ ΎϫΩήϣ ΪϣέΩ ϥΰϴϣ Ϧϴ̴ϧΎϴϣ ̵ήΑ ϒϟ
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Data – Split Files – gender- organize output by groups- ok Ϧϴ̴ϧΎϴϣ ϪΒγΎΤϣ ̵ήΑ ̶ϟ ϭ ϢϴϨ̯ ̶ϣ ΏΎδΣ inccat ΎҨ ΪϣέΩ ήϴϐΘϣ ̵ήΑ ̶ϧϭήϓ ϝϭΪΟ β̢γ ok β̢γ ϭ Ϣϴϧί ̶ϣ ̮ϴΗ έ Mean ϪϨҨΰ̳ statistics ΖϤδϗ έΩ income ήϴϐΘϣ ΐδΣ ήΑ έ ̶ϧϭήϓ ΩϮη ̶Ϥϧ ϪΒγΎΤϣ ϩΪη ̵ΪϨΑ ϩΩέ ήϴϐΘϣ ̵ήΑ Ϧϴ̴ϧΎϴϣ 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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
ϦϴηΎϣ ωϮϧ ΐδΣ ήΑ ̶ϧϭήϓ ϝϭΪΟ Ώ ϦҨ ςγϮΗ ΪϫΪϴϣ ϥΎθϧ έ ϦϴηΎϣ ωϮϧ Ϫ̯ carcat ήϴϐΘϣ β̡ ϢϴϨ̯ ήΘϠϴϓ έ ϦϴηΎϣ ωϮϧ ΪҨΎΑ ˱ϻϭ Data – Split Files –carcat - organize output by groups- ok έϮΘγΩ ϢϴϨ̯ ̶ϣ ΏΎδΣ έ inccat ΎҨ ϩΪη ̵ΪϨΑ ϩΩέ ΪϣέΩ ήϴϐΘϣ ̶ϧϭήϓ έϮΘγΩ ΪόΑ ϢϴϨ̯ ̶ϣ ήΘϠϴϓ 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
ƵŶƃ źŤƬǀƟ Śƿ split ŢƫŚů Źŵ carcat źǀƜŤƯ Śƿ ƲǀƃŚƯ ƕƺƳ ŽŚſřźŝ inccat źǀƜŤƯ Śƿ ƵŶƃ įŶƴŝ ƵŵŹ ŶƯōŹŵ İƳřƹřźƟ ƶŞſŚŰƯ
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
ŶƳŹřŵ ŹLJŵ çêååå źƿŻ ŶƯōŹŵ ŶƳŹřŵ Economy ƩŶƯ ƲǀƃŚƯ ƕƺƳ ƶĩ ëè í æ
ŶƳŹřŵ ŹLJŵ ÔÒÍÍÍ įLJŚŝ ŶƯōŹŵ Ŷƴƴĩ İƯ ƵŵŚƠŤſř Luxury ƲǀƃŚƯ ƕƺƳ Żř ƶĩ ÔÒ Ï Ï
12
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
ƵŶƃ źŤƬǀƟ Śƿ split ŢƫŚů Źŵ martial źǀƜŤƯ Śƿ ƪƷŚţ ƕƺƳ ŽŚſřźŝ inccat źǀƜŤƯ Śƿ ƵŶƃ įŶƴŝ ƵŵŹ ŶƯōŹŵ ƲǀĮƳŚǀƯ ƶŞſŚŰƯ 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.
ŶƳŹřŵ ŹLJŵ çêååå éîååå Ʋǀŝ ŶƯōŹŵ ŶƴŤƀƷ ŵźŬƯ ƶĩ èí æ æ
.K
et
ab
lin
ŶƳŹřŵ ŹLJŵ ìêååå įLJŚŝ Ʋǀŝ ŶƯōŹŵ ŶƴŤƀƷ ƪƷŚŤƯ ƶĩ çë í Ï
w w
Married
Valid
Frequency 578
w
Marital status Unmarried
13
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
agegroup ΪҨΪΟ ήϴϐΘϣ ΩΎΠҨ ϭ Ϧγ ήϴϐΘϣ ̵ΪϨΑ ϩΩέ Transform-Visual binning – cutpoint 17.5 ƵŶƃ źŤƬǀƟ Śƿ split ŢƫŚů Źŵ agegroup źǀƜŤƯ Śƿ İƴſ ƵŵŹ ŽŚſřźŝ inccat źǀƜŤƯ Śƿ ƵŶƃ įŶƴŝ ƵŵŹ ŶƯōŹŵ ƲǀĮƳŚǀƯ ƶŞſŚŰƯ 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 ŹLJŵ ìêååå įLJŚŝ ŶƯōŹŵ êå ĨƿŵżƳ éí êí İƴſ ƵŵŹ Źŵ ƶĪǀƫŚů Źŵ ŶƳŹřŵ ŶƯōŹŵ ŹLJŵ çêååå źƿŻ êç è æî çí İƴſ ƵŵŹ Źŵ æ . ŶƳŹřŵ
14
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
íë æç ç ± ¹Â Ä ¸m ƶƳƺưƳ ƭŚƀƣř
w
w w
.K
et
ab
lin
e.
co
m
Ϫ̯ Ζγ ϪόϣΎΟ ̮Ҩ ί ήμϨϋ n ϞϣΎη ϩΩΎγ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ˺ ΩέΩήμϨϋ ήϫ ΏΎΨΘϧ ̵ήΑ ̵ϭΎδϣ βϧΎη ΎϬϴ̳̬Ҩϭ ̶ϓΩΎμΗ ζҨΎϣί ΩέΩ ̶ҨΎΗ n ϪϧϮϤϧ ήϫ ΏΎΨΘϧ ̵ήΑ ̵ϭΎδϣ βϧΎη ϝϭΪΟ ί ϩΩΎϔΘγ ΎΑ Ϣ̯ ϪϨҨΰϫ ΎΑ ̵έά̳ ϩέΎϤη ϞΑΎϓ ΎҨ ϩΪη ̵έά̳ ϩέΎϤη ϪόϣΎΟ ϩΩΎϔΘγ ΩέϮϣ ̶ϓΩΎμΗ ΩΪϋ Ϣ̯ ϪϨҨΰϫ ϭ Ζϋήγ ϪΑ ̶γήΘγΩ ΖϴϠΑΎϗ ̵έΩ ΩϮη ̶ϣ ϞϣΎη έ ϪόϣΎΟ ήμϨϋ Ϧϴϣ I ήϫ Ϫ̯ Ζγ ̵ ϪϧϮϤϧ ̮ϴΗΎϤΘδϴγ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ˻ ΐγΎϨϣ ̶ϠϴΧ ήҨά̡ ΐϴΗήΗ ϊϣϮΟ ̵ήΑ Ζγ ήϔλ ήϴϏ ϭ ϡϮϠόϣ Ϫ̯ ϪόϣΎΟ ί ήμϨϋ ήϫ ϞϣΎη ̶ϨόҨ ί ̵ΩΪόΗ Ϫ̩ ϢϴϧΪΑ ϢϴϫϮΨϴϣ ˱ϼΜϣ ΩέΩ ΕϭΎϔΗ ˱ϼϣΎ̯ ̵ή̴ҨΩ ΎΑ ϪϧϮϤϧ ήϫ ̵ήϴ̳ ϪϧϮϤϧ ϦҨ έΩϭ Ζγ ̵Ύϫ Ϧ̳ϭ ϝϭ ̵ήϴ̳ ϪϧϮϤϧ έΩ ΪϧϮη ̶ϣ ΏήΧ ̶ΘϓΎδϣ ί ΪόΑ ΎΟέ ̵ήΑ ήϓΎδϣ έΎτϗ ̵Ύϫ Ϧ̳ϭ ̋ ˱ϼΜϣ ΩΪϋ ΕϭΎϔΗ ΎΑ ̵ή̴ҨΩ ̵Ύϫ Ϧ̳ϭ ϡϭΩ ̵ήϴ̳ ϪϣϮϤϧ έΩ ϭ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ έ ϭ ˺̋ϭ˺˹ϭ̋ N ϩΪη κΨθϣ ̵ήϴ̳ ϪϧϮϤϧ ϪϠλΎϓ k ΪόΑ ϩΩή̯ ̵έά̳ ϩέΎϤη N ΎΗ ˺ ί έ ϪόϣΎΟ εϭέ ϦҨ έΩ n Ϧϴϟϭ ϪΑ k ϥΩϭΰϓ ΎΑ ϭ ΖγΎϣ ϪϧϮϤϧ ΪΣϭ Ϧϴϟϭ Ϫ̯ ϩΪη ΏΎΨΘϧ k ΎΗ ˺ ί ϑΩΎμΗ ϪΑ ΩΪϋ ̮Ҩ β̢γ ΪҨ ̶ϣ ΖγΪΑ Ϣϫ ΎϫΪΣϭ ήҨΎγ ˬ ΪΣϭ ̮Ҩ ΏΎΨΘϧ ϭ ΰҨΎϤΘϣ ̵Ύϫ ϪϘΒσ ϪΑ ϱέΎϣ ϪόϣΎΟ ̮Ҩ ϢϴδϘΗ ϩΪη ̵ΪϨΑ ϪϘΒσ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ˼ ϪϘΒσ ήϫ ί ̶ϓΩΎμΗ ϪϧϮϤϧ ΎҨΰϣ ϪϘΒσ ήϫ έΩ ̶ϓΎ̯ ήλΎϨϋ ΩϮΟϭ ί ϥΎϨϴϤσ ϪόϣΎΟ ̵ΎϫήΘϣέΎ̡ ί ήΘϬΑ ̵Ύϫ ϦϴϤΨΗ ϥΩέϭ ΖγΪΑ ϪΑ έ ΎϬϧΎδϧ ή̳ ̶ϟϭ ΪҨ ̶ϣ ΖγΪΑ ̵ ϪΠϴΘϧ ̮Ҩ ϢҨήϴ̴Α έ ϪόϣΎΟ ̮Ҩ ̵ΎϬϧΎδϧ Ϊϗ ςγϮΘϣ ή̳ ˱ϼΜϣ ϪΠϴΘϧ ϢϴϨ̯ ϪΒγΎΤϣ ϩΪη ̵ΪϨΑ ϪϘΒσ ̵ήϴ̳ ϪϧϮϤϧ έΩ έ Ϊϗ ςγϮΘϣ ϭ ϢϴϨ̯ ϢϴδϘΗ Ωήϣ ϭ ϥί ϪϘΒσ ϭΩ ΪҨ ̶ϣ ΖγΪΑ ̵ήΘϬΑ ϦҨ ΪηΎΑ ϪϘΒσ ήϫ ϞΧΩ έΩ ήλΎϨϋ ϑϼΘΧ ί ήΘθϴΑ ΕΎϘΒσ ϦϴΑ ήλΎϨϋ ϑϼΘΧ Ϫ̯ ̶ΗέϮλ έΩ ̶Ϡ̯ Ϟλ ΪϫΩ ̶ϣ έ ̵ήΘϘϴϗΩ ΞҨΎΘϧ ϩΩΎγ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ εϭέ ϪΑ ΖΒδϧ εϭέ Ϟϴϣ ϝΎϣήϧ ΖϤγ ϪΑ ϥ ϊҨίϮΗ ΩϮη ̱έΰΑ ϪϧϮϤϧ ϩίΪϧ ή̳ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ήϫ έΩ ̵ΰ̯ήϣ ΪΣ Ϫϴπϗ Ωή̯ ΪϫϮΧ Ω ̯ ϫϮΧ ϥΩϮΑ Ϧ̴Ϥϫ ρήη Ϫ̯ Ζγ ϪόϣΎΟ ήλΎϨϋ ί ̶ҨΎϫ ϪηϮΧ ΏΎΨΘϧ ̵ ϪηϮΧ ̶ϓΩΎμΗ ̵ήϴ̳ ϪϧϮϤϧ ̊ ΩϮη ΖҨΎϋέ ΪҨΎΑ ϥ 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 ϲϳΎϴϓήϐΟ ϱΎϫ ϱΪϨΑ ϢϴδϘΗ ˬΕΎϗϭ ϥΎϴΑ ϪΑ. ΪϨηΎΑ ϲϧΎηϮ̡ Ϣϫ ΪϗΎϓ ϭ ΪϨϫΩ ζηϮ̡ έ ϪόϣΎΟ Ϟϛ ΪϳΎΑ Ύϫ ϪηϮΧ. ΪϨϨϛ ϲϣ ϱΪϨΑ ϢϴδϘΗ ΪηΎΑ ϪΘηΩ ϖϠόΗ Ύϫ ϪηϮΧ Ϧϳ ί ϲϜϳ ςϘϓ ϭ ϲϜϳ ϪΑ ΪϳΎΑ ϪόϣΎΟ ήλΎϨϋ ί ϡΪϛ ήϫ ή̴ϳΩ ˬϱ ϪϠΣήϣ ΪϨ̩ Ύϳ Ϛϳ ϱ ϪηϮΧ ϱήϴ̳ ϪϧϮϤϧ ˬ ϩΪϴ̪ϴ̡ ϱήϴ̳ ϪϧϮϤϧ Ρήσ SPSS ϱέΰϓ ϡήϧ ϱ ϪΘδΑ .ΪϨϛ ϲϣ Ϣϫήϓ Complex Samples ΎΑ έˬϱΪϨΑ ϪϘΒσ ϱήϴ̳ ϪϧϮϤϧ ŵŹřŵ İĮŤƀŝ İƬƯřƺƗ ƶģ ƶŝ ƶĪƴƿř ƹ ŶǀƷŵ ŮǀƋƺţ řŹ ƶƳƺưƳ ƵŻřŶƳř ƲŤƟŚƿ įŚƷ ƁƹŹ ƩřƺŘſ 15
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř Crosstabs İƤƟřƺţ ƩƹřŶū
έΩ έ ή˰Ҩί ̶ϘϓϮ˰Η ϝϭΪ˰Ο Ωϭέ ̶˰ϣ έΎ˰̰Α ̶Ϥ˰γ ̵Ύ˰ϫ ή˰ϴϐΘϣ ϦϴΑ ρΎΒΗέ ϩΩΩ ϥΎθϧ ̵ήΑ ̶ϘϓϮΗ ϝϭΪΟ ΪϴϨ̯ Ωέϭ έΰϓ ϡήϧ ϪΤϔλ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ έ Ϟϐη ϭ ΖϴδϨΟ ήϴϐΘϣ ϭΩ ƱŻ ŵźƯ ϢϴϨ̯ ̶ϣ ϒҨήόΗ ϥΩΩ ϥίϭ ̵ήΑ ΰϴϧ έ ΩΪόΗ ήϴϐΘϣ Ϫ̯ ΏΎΨΘϧ έΎΒ̰Ҩ ςϘϓ έ ΖϟΎΣ ήϫ Ύϫ ϩΩΩ Ωϭέϭ ϪΤϔλ έΩ ΪόΑ ˺̋ ̋ ˻˹ ŹŚĪǀŝ Male – jobless ϢϴϨ̯ ̶ϣ ˺˹ ˻˹ ˼˹ 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 ϩΩΩ ϪΑ Data – Weightcases – frequency ̵ϮϨϣ ί ϩΩΎϔΘγ ΎΑ β̢γ ϮϨϣ ί ̶ϘϓϮΗ ϝϭΪΟ Ϟϴ̰θΗ Analyze –Descriptive Statistics- Crosstabs έΩ ήҨί ϝϭΪΟ Ϫ̯ ϢҨέά̴Α ̶Ϥγ ΎҨ Nominal ήϴϐΘϣ ϭΩ ΪҨΎΑ ϩΪη ίΎΑ ϩήΠϨ̡ ϥϮΘγ ϭ ήτγ ΖϤδϗ έΩ ΩϮη ̶ϣ ήϫΎχ output
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
ήϴϐΘϣ ϭΩ ϝϼϘΘγ Ϫϴοήϓ Ϫ̯ ϭΩ ̶Χ ϡΎϧ ϪΑ ̶ϧϮϣί ί ϥϮΘϴϣ crosstabs ϩήΠϨ̡ έΩ statistics ϪϤ̳Ω ϥΩί ΎΑ ΩϮϤϧ ϩΩΎϔΘγ ˬΪϨ̰ϴϣ ϥϮϣί έ ̶Ϥγ 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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
ϥΎθϧ ήϴϐΘϣ ϭΩ ϝϼϘΘγ ̵ήΑ έ ̵έΩ ̶Ϩόϣ τγ Ϫ̯ ϢϴΘδϫ Asymp. Sig. (2-sided) ϥϮΘγ ϝΎΒϧΩ Ύϣ ΪηΎΑ ̶ϣ ήϴϐΘϣ ϭΩ ̶̴ΘδΑϭ ̶Ϩόϣ ϪΑ Ϫ̯ Ζγ Ϣ̯ έΎϴδΑ ΎΠϨҨ έΩ ΪϫΩ ̶ϣ ΩϮη ̶ϣ ΏΎδΣ ϝϮϣήϓ ϦҨ ςγϮΗ ΪηΎΑ ̶ϣ ̶Ϥγ ήϴϐΘϣ ϭΩ ϝϼϘΘγ ϥϮϣί Ϫ̯ ϭΩ ̶Χ ϥϮϣί Oi ϩΪη ϩΪϫΎθϣ Ei έΎψΘϧ ΩέϮϣ
(Oi Ei )2 2 Ϊϧ ϪΘδΑϭ Ei
(Oi Ei )2 2 Ϊϧ ϞϘΘδϣ Ei ƶƳ Śƿ ŵŹřŵ ŵƺūƹ İƏŚŞţŹř įŻŚŝ śŚŞſř ƕƺƳ ƹ Ţǀƀƴū Ʋǀŝ Ŷǀƴĩ ƢǀƤŰţ ƩřƺŘſ
w
w w
.K
et
ab
lin
e.
co
m
̶ΟϭήΧ έΩ ϥϮΘγ ϭ ήτγ ήϴϴϐΗ ϩΩή̯ ̮ϴϠ̯ ϥ ̵ϭέ έΎΑ ϭΩ Ζγ ̶ϓΎ̯ ϢϴϨ̯ νϮϋ έ ϥϮΘγ ϭ ήτγ ̵ΎΟ ϢϴϫϮΨΑ ή̳ ̶ΟϭήΧ ϝϭΪΟ έΩ Ωή̯ νϮϋ έ ϥϮΘγ ϭ ήτγ ̵ΎΟ pivoting tray ϩήΠϨ̡ ϭ formatting toolbar έΰΑ έϮϧ ί ϩΩΎϔΘγ ΎΑ ϭ Ϊϴθ̯ ΰϴϧ split files ί ϩΩΎϔΘγ ϭ ̶ϠΒϗ ̵Ύϫ εϭέ ΎΑ ϥϮΘϴϣ έ ϝϭΪΟ ϦϴϤϫ Layer ήϴϐΘϣ ϪҨϻ ̮Ҩ ΩΎΠҨ ΩϮϤϧ ϩΩΎϔΘγ ̶ϘϓϮΗ ϝϭΪΟ έΩ ̶ϣϮγ ήϴϐΘϣ ί ϥϮΘϴϣ Ϫϫέ Ϫγ ϝϭΪΟ ̮Ҩ ΎΗ ϢϴϨ̯ ϪϓΎο έ ήϴϐΘϣ ϪҨϻ ̮Ҩ ̶ϨόҨ έ ϡϮγ ήϴϐΘϣ ΕήϴΛΎΗ ̶Θϗϭ Ϫ̯ ΪϫΩ ̶ϣ ϥΎθϧ ϭ ϢҨίΎδΑ ϥϮΘγ ϭ ήτγ ̵ΎϫήϴϐΘϣ ϦϴΑ ϪτΑέ ϪϧϮ̴̩ ϢϴϨ̯ ̶ϣ ϝήΘϨ̯ ΪΑΎҨ ̶ϣ ήϴϴϐΗ ϭ empcat ̶Ϡϐη ϖΑϮγ ϥΰϴϣ ϭ ΪϴϨ̯ ίΎΑ έ Demo ϞҨΎϓ ϪΑ έ genderΖϴδϨΟ ήϴϐΘϣ ϭ ΪҨέϭ ΖγΪΑ έinccat ΪϣέΩ ΪϴϨ̯ Ωέϭ ϡϮγ ήϴϐΘϣ ϥϮϨϋ έ ήτγ ΪλέΩ ϭ ϩΩί έ cell ϪϤ̳Ω crosstabs ϩήΠϨ̡ έΩ ή̳ ΪϫΩ ̶ϣ ϥΎθϧ ΰϴϧ ΪλέΩ ΎΑ έ ΩΪϋ ϢϴϨ̯ ϝΎόϓ 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 έϻΩ ̀̋˹˹˹ ̵ϻΎΑ Ϊϣ έΩ ϭ ϝΎγ ˺̋ ί ήΘθϴΑ ̶Ϡϐη ϪϘΑΎγ ϪΑ ρϮΑήϣ ΩΪόΗ ϦҨήΗϻΎΑ ΎϫΩήϣ ϭ ΎϬϧί ϩϭή̳ έΩ ϪΠϴΘϧ Ζγ έϻΩ ̀̋˹˹˹ ̵ϻΎΑ Ϊϣ έΩ ϭ ϝΎγ ̋ ί ήΘϤ̯ ̶Ϡϐη ϪϘΑΎγ ϪΑ ρϮΑήϣ ΩΪόΗ ϦҨήΗ ϦϴҨΎ̡ ΎϫΩήϣ ϭ ΎϬϧί ϩϭή̳ έΩ
17
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Ʊřźƿř żƿř ƺŤǀŤƀƳř
̶ϨόҨ Ζγ .000 ˬ Asymp. Sig. (2-sided) ϥϮΘγ ΩΪϋ Ϫ̯ ϢҨϮη ̶ϣ ϪΟϮΘϣ ϭΩ ̶Χ ϥϮϣί ΎΑ Ϊϧ ϪΘδΑϭ ϭΪϨҨ ϭ ΩέΪϧ ΩϮΟϭ ΪϣέΩ ϭ ̶Ϡϐη ϖΑϮγ ήϴϐΘϣ ϭΩ ϦϴΑ ̶ϟϼϘΘγ 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
̶ϟϭ ΪϫΩ ϥΎθϧ έ ΕϼϴμΤΗ ϥΰϴϣ ϭ ΖϴδϨΟ ϦϴΑ ρΎΒΗέ Ϫ̯ ΪϴϨ̯ Ϣγέ ̶ϟϭΪΟ Demo ϞҨΎϓ έΩ ϦҨήϤΗ έΩ ΎϬϧ ΪϣέΩ ςγϮΘϣ ϥΰϴϣ ΩΪόΗ ̵ΎΟ ϪΑ ΪϨ̯ ΝέΩ ϝϭΪΟ ̶ϘϓϮΗ ϝϭΪΟ ί ϥϮΗ ̶Ϥϧ ΎΠϨҨ έΩ ̶ηέΎϔγ ϝϭΪΟ ΪҨΎΑ Ϫ̰ϠΑ ΩϮϤϧ ϩΩΎϔΘγ Ωή̯ ΩΎΠҨ Analyze – Tables – Custom Table ήτγ έΩ ϭ ΖϴδϨΟ gender ϥϮΘγ έΩ ΪϣέΩ ήϴϐΘϣ ϭ edu ΎҨ ΕϼϴμΤΗ τγ έΩ ΪηΎΑ scale ΪҨΎΑ ˱ΎϤΘΣ Ϫ̯ income ϢҨέά̳ ̶ϣ count ΖϤδϗ ϥϮΘγ ϭ ήτγ ήϴϐΘϣ ϢϬϣ έΎϴδΑ ϪΘ̰ϧ ή̳ ΪηΎΑ ϩΪη ̵ΪϨΑ ϩΩέ ήϴϐΘϣ ΪҨΎΑ ˱ΎϤΘΣ nominal ϪΑ ήϧ Ζγέ ̮ϴϠ̯ ΎΑ ΩϮΒϧ ϪΒγΎΤϣ ΖϬΟ ήϴϐΘϣ ϭ ϢϴϨ̯ ̶ϣ ϞҨΪΒΗ ΪηΎΑ scale 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
έΩ summary statistics ϪϤ̳Ω ί ϩΩΎϔΘγ ΎΑ έ ̵ή̴ҨΩ ̵έΎϣ ̵Ύϫ κΧΎη Ϧϴ̴ϧΎϴϣ ̵ΎΟ ϪΑ ϥϮΗ ̶ϣ ΩϮϤϧ ϪΒγΎΤϣ ϩήΠϨ̡ ϥΎϤϫ 18
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Ʊřźƿř żƿř ƺŤǀŤƀƳř
carcat ΎҨ ϦϴηΎϣ ωϮϧ ϭ gender ΖϴδϨΟ ϦϴΑ ρΎΒΗέ Ϫ̯ ΪϴϨ̯ Ϣγέ ̶ϟϭΪΟ Demo ϞҨΎϓ έΩ ϦҨήϤΗ ϝϭΪΟ έΩ ΎϬϧ ΪϣέΩ ςγϮΘϣ ϥΰϴϣ ΩΪόΗ ̵ΎΟ ϪΑ ̶ϟϭ ΪϫΩ ϥΎθϧ έ Primary vehicle price category ΪϨ̯ ΝέΩ Analyze – Tables – Custom Table scale ΪҨΎΑ ˱ΎϤΘΣ Ϫ̯ income ΪϣέΩ ήϴϐΘϣ ϭ carcat ΎҨ ϦϴηΎϣ ωϮϧ ήτγ έΩ ϭ ΖϴδϨΟ gender ϥϮΘγ έΩ ˬϦϴ̴ϧΎϴϣ ̵Ύϫ κΧΎη summary statistics ϪϤ̳Ω ϥΩί ΎΑ ϭ ϢҨέά̳ ̶ϣ count ΖϤδϗ έΩ ΪηΎΑ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ Ϣϫ έ Ϊϣ ϭ ϢϤϴϨϴϣ ˬϢϤҨΰ̯Ύϣ 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
Nominal ϒϠΘΨϣ ̵ΎϬϫϭή̳ ϦϴΑ έΩ scale ήϴϐΘϣ ̮Ҩ ϪδҨΎϘϣ ˺ Ϣϫ ΎΑ ήΘθϴΑ ΎҨ scale ήϴϐΘϣ ϭΩ ϪҨΎϘϣ ˻ Ύϫ case ϡΎϤΗ ϦϴΑ έΩ ήϴϐΘϣ ̮Ҩ ϪδҨΎϘϣ ˼ ΪϴϨ̯ ΖγέΩ έ ΎϫέΩϮϤϧ ϦҨ ϦҨήϤΗ ϝϭ ωϮϧ έΩϮϤϧ ΖΧΎγ Graph Chart builder ˺ Ύϫ x έϮΤϣ έΩ Gender ήϴϐΘϣ ˻ Ύϫ y έϮΤϣ έΩ income ήϴϐΘϣ ˼ Clustering variable on ϪϨҨΰ̳ Groups/point id ϪϤ̳Ω έΩ ̊ ΪϴϨ̯ ΝέΎΧ ϥΩϮΑ ϝΎόϓ ί έ X ΎҨ Graph – Legacy dialogs – bar – variable ….. mean
ƎſƺŤƯ ƎſƺŤƯ
ŵźƯ
ŶƯōŹŵ
ƶƴƿżƷ
w
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.K
et
ŹLJŵ
80.00
Mean Household income in thousands
ŶƯōŹŵ ƎſƺŤƯ
lin
e.
ϦҨήΗϻΎΑ ΎΑ ϥΎҨΎϗ ̶ϟϭ ΪϧήΧ ̶ϣ έ β̯Ϯϟ ϦϴηΎϣ ϦҨήΘϬΑ έϻΩ έΰϫ ˺˹̀˹ ΪϣέΩ ϦҨήΗϻΎΑ ΎΑ ΎϬϤϧΎΧ ϪΠϴΘϧ ΪϧήΧ ̶ϣ β̯Ϯϟ ϦϴηΎϣέϻΩ έΰϫ ˺˺˺̌ ΪϣέΩ Ζγ ϥΎҨΎϗ ί ήΘϤ̯ ΪϧήΧ ̶ϣ β̯Ϯϟ ϦϴηΎϣ Ϫ̯ ̶ҨΎϫ ϢϧΎΧ ΪϣέΩ Ϧϴ̴ϧΎϴϣ ŹřŵƺưƳ ƕřƺƳř
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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Ʊřźƿř żƿř ƺŤǀŤƀƳř ϡϭΩ ωϮϧ ϝϭΪΟ ΖΧΎγ
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
ϡϭΩ έΩϮϤϧ ΖΧΎγ 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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íë æç æë ± ¹ ZÆq Ä ¸m ϞΒϗ ϪδϠΟ ΕϻϮΌγ ϪΑ ΦγΎ̡ ̮Ҩ έΩ έ ΪϨΘδϫ ̵ ϪϨҨΰ̳ ΪϨ̩ ϭ ΪϧέΩ ϥΎδ̰Ҩ ΦγΎ̡ Ϫ̯ ̶ҨΎϫήϴϐΘϣ Demo ϞҨΎϓ έΩ ϥϮΘϴϣ ϪϧϮ̴̩ ˺ ˮΩή̯ ̵έϭ ϊϤΟ ϝϭΪΟ Analyze – Tables – Custom Tables Ζγέ ̮ϴϠ̯ ΎΑ ΪΘΑ έ ΪϧέΩ ήϴΧ ΎҨ ϪϠΑ ΏϮΟ ςϘϓ Ϫ̯ ϥΎδ̰Ҩ ΦγΎ̡ ΎΑ ̵ΎϫήϴϐΘϣ ϪϴϠ̯ β̢γ ϝϭ εϭέ ΖϤγ ί β̢γ stacking ΖϟΎΣ ϪΑ ϩΩΩέήϗ ϝϭΪΟ ήτγ ΖϤδϗ έΩϭ ϩΩή̯ ϞҨΪΒΗ category ωϮϧ ϪΑ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ έ row labels in columns ϪϨҨΰ̳ category position ΖϤδϗ ϩήΠϨ̡ ϦϴҨΎ̡ Ζγέ
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
ϡϭΩ εϭέ
lin
Analyze – Tables –Tables of frequency
nd z.
nd 2 z 2 2
xx d
.K
xx x
ˮϢϴϨ̯ ϪΒγΎΤϣ ϪϧϮ̴̩ έ ϪϧϮϤϧ ϩίΪϧ ˻ ̶Ϥ̯ ήϴϐΘϣ ̵ήΑ ± ϒϟ ΩϭΪΤϣΎϧ ϪόϣΎΟ έΩ
et
z
ab
ΪϴϧΰΑέ ok ϭ ΪҨήΒΑ ϩήΠϨ̡ έΩ έΎϫήϴϐΘϣ ϪϤϫ
z 2 2 n 2 d
w
w w
x n d nd z Ζγ κΨθϣ ϪόϣΎΟήλΎϨϋ ΩΪόΗ N Ϫ̯ ΩϭΪΤϣ ϪόϣΎΟ έΩ n 2 2 Nz n ( N 1)d 2 z 2 2 z 1.64 ˬ 0.05 ̵ήΑ ̶ϔϴ̯ ήϴϐΘϣ Ώ ϥ ϦΘηΪϧ ϝΎϤΘΣ q=1-p ϭ ήψϧ ΩέϮϣ Ζϔλ ϦΘηΩ ϝΎϤΘΣ p Ϫ̯ ϢϴϫΩ ̶ϣ έήϗ έ pq ˬ ̵ΎΟ ϪΑ Ζγ Ζϔλ Nz 2 pq n ( N 1) d 2 z 2 pq 21
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Ʊřźƿř żƿř ƺŤǀŤƀƳř ƉźƟ ƱƺƯŻō
κΧΎη ϥϮϨϋ ϪΑ ϪϧϮϤϧ κΧΎη έΪϘϣ ΏΎΨΘϧ ϭ ϪϧϮϤϧ ΎΑ ΖϴόϤΟ κΧΎη ϪδϳΎϘϣ ϱήΑ νήϓ ϥϮϣί Ωϭέϲϣ έΎϛ ϪΑ ΖϴόϤΟ ϢϳϮη ϞϤΤΘϣ ϢϴϧϮΗϲϣ Ϫϛ Ζγ ϱΎτΧ ϥΰϴϣ ϭ ϪϧϮϤϧ ϭ ΖϴόϤΟ ϩίΪϧ ϪΑ ϪΟϮΗ ΎΑ ΏΎΨΘϧ Ϧϳ ΖϗΩ ϊϳίϮΗ ϪΑ ΖϴόϤΟ Ϫ̩ήϫ ΩϮΑ ΪϫϮΧ ήΘθϴΑ ϥϮϣί ΖϗΩ ΪηΎΑ ήΘθϴΑ ΖϴόϤΟ ϪΑ ΖΒδϧ ϪϧϮϤϧ ϩίΪϧ Ϫ̩ήϫ ΩϮΑ ΪϨϫϮΧ ήΗϥϮΗή̡ ΎϫϥϮϣί ΪηΎΑ ήΘϜϳΩΰϧ ϝΎϣήϧ ŚƷ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ įřźŝ ƱƺƯŻō śŚŴŤƳř Á
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ϪόϣΎΟ ϪΑ έ ϪϧϮϤϧ ΖϴλϮμΧ ϢϴϫϮΧ ̶ϣ ϭ ϢҨ ϩΩή̯ ΏΎΨΘϧ ϪϧϮϤϧ ̮Ҩ ϭ ϢҨέΪϧ έ ϪόϣΎΟ Ϫ̯ ̶ϧΎϣί ϦϴΑ ϑϼΘΧ ϥϮΘϴϣ ΕΎϴοήϓ ϥϮϣί ΖϴϠΑΎϗ ί ϩΩΎϔΘγ ΎΑ ϭ ϢϴϫΩ ̶ϣ ϡΎΠϧ ϥϮϣί άϟ ϢϴϫΪΑ ΖΒδϧ ΐ̰Ηήϣ ϢϴϨ̯ Ωέ έ ϥ Ύϣ ϭ ΪηΎΑ ΖγέΩ ̵έΎϣ νήϓ ή̳ ˬ νήϓ ϥϮϣί ϡΎΠϧ έΩ ΪϴΠϨγ έ Ύϫ Ϧϴ̴ϧΎϴϣ ϢҨϮη ̶ϣ ϡϭΩ ωϮϧ ̵ΎτΧ ΐ̰Ηήϣ ΪηΎΑ ΖγέΩΎϧ ϢϴΘϓήҨά̡ Ϫ̯ έ ̵έΎϣ νήϓ ή̳ ϭ ϝϭ ωϮϧ ̵ΎτΧ ̵ΎτΧ Ζγ ήΘϤ̯ ϡϭΩ ωϮϧ ̵ΎτΧ ϭ ΩϮΑ ΪϨϫϮΧ ήΗ ϥϮΗή̡ Ύϫ ϥϮϣί ΩϭήΑ ζϴ̡ ϝΎϣήϧ ϑήσ ϪΑ ϊҨίϮΗ Ϫ̩ήϫ ΪηΎΑ ΖγέΩ Ϫ̰ϨҨ ρήη ϪΑ H1 Ωέ ϝΎϤΘΣ ̶ϨόҨ ϡϭΩ ωϮϧ 2 ϡϮϠόϣ βϧΎҨέϭ ϭ ϝϮϬΠϣ Ϧϴ̴ϧΎϴϣ ΎΑ ϝΎϣήϧ ϊҨίϮΗ ί ̶ҨΎΗ n ϪϧϮϤϧ ̮Ҩ X 1 , X 2 ,..., X n ϢϴϨ̯ νήϓ Ζγ ϪόϣΎΟ Ϧϴ̴ϧΎϴϣ ΩέϮϣ έΩ ήҨί νήϓ ϥϮϣί ϑΪϫ ΪϨηΎΑ ΩέΩέήϗ Ζγ Ϫϓήσ ̮Ҩ ΐ̯ήϣ νήϓ ̮Ҩ Ϫ̯ H1 νήϓ ϞΑΎϘϣ έΩ ϩΩΎγ νήϓ ̮Ҩ H 0 νήϓ H0: 0 H 1: 0
22
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
έϮσ ϪΑ ϭ ΪϨ̯ ̶Ϥϧ ΕϭΎϔΗ ή̴ҨΩ ϪϧϮϤϧ ϪΑ ϪϧϮϤϧ ̮Ҩ ί ϥϮϣί ϪΠϴΘϧ ϭ Ζγ ήΘθϴΑ ϥϮΗ ϝΎϣήϧ ϊҨίϮΗ έΩ ΐϴΗήΗ ϪΑ Ϫ̯ ΩϮη ̶ϣ ϩΩΎϔΘγ ̵ήΘϣέΎ̡ ̵Ύϫ ϥϮϣί ί ϥΩϮΑ ϝΎϣήϧ νήϓ ΎΑ ̱έΰΑ ϊϣϮΟ έΩ ̶Ϡ̯ ΪϧέΩ Ϫϴ̰Η ΕΎϋϼσ ϦΘϓή̳έήϗ ̵ΎϬϧϮϣί ΪϨϨ̯ ̶ϣ ϩΩΎϔΘγ ̵ήΘϣέΎ̡Ύϧ ̵Ύϫ ϥϮϣί ί ̮̩Ϯ̯ ϭ ϝΎϣήϧ ήϴϏ ϊҨίϮΗ ΎΑ ̵Ύϫ ΖϴόϤΟ έΩ ΐΟϮϣ ϥΰϴϣ ϥΎϤϫ ϪΑ Ϫ̯ ή̴ҨΩ Ωή̰Ҩϭέ ΪϨϨ̯ ̶Ϥϧ ̶λΎΧ νήϓ ϥ βϧΎҨέϭ ϭ ϪόϣΎΟ ̵ΎϬόҨίϮΗ ϩέΎΑ έΩ ̵ήΘϣέΎ̡Ύϧ Ζγ ΪҨΪΟ ̵Ύϫ ϩΩΩ ϪϋϮϤΠϣ ΎΑ t ϥϮϣί ϥΩήΑ έΎ̯ ϪΑ ϭ Εή̡ ήҨΩΎϘϣ ϥΩή̯ ΝέΎΧ ΩϮη ̶Ϥϧ ϥϮΗ Ζϓ
ΪηΎΒϧ ϴΤλ Ϫ̯ ̶σήη ϪΑ Ζγ H0 ϥΩή̯ Ωέ ϝΎϤΘΣ ̵έΎϣ ϥϮϣί ̮Ҩ power ΎҨ ϥϮΗ
p value ή̳ Ζγ ϝΎϤΘΣ έΪϘϣ ΎҨ p value ί ϩΩΎϔΘγ ̵έΎϣ νήϓ ϥϮϣί ̵ΎϬηϭέ ί ̶̰Ҩ ΩϮη ̶ϣ Ωέ H 0 νήϓ έΪϘϣ Means ƵŶƃ įŶƴŝ ƵŵŹ źǀƜŤƯ Ĩƿ Źŵ İĭĦƿƹ Ŷƴģ Śƿ ƹŵ įřźŝ įŵŶƗ źǀƜŤƯ Ĩƿ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ įźŤƯřŹŚě ƱƺƯŻō
Mean Household income in thousands
w
w w
.K
et
ab
lin
e.
co
m
ΖϴόϤΟ ί ϩϭή̳ ϭΩ ϦϴΑ ϲΒγΎϨϣ ϪδϳΎϘϣ ϥϮΗϲϣ ΖϴόϤΟ ΰϛήϤΗ κΧΎη ϥϮϨϋ ϪΑ Ϧϴ̴ϧΎϴϣ ϪδϳΎϘϣ ΎΑ Ωέϭ ΖγΪΑ ϪϧϮϤϧ
ΖϴόϤΟ ϩΪϨϳΎϤϧ ϭ κΧΎη ϥϮϨϋ ϪΑ Ϊϣ ϭ ϪϧΎϴϣ ϞΜϣ ΰϛήϤΗ ϱΎϫέΎϴόϣ ή̴ϳΩ Ύϳ Ϧϴ̴ϧΎϴϣ Ϫϛ ϲϳΎΠϧ ί ϪδϳΎϘϣ έ ΎϬϧ ΰϛήϤΗ ϱΎϫέΎϴόϣ Ζγ ήΘϬΑ ΖϴόϤΟ ϭΩ Ύϳ ϲ̳̬ϳϭ ϭΩ ϪδϳΎϘϣ ϱήΑ ˬΪϨΘδϫ ϩΪη ϪΘΧΎϨη Ωήϛ Analyze – Compare Means – Means Dependent list ΖϤδϗ έΩ Ωέϭ έ ϩΪη ϱήϴ̳ ϩίΪϧ ϲ̳̬ ϳϭ Ύϳ ήϴϐΘϣ ϱΩΪϋ ήϳΩΎϘϣ ϱέΩ ΪϳΎΑ ήϴϐΘϣ Ϧϳ ΪϴϨϛ ΪηΎΑ Independent list ΖϤδϗ έΩ ϭ ϲϓήόϣ έ ϱΪϨΑϩϭ ή̳ Ύϳ ϱΪϨΑ ϪΘγΩ ήϴϐΘϣ αΎγήΑ ϲ̳̬ϳϭ Ϧϴ̴ϧΎϴϣ έΎϛ Ϧϳ ΎΑ ΪϴϨϛ ΖγΪΑ ϩϭή̳ ήϫ ϱήΑ ϱΪϨΑ ϪΘγΩ ήϴϐΘϣ ΪϧϮηϲϣ ϪδϳΎϘϣ ϭ ϩΪϣ ϞϫΎΗ Ζϴόοϭ αΎγήΑ ΪϣέΩ ϪδҨΎϘϣ ˱ϼΜϣ xx ΪηΎΑ ̶ϣ T Ϧϴ̴ϧΎϴϣ ϩέΎϣ s n ̵ΎϬϴ̳̬ϳϭ Á Statistics ϩέΎϣ έ ϩήϴϏ ϭ έΎϴόϣ ϑήΤϧ ˬϦϴ̴ϧΎϴϣ ϪϧϮϤϧ ̮ϳ ̵ΎϬϴ̳̬ϳϭ ϪΑ ρϮΑήϣ ήϳΩΎϘϣ ΪϨҨϮ̳ ̶ϣ Parameters ήΘϣέΎ̡ έ ̶Ϡλ ϪόϣΎΟ έΩ ΎϬϧ ϝΩΎόϣ Ϣϴθ̯ ̶ϣ ϞϫΎΗ Ζϴόοϭ αΎγ ήΑ ΰϴϧ έ income ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ έΩϮϤϧ 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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
ΎϫήϴϐΘϣ ̵ΪϨΑ ϪҨϻ Ωή̯ ̵ΪϨΑ ϪҨϻ ϥϮΘϴϣ ΖϴδϨΟ ϩήϤϫ ϪΑ έ ΖϴόϗϮϣ Mean ϞϤόϟέϮΘγΩ ί 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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
ab
lin
e.
co
m
̶ϣ box ̮Ҩ ήϴϐΘϣ ήϫ ̵ήΑ έΩϮϤϧ ωϮϧ ϦҨ έΩ Ζγ Boxplot έΩϮϤϧ ϦҨήΘϬΑ Ύϫ Ϧϴ̴ϧΎϴϣ ϪδҨΎϘϣ ̵ήΑ Ζγ ήΘθϴΑ Ύϫ ϩΩΩ ̶̳ΪϨ̯ή̡ ΪηΎΑ ήΘθϴΑ box ωΎϔΗέ Ϫ̩ ήϫ Ϫ̯ Ϊθ̯ 1 Max ήΗϻΎΑ ή̳ ϭ Ζγ ϥέΎϘΘϣ ΪηΎΑ box ςγϭ Ϧϴ̴ϧΎϴϣ ή̳ Ζγ ̶̴ϟϮ̩ ϪϧΎθϧ ΪηΎΑ ςγϭ ίήΗ ϦϴҨΎ̡ ΎҨ Q3 ϥ ˺ ϩέΎϤη Ϫ̯ Ζγ Εή̡ ϩΩΩ ̮Ҩ ϩΪϨϫΩ ϥΎθϧ Ζγ ˺ ϩέΎϤη case ϪΑ ρϮΑήϣ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ ΪϨҨϮ̳ ̶ϣ outliar Εή̡ ϩΩΩ ϪΑ ΪηΎΑ ϪΘηΩ έ ρήη ϦҨ Ϫ̯ ̵ ϩΩΩ ̶Ϡ̯ έϮσ ϪΑ Mean=median= ΩϮη ̶ϣ ΏΎδΣ Εή̡ Q2 Q Q3 Q1 Q1 1 Q Q3 Outliar Q1 1 Q 2 2 Min ΩϮΟϭ ΎΑ ΪϧέΩ ϪϠλΎϓ Q ήΑήΑ Ϫγ ί ήΘθϴΑ Ϫ̯ΪϨҨϮ̳ ̶ϣ Extreme variable ΰϴϧ Εή̡ ̶ϠϴΧ ̵Ύϫ ϩΩΩ ϪΑ ϢҨέϭΎϴϧ ΖγΪΑ ΖγέΩ έ ϥϮϣί ϪΠϴΘϧ ϭ ΩϮη ϞҨΎϤΘϣ ΎϬϧ ΖϤγ ϪΑ Ϧϴ̴ϧΎϴϣ Ζγ Ϧ̰Ϥϣ Εή̡ ̵Ύϫ ϩΩΩ ϢϴϨ̯ ϑάΣ έ Εή̡ ̵Ύϫ ϩΩΩ Ζγ ήΘϬΑ β̡ ̶ϟϭ ̶̳ΪϨ̯ή̡ ̶ϨόҨΪηΎΑ ϪΘηΩ ΩϮΟϭ ̶ϧΎηϮ̢Ϥϫ ή̳ ήϴϐΘϣϭΩ Ϧϴ̴ϧΎϴϣ ϦϴΑ ̵Ύϫ boxplot ϪδҨΎϘϣ έΩ ϡΎΠϧ έ ϥϮϣί ΩϮη ̶ϣ ϭ ΪηΎΑ ̶Ϥϧ Ύϫ Ϧϴ̴ϧΎϴϣ ϦϴΑ ̵ΩΎҨί ϑϼΘΧ άϟ Ζγ ̶ϣϭΩ ̶̳ΪϨ̯ή̡ ϞϣΎη ΩΩ 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
̵ϮϨϣ ί Εή̡ ̵Ύϫ ϩΩΩ ϑάΣ ̵ήΑ Data-select cases-if income<200 ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ έ
3,254 5,530 4,277 4,057 1,706 4,709
Εή̡ ̶ϠϴΧ Ϫ̯ έ ˻˹˹ ί ήΘθϴΑ ΎΗ έΩϮϤϧ 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 έΩϮϤϧ ϦҨ ΩέΩ ΩϮΟϭ ̵έΎϴδΑ Εή̡
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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
One_ sample T Test ƵŶƃ ƶŤųŚƴƃ ŹřŶƤƯ Ĩƿ ƪŝŚƤƯ Źŵ ƶƳƺưƳ ƲǀĮƳŚǀƯ ƱƺƯŻō
co
m
t ϥϮϣί ϭ ϪδϳΎϘϣ ϥΎϜϣ έϮΘγΩ Ϧϳ ϱήΟ ΎΑ Ωϭέϲϣ έΎϛ ϪΑ ϥϮϣί ζΠϨγ ϱήΑ Ζγ Ύϫ ϩΩΩ ϥΩϮΑ ϝΎϣήϧ ήΑ νήϓ ϥϮϣί Ϧϳ έΩ ϪΘηΩ ϱΩΪϋ ήϳΩΎϘϣ ΪϳΎΑ ϩΪη ϲϓήόϣ ήϴϐΘϣ ΪηΎΑ ΪϨηΎΑ ϪΘηΩ Ϣϫ ϲ̴ϟϮ̩ ϱέΪϘϣ ΎϫϩΩΩ ή̳ Ωήϛ ΪϫϮΧ ΖϣϭΎϘϣ ϩΎΒΘη ήΑήΑέΩ ϥϮϣί Ϧϳ Ζγ ϩΪη ϪΘϓή̳ ήψϧ έΩ ήϳί ϞϜη ϪΑ ϥϮϣί ϲγΪΣ έΪϘϣ 80 ϭ Ζγ ϪϧϮϤϧ Ϧϴ̴ϧΎϴϣέΪϘϣ
Ϣϴϧίϲϣ Ϧϴ̴ϧΎϴϣ ϱήΑ ΖϴόϤΟ ί Ϫϛ Ζγ
Analyze – compare Means – One_ sample T Test / Test variable ……Income Test value …..……80
e.
w
w w
.K
et
ab
lin
ϼΜϣ ΎΠϨϳ έΩ test value έΪϘϣ ΎΑ ΪϳΎΑ ϥΎθϨϴ̴ϧΎϴϣ Ϫϛ ϲϳΎϫήϴϐΘϣ Ύϳ ήϴϐΘϣ Test variables ΖϤδϗ έΩ Ωήϴ̳ϲϣ έήϗ ΩϮη ϪδϳΎϘϣ 80 ΩϮΟϭ 95% ϥΎϨϴϤσ ϪϠλΎϓ Ϛϳ ϼΜϣ ϥΎϨϴϤσ ϪϠλΎϓ ΪλέΩ ϒϳήόΗ ϥΎϜϣ ΰϴϧ options ϪϤϛΩ ΏΎΨΘϧ ΎΑ ΩέΩ ϞϣΎη Ϫϛ Ζγ ϒϠΘΨϣ ϱΎϫ ϪϧϮϤϧ ί ϲϠλϮϓ ΪλέΩ ϩΪϨϫΩ ϥΎθϧ ϥΎϨϴϤσ ϪϠλΎϓ ϢϴηΎΑ ϪΘηΩ ΖϗΩ ˬΖϴόϤΟ ί ή̴ϳΩ ϪϧϮϤϧ ˺˹˹ έΩ ˬΪϫΩϲϣ ϥΎθϧ ̂̋ ϥΎϨϴϤσ ϪϠλΎϓ Ϛϳ ϝΎΜϣ ϱήΑ ΪϨηΎΑ ϲϣ Ϧϴ̴ϧΎϴϣ ΩϮΑ ΪϨϫϮΧ ϪϠλΎϓ Ϧϳ έΩ Ύϫ ϪϧϮϤϧ Ϧϴ̴ϧΎϴϣ ˬϪϧϮϤϧ ̂̋ Ζδϴϧ ϩΪη ϪΘϓή̳ ϪϧϮϤϧ ί ̶ηΎϧ ̶ϨόΑ Ζγ έΩ ̶Ϩόϣ ̵ϭΎδΗ ΎҨ ϑϼΘΧ ϦϴҨϮ̳ ̶ϣ ̶Θϗϭ ϪΘ̰ϧ ΎҨ ϑϼΘΧ ̶ϨόҨ ΪηΎΒϧ έΩ ̶Ϩόϣ ή̳ Ҩ ϑ ΘΧ ό Ύ ϧ έΩ ΩϮΑ ΪϫϮΧ ϦϴϤϫ ϪΠϴΘϧ ΩϮη ϥϮϣί Ϣϫ ̵ή̴ҨΩ ϪϧϮϤϧ ΎΑ ϫ Ϯ ϦϴϤ Π ϧ Ω η ϫ ή̴ Ω ϧ Ϥϧ ΎΑ ή̳ Ϫ̰ϠΑ ή̳ Ϫ̰ Ζγ ϥϮϣί ˬ κϴΨθΗ εϭέ Ζγ ΖϴόϤΟ ί ̶ηΎϧ ̵ϭΎδΗ 26
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
ˮϪϧ ΎҨ Ζγ έΩ ̶Ϩόϣ ΪϣέΩ ήϴϐΘϣ ΩέϮϣ έΩ ́˹ έΪϘϣ ΎΑ ϩΪη ϩΩί αΪΣ Ϧϴ̴ϧΎϴϣ ϢϴϨϴΒΑ ϢϴϫϮΧ ̶ϣ
H 0: 80 H1: 80
H0: 800
SPSS
H1: 800
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=6399 ̶ϨόҨ n-1 ̵Ωί ϪΟέΩ
-10.52516
xx 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 νήϓ Ωέ ΩέΪϧ ΩϮΟϭ H 0 ΪϴҨΎΗ ̵ήΑ ̶ϠϴϟΩ β̡ Ζγ ϩΪη ήΘ̰̩Ϯ̯ ί p value έΪϘϣ ϥϮ̩ ˺ T
ΩϮΧ ϦҨ Ϫ̯ ΪηΎΑ ̶Ϥϧ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩΩ ϥΎθϧ ΩΪϋ ϭΩ ˻ ϭ ϩΪη ̶ϔϨϣ 80 ̶ϨόҨ Ζγ ̶ϔϨϣ ϩίΎΑ ϦҨ ϥϮ̩ Ζγ έΎ̰η ΕϭΎϔΗ ϩΪϨϫΩ ϥΎθϧ ϭ Ζγ H 0 νήϓ ϩΪϨϨ̯ Ωέ ϞϣΎϋ Ζγ ήΘϤ̯ ́˹ ί Ϧϴ̴ϧΎϴϣ ̶ϨόҨ Ζγ έΩ ̶Ϩόϣ ϑϼΘΧ ˼ Ζγ έΩ ̶Ϩόϣ ˬΪηΎΑ ˻ ί ήΘ̳έΰΑ ΎҨ ήΑήΑt ϖϠτϣ έΪϗ .̊
27
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
H 0: 70 H1: 70
ϢϴϨ̯ ̶ϣ έή̰Η ΰϴϧ ̀˹ ΩΪϋ ̵ήΑ έ ϥϮϣί ϦҨ
H0: 700
SPSS
Ʊřźƿř żƿř ƺŤǀŤƀƳř
H1: 700
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
ΩέΪϧ ΩϮΟϭ H 0 Ωέ ̵ήΑ ̶ϠϴϟΩ β̡ Ζγ ϩΪη ήΘ̳έΰΑ ί p value έΪϘϣ ϥϮ̩ ˺
ΩϮΧ ϦҨ Ϫ̯ ΪηΎΑ ̶ϣ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩΩ ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ ϪϧϮϤϧ ςγϮΗ H 0 νήϓ ϩΪϨϨ̯ ΪϴҨΎΗ ϞϣΎϋ Ζδϴϧ έΩ ̶Ϩόϣ ϑϼΘΧ ˼ Ζγ έΩ ̶Ϩόϣ ˬΪηΎΑ ˻ ί ήΘ̳έΰΑ ΎҨ ήΑήΑt ϖϠτϣ έΪϗ .̊
ήϴϴϐΗ έ ΎҨ ϝϭ ωϮϧ ̵ΎτΧ έΪϘϣ ϥϮΘϴϣ Option ΖϤδϗ έΩ One_ sample T Test ϩήΠϨ̡ έΩ ϪΘ̰ϧ ΪϧϮη ̶ϣ Ωέ Ύϫ Ϫϴοήϓ ήΘθϴΑ ϢҨά̴Α ˺ έ ̶ϨόҨ ϢҨήΒΑϻΎΑ ̂̂ ΎΗ έ ϥΎϨϴϤσ ΐҨήο ή̳ ΩΩ 28
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ϝΎΜϣ ΪҨέϭ ΖγΪΑ έweight ήϴϐΘϣ ΎҨ ϥίϭ Ϧϴ̴ϧΎϴϣ ϭ ΪϴϨ̯ ίΎΑ έ spss sample data.sav ϞҨΎϓ Analyze- report – frequency Ζγ ̀̋ ϥίϭ Ϧϴ̴ϧΎϴϣ Ϫ̰ϨҨ νήϓ ΎΑ ΪϴϫΩ ϡΎΠϧ ̶ϧϮϣί H0: 750 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 =
ΩέΪϧ ΩϮΟϭ H 0 Ωέ ̵ήΑ ̶ϠϴϟΩ β̡ Ζγ ϩΪη ήΘ̳έΰΑ ί p value έΪϘϣ ϥϮ̩ ˺
ΩϮΧ ϦҨ Ϫ̯ ΪηΎΑ ̶ϣ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩΩ ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ ϪϧϮϤϧ ςγϮΗ H 0 νήϓ ϩΪϨϨ̯ ΪϴҨΎΗ ϞϣΎϋ Ζδϴϧ έΩ ̶Ϩόϣ ϑϼΘΧ ˼ Ζγ έΩ ̶Ϩόϣ ˬΪηΎΑ ˻ ί ήΘ̳έΰΑ ΎҨ ήΑήΑt ϖϠτϣ έΪϗ .̊
29
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ΪҨέϭ ΖγΪΑ έ weight -after ήϴϐΘϣ ΎҨ ϢҨ̫έ ί ΪόΑ ϥίϭ Ϧϴ̴ϧΎϴϣ Analyze – compare Means – One_ sample T Test / Test variable ……weight , weight - after Test value …..……75
Ζγ ̀̋ ϢҨ̫έ ί ΪόΑ ϥίϭ Ϧϴ̴ϧΎϴϣ Ϫ̰ϨҨ νήϓ ΎΑ ΪϴϫΩ ϡΎΠϧ ̶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 νήϓ Ωέ ΩέΪϧ ΩϮΟϭ H 0 ΪϴҨΎΗ ̵ήΑ ̶ϠϴϟΩ β̡ Ζγ ϩΪη ήΘ̰̩Ϯ̯ ί weight-afterήϴϐΘϣ)
p value έΪϘϣ ϥϮ̩ ˺
w
ΩϮΧ ϦҨ Ϫ̯ ΪηΎΑ ̶Ϥϧ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩΩ ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ έΎ̰η ΕϭΎϔΗ ϩΪϨϫΩ ϥΎθϧ ϭ Ζγ H 0 νήϓ Ωέ ϞϣΎϋ ΪηΎΑ ϡή̳ϮϠϴ̯ ̀̋ ΪϧϮΘϴϤϧ ϢҨ̫έ ί ΪόΑ ϥίϭ Ϧϴ̴ϧΎϴϣ β̡ Ζγ έΩ ̶Ϩόϣ ϑϼΘΧ ˼ Ζγ έΩ ̶Ϩόϣ ˬΪηΎΑ ˻ ί ήΘ̳έΰΑ ΎҨ ήΑήΑt ϖϠτϣ έΪϗ .̊
exclude cases analysis by ϪϨҨΰ̳ option έΩ ή̳ ϪΘ̰ϧ έΩ aly ysis ϢϴηΎΑ Ϣ Α ϪΘηΩ missiing ϭ ϢϴϧΰΑ Ϣ ϧΰΑ ̮ϴΗ ϴΗ έ ana ϭ ΪϨ̯ ̶ϣ ΏΎδΣ ϪϧΎ̳ΪΟ έ ϡΪ̯ ήϫ ήϴϐΘϣ ϭΩ ϪδҨΎϘϣ ϪΘϓή̳ ήψϧ έΩ ϥϭΪΑ έ ϪΘϓέ έΎϛ ϪΑ ϱΎϫήϴϐΘϣ ϱήΑ ϥϮϣί ήψ ϥϭΪ Θ ϛ Ύ ϣ ήΑ ϥϮϣί Ωϭέ ϲϣ έΎϛ ϪΑ ή̴ϳΩ ϱΎϫήϴϐΘϣ ϱήΑ ϩΪθϤ̳ ήϳΩΎϘϣ ̮ϴΗ έ es listtwise e̶ϨόҨ ϡϭΩ ϪϨҨΰ̳ ή̳ ̶ϟϭ ϴ έ excllude case ̶Ϩ Ҩ ϭΩ ΰ̳ ή̳ ̶ϟ ̶ϣ ϑάΣ έ ΩέΩ missingϪ̯ ̵ case ΎҨ ΩέϮϣ Ϟ̯ ϢϴϧΰΑ έΎϛ ϪΑ ϱΎϫήϴϐΘϣ ϪϤϫ ϱήΑ ϩΪθϤ̳ έΪϘϣ ΎΑ ΩέϮϣ Ύ ήϴϐΘϣ Ϫ ϫ ήΑ ϩ Ϥ έΪ Α ΩέϮ ΪϨ̯ έΩ Ϫ̩ ΪηΎΑ ̵ ϩΩΩ ϥΩή̰ϧ ̠ҨΎΗήΛ έΩ Ϫ̩ missing ϦҨ .Ϊη ΪϫϮΨϧ ϪΘϓή̳ ήψϧ έΩ ϥϮϣί έΩ ϪΘϓέ ΪϨ̯ ̶Ϥϧ ̶ϗήϓ ΪηΎΑ ϩΪη ϒҨήόΗ g value e Ϊ ̶Ϥ Ϊ Α ϩ ϒ ήόΗ missing 30
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( Independent-Samples T Test ƵŵŹ Śƿ Ƶƹźĭ ƹŵ Ʋǀŝ źǀƜŤƯ Ĩƿ ƲǀĮƳŚǀƯ ƱƺƯŻō
ϢϴϨϛϲϣ ϩΩΎϔΘγ εϭέ Ϧϳ ί ϩϭή̳ ϭΩ ϦϴΑ έΩ ΖϴόϤΟ ϲ̳̬ϳϭ Ϛϳ Ϧϴ̴ϧΎϴϣ ϥΩϮΑ ήΑήΑ ϥϮϣί ϱήΑ ϲϤϛ έΪϘϣ Ϛϳ ήϴϐΘϣ ϭ Ζγ ϩΪη ϊϳίϮΗ ϝΎϣήϧ ΕέϮλ ϪΑ ΖϴόϤΟ ϲ̳̬ϳϭ Ϫϛ Ζγ ϦϳήΑ νήϓ Ζγ ϱΩΪϋ
Ζγ ϩΪη ΏΎΨΘϧ ϲϓΩΎμΗ ϭ ϞϘΘδϣ ΕέϮλ ϪΑ ϪϧϮϤϧ ϦϴϨ̪Ϥϫ Ζγ έΩέϮΧήΑ ΖϴϤϫ ί ϥϮϣί Ϧϳ έΩ Εή̡ ρΎϘϧ ΩϮΟϭ ϡΪϋ ϭ ϥέΎϘΗ Ζδϴϧ αΎδΣ ϲ̴ϟϮ̩ ϪΑ ΖΒδϧ ϥϮϣί Ϧϳ ΪϫΩϲϣ ήϴϴϐΗ έ ϥϮϣί ϲΟϭήΧ ˬϩϭή̳ ϭΩ έΩ Ύϫϥ ϥΩϮΒϧ ήΑήΑ ΎΑ ΎϫβϧΎϳέϭ ϥΩϮΑ ήΑήΑ
Analyze -
ΪϴϨϛ ϲσ έ ήϳί ήϴδϣ έϮΘγΩ Ϧϳ ϪΑ ϲγήΘγΩ ϱήΑ Compare Means - Independent-Samples T Test/Test variables… ….weight
w
w w
.K
et
ab
lin
e.
co
test variable ΖϤδϗ έΩ έ ΪϧϮη ϥϮϣί ΪϳΎΑ Ϫϛ ϲϳΎϫήϴϐΘϣ Ύϳ ήϴϐΘϣ ΪϴϨϛ Ωέϭ Grouping Variable ΖϤδϗ έΩ ΪϴϨϛ κΨθϣ έ ϱΪϨΑϩϭή̳ ήϴϐΘϣ ϭΩ ϦϴΑ ϪδϳΎϘϣ ΪϧϮΗ ϲϣ ςϘϓ ϥϮϣί Ϧϳ Ωέϭ ΖγΪΑ έ ϩϭή̳ ήϴϐΘϣ ϱήΑ define group ϪϤϛΩ ΏΎΨΘϧ ΎΑ ϭΩ ϒϳήόΗ ϱήΑ ήψϧ ΩέϮϣ έΪϘϣ ϱΪϨΑϩϭή̳ ̵ΪϨΑ ϩϭή̳ ήϴϐΘϣ ή̳ ̶ΘΣ ΪϴϨϛ Ωέϭ έ ϩϭή̳ ϥϮϣί ϦҨ ΪηΎΑ ϪΘηΩ ϩϭή̳ ϭΩ ί ήΘθϴΑ Ϊη ΪϫϮΧ ϪΒγΎΤϣ ϩϭή̳ ϭΩ έΩ ςϘϓ έΪϘϣ ϱϭΎδϣ ϭ ήΘθϴΑ ϭ ήΘϤϛ ΕέϮλ ϪΑ Ύϫϩϭή̳ ϚϴϜϔΗ ϥΎϜϣ ΰϴϧ cut point έΪϘϣ ϢϴψϨΗ ΎΑ ϦϴϨ̪Ϥϫ Ζδϫ ΰϴϧ cut point H0:m1 m2 0 H0: m1 m2 SPSS H1:m1 m2 0 H1: m1 m2 κΨθϣ έ ϝϭ ϪΘγΩ ˬϱΪϨΑϩϭή̳ ήϴϐΘϣ ϱήΑ Group1 έΪϘϣ ΪϨϛϲϣ κΨθϣέ ϡϭΩ ϪΘγΩ ˬϱΪϨΑϩϭή̳ ήϴϐΘϣ ϱήΑ Group2 έΪϘϣ ΪϨϛϲϣ Cut έΪϘϣ ϦϴϴόΗ ΎΑ ϥϮΘϴϣ ΩϮΑ ̵ΩΪϋ ˬ̵ΪϨΑ ϩΩέ ήϴϐΘϣ ή̳ Ωή̯ ϒϳήόΗ ήϳί ΕέϮλ ϪΑ έ ϩϭή̳ ϭΩ Point cut point ί ϱϭΎδϣ ϭ ήΘθϴΑ ήϳΩΎϘϣ ˺ ϩϭή̳ cut point ί ήΘϤϛ ήϳΩΎϘϣ ˻ ϩϭή̳ 31 www.ircdvd.com
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m
Grouping variables……gender
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ΪϴϨ̯ ̶γέήΑ ϥΩήϣ ϭ ϥΎϧί ϩϭή̳ ϭΩ έΩ έ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ
H0: m1 m2 H1: m1 m2
SPSS
H0:m1 m2 0
ϥΩήϣ ϩϭή̳ έΩ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1
H1:m1 m2 0
ϥΎϧί ϩϭή̳ έΩ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ 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 =
̶Ϥϧ Ωέ Ύϫ βϧΎҨέϭ ̵ήΑήΑ β̡ Ζγ ϩΪη ήΘ̳έΰΑ ί Ζγ βϧΎҨέϭ ϪΑ ρϮΑήϣ Ϫ̯ weightήϴϐΘϣ) p value έΪϘϣ ϥϮ̩ ˺ Ωή̯ ϢϴϫϮΧ ̶γέήΑ έ ϡϭΩ ήτγ ΕέϮμϨҨ ήϴϏ έΩ ϢϴϨ̯ ̶ϣ ϩΎ̴ϧ ϝϭ ήτγ έΩ Ϧϴ̴ϧΎϴϣ ϪΑ ρϮΑήϣ p value ϪΑ άϟ ΩϮη Sig(2-tailed) = p value 0.319 > 0.05 =
ΩϮΧ ϦҨ Ϫ̯ ΪηΎΑ ̶ϣ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩΩ ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ ϪϧϮϤϧ ςγϮΗ H 0 νήϓ ΪϴҨΎΗ ϞϣΎϋ Ζγ ήΑήΑ ϥΎϧί ϭ ϥΩήϣ ϩϭή̳ ϭΩ έΩ ϥίϭ Ϧϴ̴ϧΎϴϣ β̡ Ζδϴϧ έΩ ̶Ϩόϣ ϑϼΘΧ ˼ Ζγ έΩ ̶Ϩόϣ ˬΪηΎΑ ˻ ί ήΘ̳έΰΑ ΎҨ ήΑήΑt ϖϠτϣ έΪϗ .̊
32
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ΪϴϨ̯ ̶γέήΑ ϝΎγ ˼˹ ϦϴҨΎ̡ ϭ ϝΎγ ˼˹ ̵ϻΎΑ ̶Ϩγ ϩϭή̳ ϭΩ έΩ έ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ compute ΎҨ recode ϩέ ί ϥϮΘϴϣ ϝΎγ ˼˹ ϦϴҨΎ̡ ϭ ϝΎγ ˼˹ ̵ϻΎΑ ϩϭή̳ ϭΩ ϪΑ ϥίϭ ήϴϐΘϣ ̵ΪϨΑ ϩϭή̳ ̵ήΑ ϪϤ̳Ω ϭ Independent-Samples T Test ϩήΠϨ̡ έΩ Ϫ̯ Ζγ ϦҨ ϩέ ϦҨήΗ ϩΩΎγ ̶ϟϭ ΩϮϤϧ ϩΩΎϔΘγ Ωή̯ Ωέϭ έ ˼˹ ΩΪϋ ϭ ΩϮϤϧ ϩΩΎϔΘγ cutpoint ϪϨҨΰ̳ ί option
H0: m1 m2 H1: m1 m2
H0:m1 m2 0
SPSS
ϝΎγ ˼˹ ί ήΗ ϦϴҨΎ̡ ̶Ϩγ ϩϭή̳ έΩ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1
H1:m1 m2 0
ϝΎγ ˼˹ ί ήΗϻΎΑ ̶Ϩγ ϩϭή̳ έΩ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ 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 ̶Ϥϧ Ωέ Ύϫ βϧΎҨέϭ ̵ήΑήΑ β̡ Ζγ ϩΪη ήΘ̳έΰΑ ί Ζγ βϧΎҨέϭ ϪΑ ρϮΑήϣ Ϫ̯ weightήϴϐΘϣ) p value έΪϘϣ ϥϮ̩ ˺ Ωή̯ ϢϴϫϮΧ ̶γέήΑ έ ϡϭΩ ήτγ ΕέϮμϨҨ ήϴϏ έΩ ϢϴϨ̯ ̶ϣ ϩΎ̴ϧ ϝϭ ήτγ έΩ Ϧϴ̴ϧΎϴϣ ϪΑ ρϮΑήϣ p value ϪΑ άϟ ΩϮη Sig(2-tailed) = p value 0.093 > 0.05 =
ΩϮΧ ϦҨ Ϫ̯ ΪηΎΑ ̶ϣ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩΩ ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ ϪϧϮϤϧ ςγϮΗ H 0 νήϓ ΪϴҨΎΗ ϞϣΎϋ Ζγ ήΑήΑ ϝΎγ ˼˹ ί ήΗ ϦϴҨΎ̡ ϭ ϝΎγ ˼˹ ί ήΗϻΎΑ ̶Ϩγ ϩϭή̳ ϭΩ έΩ ϥίϭ Ϧϴ̴ϧΎϴϣ β̡ Ζδϴϧ έΩ ̶Ϩόϣ ϑϼΘΧ ˼ Ζγ έΩ ̶Ϩόϣ ˬΪηΎΑ ˻ ί ήΘ̳έΰΑ ΎҨ ήΑήΑt ϖϠτϣ έΪϗ .̊
33
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
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Ϫ̯ ̶ϧΎδ̯ ϭ ΪϧέΩ ϪϴϟΎϋ ΕϼϴμΤΗ Ϫ̯ ̶ϧΎδ̯ ϩϭή̳ ϭΩέΩ Demo ϞҨΎϓ έΩ έ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ ΪϴϨ̯ ̶γέήΑ Ϊϧ ϩΩή̰ϧ ϞϴϤ̰Η έ ϥΎθΗϼϴμΤΗ
ϩΪθϧ ϞϴϤ̰Η ΕϼϴμΤΗ ϩϭή̳ έΩ income ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1
H0: m1 m2 H1: m1 m2
SPSS
H0:m1 m2 0
ϪϴϟΎϋ ΕϼϴμΤΗ ϩϭή̳ έΩ income ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ 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 βϧΎҨέϭ ̵ήΑήΑ β̡ Ζγ ϩΪη ήΘ̰̩Ϯ̯ ί Ζγ βϧΎҨέϭ ϪΑ ρϮΑήϣ Ϫ̯ income ήϴϐΘϣ) p value έΪϘϣ ϥϮ̩ ˺ ΩϮη ̶ϣ Ωέ H 0 νήϓ β̡ Ζγ ί ήΘ̰̩Ϯ̯ ϢϴϨ̯ ̶ϣ ϩΎ̴ϧ ϡϭΩ ήτγ έΩ Ϧϴ̴ϧΎϴϣ ϪΑ ρϮΑήϣ p value ϪΑ άϟ ΩϮη ̶ϣ Sig(2-tailed) = p value 0.000 0.05 =
ΩϮΧ ϦҨ Ϫ̯ ΪηΎΑ ̶Ϥϧ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩΩ ϥΎθϧ ΩΪϋ ϭΩ ˻ Ζγ έΎ̰η ΕϭΎϔΗ ϩΪϨϫΩ ϥΎθϧ ϭ Ζγ H 0 νήϓ Ωέ ϞϣΎϋ Ζδϴϧ ήΑήΑ ϪϴϟΎϋ ϭ ϩΪθϧ ϞϴϤ̰Η ΕϼϴμΤΗ ϩϭή̳ ϭΩ έΩ ΪϣέΩ Ϧϴ̴ϧΎϴϣ β̡ Ζγ έΩ ̶Ϩόϣ ϑϼΘΧ ˼ Ζγ έΩ ̶Ϩόϣ ˬΪηΎΑ ˻ ί ήΘ̳έΰΑ ΎҨ ήΑήΑt ϖϠτϣ έΪϗ .̊
34
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
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( Paired-Samples T Test ŢƠū źǀƜŤƯ ƹŵ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ ƱƺƯŻō ϢϴϨϛϲϣ ϩΩΎϔΘγ ϥϮϣί Ϧϳ ί ΪϧϮη ϪδϳΎϘϣ ΪϳΎΑ ϪϧϮϤϧ έΩ ήϴϐΘϣ ϭΩ Ϧϴ̴ϧΎϴϣ ή̳ Ϫϛ ΪϧϮηϲϣ ϪΘϓή̳ ήψϧ έΩ x,y ΐΗήϣ Νϭί ΕέϮλ ϪΑ ϩΪη ϱήϴ̳ ϩίΪϧ ϱΎϫ ϪμΨθϣ ϥϮϣί Ϧϳ έΩ Ζγ ϪϧϮϤϧ έΩ ϡϭΩ ϲ̳̬ϳϭ y ϭ ϝϭ ϲ̳̬ϳϭ x ϥ έΩ ΪΘΑ έΩ ϪϧϮϤϧ Ωήϓ ϪΑ ϢϴϨϛ κΨθϣ ϥϮΧ έΎθϓ ζϫΎϛ έΩ έ ϭέΩ Ϛϳ ήΛ ϢϴϫϮΧ ϲϣ ϝΎΜϣ ϱήΑ ϥϮΧ έΎθϓ ΰϴϧ ϲόϗϭ ϭέΩ ϥΪϧΩέϮΧ ί β̢γ ϢϴϨϛ ϲϣ ϱήϴ̳ ϩίΪϧ έ ϥϮΧ έΎθϓ ϩΩΩ ΎϤϧϭέΩ ϪδϳΎϘϣ ϲόϗϭ ϱϭέΩ ί ΪόΑ ϭ ϞΒϗ έ ϥϮΧ έΎθϓ Ϧϴ̴ϧΎϴϣ ΪϫϮΧϲϣ ϥϮϣί Ϊη ΪϫϮΧ ϱήϴ̳ϩί Ϊϧ ΪϨϛ ΪϨΘδϫ ϱΩΪϋ ήϳΩΎϘϣ Ζγ Ϧϳ ήΑ νήϓ Ζγ ϝΎϣήϧ ϊϳίϮΗ ϱέΩ ΎϬϧ ΕϭΎϔΗ Ύϳ ΪϧέΩ ϝΎϣήϧ ϊϳίϮΗ ήϴϐΘϣ ϭΩ ήϫ ΎϫϩΩΩ ΪϧϩΪη ΏΎΨΘϧ ϲϓΩΎμΗ ϭ ϞϘΘδϣ ΕέϮλ ϪΑ Ύϫ ϪϧϮϤϧ έϮσ ϪΑ ήϴϐΘϣ Ϛϳ έΩ ϩΪθϤ̳ έΪϘϣ ΩϮΟϭ ΕέϮλ έΩ ΪηΎΑ ήΑήΑ ΪϳΎΑ ήϴϐΘϣ ϭΩ ήϫ ϱήΑ ΎϫϪϧϮϤϧ ΩΪόΗ Ϊη ΪϫϮΧ ϪΘϓή̳ ϩΪϳΩΎϧ ΩέϮϣ ϥ ϞϣΎϛ ΩέΪϧ ϲϟΎϜη ήϴϐΘϣ ϭΩ βϧΎϳέϭ έΪϘϣ έΩ ΕϭΎϔΗ
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
ϝϭήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1 ϡϭΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2
et
Analyze -
lin
ΪϴϨϛ ϲσ έ ήϳί ϞΣήϣ ϥϮϣί Ϧϳ ϪΑ ϲγήΘγΩ ϱήΑ
ϪΑ ϭ ϩΩήϛ ΏΎΨΘϧ ̠̩ ΖϤγ ΖϤδϗ έΩ έ ήϴϐΘϣ ϭΩ ήϫ .ΪϴϫΩ ϝΎϘΘϧ pair Variable ΖϤδϗ 35
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m
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Analyze -
H0: m1 m2 H1: m1 m2
Ʊřźƿř żƿř ƺŤǀŤƀƳř
ΪϴϨ̯ ϪδҨΎϘϣ Ϣϫ ΎΑ έ weight-after ϭ weight ήϴϐΘϣ ϭΩ Compare Means - Paired-Samples T Test...
SPSS
H0:m1 m2 0
weight ϥίϭήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1
H1:m1 m2 0
weight-after ϢҨ̫έ ί ΪόΑ ϥίϭ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ 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 ήϴϐΘϣ ϭΩ ̶ϨόҨ ΪηΎΑ ήΘ̰ҨΩΰϧ 1 ΩΪϋ ϪΑ ΩΪϋ ϦҨ Ϫ̩ ήϫ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ έ 0.941 ΩΪϋ correlation ΎҨ ̶̴ΘδΒϤϫ ΐҨήο ˺ ΪϧΩϮΑ ̲ϨϫΎϤϫ Ϣϫ ΎΑ ˱ϼϣΎ̯ ϭ Ζγ ϪΘηΩ ΩϮΟϭ ϢҨ̫έ ί β̡ ϥίϭ ζϫΎ̯ ΩέϮϣ ϡΎϤΗ έΩ ̶ϨόҨ ΪϧέΩ Ϣϫ ̵ϭέ ̵ήΘθϴΑ ήΛ ΩϮη ̶ϣ Ωέ H 0 νήϓ ˬ ί p value ϥΩϮΑ ήΘ̰̩Ϯ̯ ϪΑ ϪΟϮΗ ΎΑ ˻ Sig(2-tailed) = p value 0.039 0.05 =
ΩϮΧ ϦҨ Ϫ̯ ΪηΎΑ ̶Ϥϧ ήϔλ ϞϣΎη 95% Confidence Interval of the Difference ϥϮΘγ έΩ ϩΪη ϩΩΩ ϥΎθϧ ΩΪϋ ϭΩ ˼ Ζγ έΎ̰η ΕϭΎϔΗ ϩΪϨϫΩ ϥΎθϧ ϭ Ζγ H 0 νήϓ Ωέ ϞϣΎϋ
Ζδϴϧ ήΑήΑ ϢҨ̫έ ί ΪόΑ ϥίϭ ϭ ϥίϭ ήϴϐΘϣ ΖϔΟ Ϧϴ̴ϧΎϴϣ β̡ Ζγ έΩ ̶Ϩόϣ ϑϼΘΧ ̊ Ζγ έΩ ̶Ϩόϣ ˬΪηΎΑ ˻ ί ήΘ̳έΰΑ ΎҨ ήΑήΑt ϖϠτϣ έΪϗ .̋
36
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One-Way Anova Ƶƹźĭ ƲƿŶƴģ Źŵ źǀƜŤƯ Ĩƿ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ ƱƺƯŻō
co
e.
lin
ab
et
.K
w w
w
ϡΎΠϧ έ ΎϫϲϳΎΗϭΩ ϪΑ ρϮΑήϣ ϥϮϣί Ϊϴϫ ϮΧϲϣ ή̳ ΪϴϨϛ ΏΎΨΘϧ έ Post Hoc ϪϨϳΰ̳ ΪϴϫΩ ϥΩϮΑ ήΑήΑ ΕέϮλ έΩ ΪϴϧϮΗ ϲϣ έ ϥϮϣί ωϮϧ ΪϨϧΎϣ ϲϟϭ έΩΎϛ ϱΎϫ ϪϨϳΰ̳ ί ϲϜϳ Ύϫ βϧΎϳέϭ ΪϴϨϛ ΏΎΨΘϧ tukey-Bunfferoni- LSD ΪϴϧΰΑ έContinue ϪϤϛΩ β̢γ ϱΎϫϦϴ̴ ϧΎϴϣ ί ϲτΧ ϲΒϴϛήΗ ΪϴϫϮΧ ϲϣ ή̳ Contrast ϪϤϛΩ ί ΪϴϨϛ ϪδϳΎϘϣ Ϣϫ ΎΑ έ Ύϫϩϭή̳ ΪϴϨϛ ϩΩΎϔΘγ ΐϳήο ωϮϤΠϣ Ζγ ήΘϬΑ Ϫϛ ΪϴηΎΑ ϪΘηΩ ϪΟϮΗ ΪηΎΑ ήϔλ ήΑήΑ ϲτΧ ΐϴϛήΗ 37 www.ircdvd.com
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m
ΩϮΟϭ ϩϭή̳ ϦϳΪϨ̩ ϦϴΑ έΩ κΧΎη Ϛϳ έΪϘϣ ϪδϳΎϘϣ ϥΎϜϣ Ϫϓήσ Ϛϳ βϧΎϳέϭ ΰϴϟΎϧ ϝϭΪΟ ί ϩΩΎϔΘγ ΎΑ ΩέΩ ϲϫϭή̳ ϥϭέΩ ϱΎτΧ ΕΎόΑήϣ Ϧϴ̴ϧΎϴϣ έΪϘϣ ΖΒδϧ ή̳ βϧΎϳέϭ ΰϴϟΎϧ ϝϭΪΟ ϱΎϫ ϩέΎϣ ϪΑ ϪΟϮΗ ΎΑ ϲϫϭή̳ ϦϴΑ ϱΎτΧ ΕΎόΑήϣ Ϧϴ̴ϧΎϴϣ έΪϘϣ ΎΑ ϩϭή̳ Ϧϴ̴ϧΎϴϣ ί ϩϭή̳ ήϫ ήϳΩΎϘϣ ϑϼΘΧ ΕΎόΑήϣ ωϮϤΠϣ
ζϘϧ ϩΪϨϫΩ ϥΎθϧ ΪηΎΑ ϱΩΎϳί ϑϼΘΧ ϱέΩ Ϟϛ Ϧϴ̴ϧΎϴϣ ί ϩϭή̳ ήϫ Ϧϴ̴ϧΎϴϣ ϑϼΘΧ ΕΎόΑήϣ ωϮϤΠϣ
ΪϫΩϲϣ ϥΎθϧ έ ϩϭή̳ Ϧϴ̴ϧΎϴϣ ήϴϴϐΗ έΩ ϞϣΎϋ Ϫϴϟϭ ϱΎϫ νήϓ ΪηΎΑ ϪΘηΩ ϴΤλ ήϳΩΎϘϣ ΪϳΎΑ έϮΘϛΎϓ ήϴϐΘϣ ΪηΎΑ ϪΘγϮϴ̡ ήϳΩΎϘϣ ϱέΩ ΪϳΎΑ ϪΘδΑϭ ήϴϐΘϣ ΪϨηΎΑ ϲϓΩΎμΗ ϝΎϣήϧ ϊϳίϮΗ ϱέΩ ΪϳΎΑ ϩϭή̳ ήϫ έΩ ϪϧϮϤϧ ΪηΎΑ ϥέΎϘΗ ϱέΩ ϊϳίϮΗ ΪϳΎΑ ϲϟϭ Ζγ ϡϭΎϘϣ ϥΩϮΑ ϝΎϣήϧ ρήη ϪΑ ΖΒδϧ βϧΎϳέϭ ΰϴϟΎϧ ϥϮϣί ϩέΎϣ ί ϩΩΎϔΘγ ΎΑ έ ρήη Ϧϳ ΪϨηΎΑ ϩΪη ΏΎΨΘϧ ήΑήΑ βϧΎϳέϭ ΎΑ ϲΘϴόϤΟ ί ΪϳΎΑ Ύϫϩϭή̳ Ωήϛ ϱήϴ̳ ϩίΪϧ ϥϮΗϲϣ Levens ΩέΩ ΩϮΟϭ Ύϫϩϭή̳ ϦϴΑ έΩ Ϧϴ̴ϧΎϴϣ έΪϘϣ έΩ ήϴϴϐΗ ϱήϴ̳ ϩίΪϧ ϥΎϜϣ ϝϭΪΟ Ϧϳ ί ϩΩΎϔΘγ ΎΑ ΪϴϨϛ ϲσ έ ήϳί ϞΣήϣ έϮΘγΩ Ϧϳ ϪΑ ϲγήΘγΩ ϱήΑ Analyze - Compare Means - One-Way ANOVA... Ωέϭ Dependent List ϞΤϣ έΩ έ ϪΘδΑϭ ήϴϐΘϣ ΪϴϨϛ έ ϪΘδΑϭ ήϴϐΘϣ έΪϘϣ Ϫϛ ϱήϴϐΘϣ ϞϣΎϋ ήϴϐΘϣ ϥϮϣί ΪϴϫϮΧ ϲϣ ϥ ϒϠΘΨϣ ϱΎϫϩϭή̳ ϪΑ ΖΒδϧ ΪϴϨϛ Ωέϭ Factor ΖϤδϗ έΩ έ ΪϴϨϛ Ωέϭ Ζδϴϟ έΩ έ ϪΘδΑϭ ήϴϐΘϣ ϦϳΪϨ̩ ϥϮΗϲϣ ήϴϐΘϣ Ϛϳ ί ςϘϓ έϮΘϛΎϓ ήϴϐΘϣ ϱήΑ ϲϟϭ ΪϴϨϛ Ωήϛ ϩΩΎϔΘγ ϥϮΗϲϣ έϮΘϛΎϓ ήϴϐΘϣ ΡϮτγ έΩ ήϴϐΘϣ ήϫ Ϧϴ̴ϧΎϴϣ έΎϛ Ϧϳ ΎΑ ΪϧϮηϲϣ ϪδϳΎϘϣ
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Ʊřźƿř żƿř ƺŤǀŤƀƳř
co
m
Coefficient ΖϤδϗ έΩ έ Ϧϴ̴ϧΎϴϣ ήϫ ΐϳήο ΐϴΗήΗ ϪΑ ΪϴϧΰΑ έ Add ϪϤϛΩ ϭ ϩΩήϛ Ωέϭ Coefficient ΖϤδϗ έΩ ΐϳήο ωϮϤΠϣ Ϫϛ ΪϴϨϛ ΖϗΩ ΪηΎΑ ήϔλ ήΑήΑ ΪϳΎΑ ϭ Ζγ ϩΪη ϪΒγΎΤϣ Total ϪϨϳΰ̳ ΪϳέΩ ΝΎϴΘΣ ΎϫϦϴ̴ϧΎϴϣ ϦϴΑ ϲτΧ ϪτΑέ Ϛϳ ή̳ ΪϴϨϛ ΏΎΨΘϧ έ Polynomial ΪϴϧΰΑ έ Continue ϪϤϛΩ
e.
źǀƜŤƯ Ŷƴģ ƲǀĮƳŚǀƯ ƶƀƿŚƤƯ ƱƺƯŻō
w
w w
.K
et
ab
lin
ΩϮΑ ΪϫϮΧ ήϳί ΕέϮλ ϪΑ ϥϮϣί νήϓ H0: m1=m2=m3 H1: m1#m2#m3 ϡΪϋ ΖϠϋ Ϫϛ Ωήϛ κΨθϣ ϲϳΎΗϭΩ ϱΎϫϥϮϣί ί ϩΩΎϔΘγ ΎΑ ϥϮΗ ϲϣ ΩϮη Ωέ ήϔλ νήϓ Ϫϛ ϲΗέϮλ έΩ ΖγϩΩϮΑ Νϭί ϡΪϛ ϱήΑ ϱϭΎδΗ ΪϨγΎϨηϲϣ ΰϴϧ Post Hoc ϥϮϨϋ ϪΑ έ ϥϮϣί Ϧϳ ΩέΩ ΩϮΟϭ Contrast ΎϫϦϴ̴ϧΎϴϣ ί ϲΒϴϛήΗ ϥϮϣί ϥΎϜϣ ϦϴϨ̪Ϥϫ
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ˮΩέΩ ̵ήΛ Ϫ̩ ̶ϠϴμΤΗ ϒϠΘΨϣ ΡϮτγ ήΑ ΪϣέΩ Ϧϴ̴ϧΎϴϣ Ϫ̯ ΪϴϨ̯ ϪδҨΎϘϣ Demo ϞҨΎϓ έΩ 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 ϩϭή̳ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1 high sckool degree ϡϭΩ ϩϭή̳ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2 some college ϡϮγ ϩϭή̳ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m3 college degree ϡέΎϬ̩ ϩϭή̳ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m4 post-undergraduate degree ϢΠϨ̡ ϩϭή̳ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ 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
ΩϮη ̶ϣ Ωέ H 0 νήϓ ˬ Sig = p value 0.000 0.05 =
F 15.309
Sig. .000
ϪΠϴΘϧ ί p value ϥΩϮΑ ήΘ̰̩Ϯ̯ ϪΑ ϪΟϮΗ ΎΑ ˺
ήΑήΑ ΕϼϴμΤΗ ̵ΎϬϫϭή̳ ϦϴΑ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ β̡ Ζγ ΕϼϴμΤΗ ̵ΪϨΑ ϩϭή̳ ί ̶ηΎϧ ϭ Ζγ έΩ ̶Ϩόϣ ϑϼΘΧ ˻ Ζδϴϧ between Groups - Ϟϛ Ϧϴ̴ϧΎϴϣ ί ϩϭή̳ ήϫ Ϧϴ̴ϧΎϴϣ ϑϼΘΧ ΕΎόΑήϣ ωϮϤΠϣ ̶ϫϭή̳ ϦϴΑ ̵ΎτΧ ΕΎόΑήϣ ΩΎҨί ΕϭΎϔΗ ˼ ϩΪϨϫΩ ϥΎθϧ within Groups ± ϩϭή̳ Ϧϴ̴ϧΎϴϣ ί ϩϭή̳ ήϫ ήϳΩΎϘϣ ϑϼΘΧ ΕΎόΑήϣ ωϮϤΠϣ ϲϫϭή̳ ϥϭέΩ ϱΎτΧ ΕΎόΑήϣ ΎΑ Ζγ ϩϭή̳ Ϧϴ̴ϧΎϴϣ ήϴϴϐΗ έΩ factor ϞϣΎϋ ζϘϧ
39
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
βϧΎҨέϭ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ Test of Homogeneity of Variances ϝϭΪΟ έΩ ΎϬδϧΎҨέϭ ϪδҨΎϘϣ ϥϮϣί ί ϩΪϣ ΖγΪΑ ΞҨΎΘϧ ̊ p value 0.000 = Sig < ϭ ΪϧέΩ ̵έΩ ̶Ϩόϣ ϑϼΘΧ ΕϼϴμΤΗ ήϴϐΘϣ ϩϭή̳ ̋ έΩ Ϊϣ έΩ ήϴϐΘϣ ̵Ύϫ ήϴϐΘϣ ϩϭή̳ ̋ έΩ ΪϣέΩ ήϴϐΘϣ ̵Ύϫ Ϧϴ̴ϧΎϴϣ ϦϴΑ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ ANOVA ϝϭΪΟ έΩ βϧΎҨέϭ ΰϴϟΎϧ ί ϩΪϣ ΖγΪΑ ΞҨΎΘϧ ̋ Ϣϫ ΎΑ ϭΩ ϪΑϭΩ Post HOC ϪϨҨΰ̳ ί ϩΩΎϔΘγ ΎΑ ϭ ϢϴηΎΑ ΎϬϓϼΘΧ ϝΎΒϧΩ ϪΑ ΪҨΎΑ β̡ ΩέΩ ΩϮΟϭ ̵έΩ ̶Ϩόϣ ϑϼΘΧ ΕϼϴμΤΗ ϢϴϨ̯ ϪδҨΎϘϣ
ab
lin
e.
co
m
ΩέΩ ΩϮΟϭ ΖϟΎΣ ϭΩ Post HOC ϩήΠϨ̡ έΩ ΪϨηΎΑ ϪΘηΪϧ ̶ΗϭΎϔΗ ϊϣϮΟ βϧΎϳέϭ Ϫ̯ ̶ΘϟΎΣ έΩ ϩΩΎϔΘγ ΩέϮϣ ̵ΎϬϧϮϣί ϪΑ ρϮΑήϣ ϻΎΑ ΖϤδϗ Equal Variances Assumed ΪϨηΎΑ ΕϭΎϔΘϣ ϊϣϮΟ βϧΎϳέϭ Ϫ̯ ̶ΘϟΎΣ έΩ ϩΩΎϔΘγ ΩέϮϣ ̵ΎϬϧϮϣί ϪΑ ρϮΑήϣ ϦϴϳΎ̡ ΖϤδϗ Equal Variances Not Assumed έϮσ ϪΑ ΪϧέΪϧ ̵έΩ ̶Ϩόϣ ϑϼΘΧ Ϣϫ ΎΑ ΎϬϫϭή̳ βϧΎϳέϭ Ϫ̯ ̶ΘϟΎΣ έΩ ΎϬδϧΎҨέϭ ̵ήΑήΑ ϻΎΑ ΖϤδϗ έΩ ΩϮη ̶ϣ ΏΎΨΘϧ ̵έΎϣ ϞϴϠΤΗ ϭ ϪϳΰΠΗ ̵ήΑ έ Dunnett ˬDuncanˬTukey ϝϭΪΘϣ ϥϮϣί Ϫγ ϪϧϮϤϧ ϩϭή̳ ̋ ΎϬϫϭή̳ ί ̶̰ϳ ϥΎϴϣ ί Ϫ̯ ΩΩ έήϗ ϪΟϮΗ ΩέϮϣ ΪϳΎΑ έ ϪΘ̰ϧ Ϧϳ Dunnett ϥϮϣί ΩέϮϣ έΩ Ϧϳ ΪϨΠϨδΑ ϥ ΎΑ έ ΎϬϫϭή̳ ήϳΎγ ΎΗ Ϣϳήϴ̳ ̶ϣ ήψϧ έΩ ΪϫΎη ϝήΘϨ̯ ϩϭή̳ ϥϮϨϋ ϪΑ έ ̶̰ϳ ̶ϠϴμΤΗ ϩϭή̳ ϥϮϨϋ ϪΑ ΎϬϫϭή̳ Ϧϳ ί ϡΪ̯ ήϫ ΏΎΨΘϧ ΪηΎΑ Last ήΧ ϩϭή̳ Ύϳ First ϝϭ ϩϭή̳ ϥϮΗ ̶ϣ ϩϭή̳ Control ϪϨϳΰ̳ Dunnett ϥϮϣί ϥΩή̯ ϝΎόϓ ΎΑ έΎ̯ Ϧϳ ̵ήΑ ΪϨ̯ ̶Ϥϧ ΩΎΠϳ ̵ήϴϴϐΗ ΞϳΎΘϧ έΩ ήΧ Ύϳ ϝϭ ήΑ ̮ϴϠ̯ ΎΑ ΪόΑ ϪϠΣήϣ έΩ ϢϴϨ̯ ̶ϣ ΏΎΨΘϧ έ ϝήΘϨ̯ ϩϭή̳ ϥ ̵ϭήΑϭέ ϊΑήϣ έΩ ϭ ϩΪη ϝΎόϓ Category έ ήψϧ ΩέϮϣ ̵ΎϬϴΟϭήΧ ϢϴϧϮΗ ̶ϣ Ok ̵ϭέ ήΑ ̮ϴϠ̯ ΎΑ ϭ Ϣϳϭέ ̶ϣ ̶ϠΒϗ ϩήΠϨ̡ ϪΑ Continue ̵ϭέ ϢϴϨ̯ ϩΪϫΎθϣ
et
ˬ Games Howell ˬ Dunnet’s T3 ˬ Tamhane’s T2 ̵ΎϬϧϮϣί ΎϬδϧΎҨέϭ ̵ήΑήΑΎϧ ϦϴҨΎ̡ ΖϤδϗ έΩ ΪϧϮη ̶ϣ ϩΩΎϔΘγ Dunnet’s C
w
w w
.K
έ ΎϬδϧΎϳέϭ ̵ήΑήΑ Ϫ̯ Ho νήϓ ή̴ϳΩ ϥΎϴΑ ϪΑ ˬΪθϧ ϪΘϓήϳά̡ ΎϬδϧΎϳέϭ ̶Ϩ̴Ϥϫ νήϓ ϥϮ̩ ϝΎΜϣ Ϧϳ έΩ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ ̶ϨϴҨΎ̡ ̵ΎϬϧϮϣί ί Ζγ ϩΪη Ωέ ΪϨ̯ ̶ϣ Ρήτϣ έΩ ΪϨΘδϫ ̵ϭΎδϣ ˱ΎΒҨήϘΗ ΎҨ ̵ϭΎδϣ ϪϧϮϤϧ ̵Ύϫ ϩίΪϧ Ϫ̯ ̶όϗϮϣ ΎΗ F ϩέΎϣ ˬ post hoc ΞҨΎΘϧέΩ ϪΘ̰ϧ ΞҨΎΘϧ ϭ Ζγ ϥϮΗ ΪϗΎϓ ̵ϭΎδϣΎϧ ϪϧϮϤϧ ϩίΪϧ ΎΑ ̶ϟϭ ΪϨ̯ ̶ϣ ΖϣϭΎϘϣ ΰϴϧ ̵ϭΎδϣΎϧ ̵Ύϫ βϧΎҨέϭ ήΑήΑ ΪϫΩ ̶ϣ ϴΤλΎϧ ΞҨΎΘϧ ϦҨ ϪΑ Ϫ̯ ϢϴΘδϫ ϝΩϭΩ Ύϣ ΪηΎΑ ˹ ˹̋ ̮ҨΩΰϧ sig ˬF ϩέΎϣ ΩέϮϣ έΩ ϭ ΪϧΩϮΒϧ ήΑήΑ Ύϫ βϧΎҨέϭ ή̳ ϮϫΩή̯ ΏΎΨΘϧ ΰϴϧ έ Welch,Brown-Forsythe ̵Ύϫ ϪϨҨΰ̳ option ϪϤ̳Ω έΩ άϟ ˮϪϧ ΎҨ ϢϴϨ̰Α ΩΎϤΘϋ ϢϴϨ̯ ήΟ έ ANOVA ϩέΎΑϭΩ ϭ ϩΩή̯ ϑάΣ έ Εή̡ ήҨΩΎϘϣ ΪҨΎΑ ΪϴҨΎΗ ΕέϮλ έΩ ϢϴϨ̯ ̶ϣ ̶γέήΑ 40
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
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 έΎϬ̩ ήϫ ϪΠϴΘϧ m2 ϭ m1 ϞΜϣ Ζγ ήΑήΑ Ϣϫ ήγ Ζθ̡ ϱΎϫ ϩϭή̳ έΩ ΪϣέΩ Ϧϴ̴ϧΎϴϣ ˺ m 2 m3 m3 ϭ m1 ϞΜϣ Ζδϴϧ ήΑήΑ ϪϠλΎϓ ΎΑ ϱΎϬϫϭή̳ έΩ ϲϟϭ m 4 m3 m 4 m5
41
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
(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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř
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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
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íì æ çç ± ºnÀa Ä ¸m Ωή̯ ϩΩΎϔΘγ ΩέΪϧΎΘγ ήϴϐΘϣ ί ϥϮΘϴϣ ΪϨηΎΒϧ ΪΣϭ Ϣϫ ήϴϐΘϣ ϭΩ ˬϪδҨΎϘϣ ΖϬΟ ή̳ ϪΘ̰ϧ ϥ ΝήΨϣ ϭ ήδ̯ ΕέϮλ ΪΣϭ s ήΑ ϢϴδϘΗ ΎΑ ϭ ΩϮη ̶ϣ ήϔλ ̶ϨΤϨϣ ΪΒϣ ί νήϋ x x ϪΒγΎΤϣΎΑ xx z ΩϮΑ ΪϫϮΧ ΪΣϭ ̶Α z ΖҨΎϬϧ έΩ ϭ ϩΪη ̶̰Ҩ s ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ COMPUTE έϮΘγΩ ί SPSS έΰϓ ϡήϧ έΩ z ϪΒγΎΤϣ ̵ήΑ įźŤƯřŹŚěŚƳ įŚƷ ƱƺƯŻō
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̵ήΘϣέΎ̡Ύϧ ϥϮϣί ί ϩΩΎϔΘγ ϞϻΩ ΪηΎΒϧ ϝΎϣήϧ Ύϫ ϩΩΩ ϊҨίϮΗ ΪηΎΑ ΩΎҨί ̶̳ΪϨ̯ή̡ ϑϼΘΧ ΪηΎΑ Ϣ̯ ϪϧϮϤϧ ϩίΪϧ ˮϢϴϨ̯ ̶Ϥϧ ϩΩΎϔΘγ ̵ήΘϣέΎ̡Ύϧ ϥϮϣί ί ϡίϻ ςҨήη ϦΘδϧΩ ϥϭΪΑ ΪΘΑ ί ή̩ Ζγ Ϣ̯ ΪηΎΑ ϝΎϣήϧ Ύϫ ϩΩΩ ϊҨίϮΗ Ϫ̯ ̶τҨήη έΩ ϥϮϣί ϦҨ ϥϮΗ ήҨί ήϔλ νήϓ Ωέ ̵ήΑ ϭ Ζγ ̶ҨΎϋΩ νήϓ ˬϞΑΎϘϣ νήϓ Ύϣ ΪϨ̯ ̶ϣ κΨθϣ έ ΖϴόϤΟ ϊҨίϮΗ ήϔλ νήϓ ϢϴϨ̯ Ωέ έ ήϔλ νήϓ ϪϧϮϤϧ ̮Ҩ Ϫέ ΎΑ Ϫ̯ Ζγ ϦҨ ϥϮϣί ϑΪϫ ϭ Ζγ ϩΪη ΎϋΩ Ωέ ήϔλ Ϫϴοήϓ Ϫ̯ ϢҨ ϩΩΩ ϥΎθϧ ϪϧϮϤϧ ̮Ҩ ΎΑ ςϘϓ ϥϮ̩ Ζγ ϴΤλ ϞΑΎϘϣ νήϓ ϢϴҨϮ̳ ̶Ϥϧ Ζϗϭ ̨ϴϫ Ζδϴϧ ϞΑΎϘϣ Ϫϴοήϓ ΕΎΒΛ ϞϴϟΩ ϦҨ ̶ϟϭ Ϊη ϥϮϣί ̵ήΑ Ύϣ ̶Ϡλ ϑΪϫ ΪҨϴϣ ΖγΪΑ ϪϧϮϤϧ ί ϭ Ζγ ήϔλ νήϓ Ωέ ̵ήΑ έΪϘϣ ϦҨήΘϤ̯ p value ̵ήΑ Ζγ ϩΪη ϡΎΠϧ ̵ ϩΩϮϬϴΑέΎ̯ ϥϮϣί ϦҨΎΑ Ϫϧή̳ϭ Ζγ H 0 Ϫϴοήϓ Ωέ ϭ p value ϥΪη ̮̩Ϯ̯ έ κΧΎηϭ ϢҨήϴ̴Α ̶ϟϮϤόϣ ϪϧϮϤϧ ϢΒϨ̯ ΏΎΨΘϧ ΖγέΩ έ Ϫϴοήϓ ϝϭί Ζγ ήΘϬΑέΎ̰ϨҨ ί ̵ήϴ̳ϮϠΟ ΪϨ̯ ϞϤΤΗΪϧϮΘϴϣ ϖϘΤϣ Ϫ̯ Ζγ ̶ҨΎτΧ Ϣϫ ϢϴϨ̯ ΏΎΨΘϧ έ ϪϴϘΑ ϭ ϪϧϮϤϧ ϢΠΣΪόΑ ϢϴϨ̯ ΝήΨΘγ ϥϮϣί ϥϮΗ ϢϴϤμΗ ϢҨ ϩΩϮΑ ΎτΧ ϥϭΪΑ ΩϮη ςϠϏ Ϣϫ ϪϧϮϤϧ ϥϮϣί ςγϮΗ ϭ ΪηΎΑ ςϠϏ ήϔλ νήϓ Ύ˱όϗϭ ή̳ ϢҨ ϩΪη ΎτΧ έΎ̩Ω ΕέϮμϨҨ ήϴϏ έΩ Ϫϧή̳ϭ ΖγέΩ Ζγ ϩΩϮΑ ϴΤλ ΖϴόϤΟ έΩ ήϔλ νήϓ Ϫ̯ ̶σήη ϪΑ ϪϧϮϤϧ ςγϮΗ ήϔλ νήϓ Ωέ ϝϭ ωϮϧ ̵ΎτΧ Ζγ ϩΩϮΑ ςϠϏ ΖϴόϤΟ έΩ ήϔλ νήϓ Ϫ̯ ̶σήη ϪΑ ϪϧϮϤϧ ςγϮΗ ήϔλ νήϓ ϝϮΒϗ ϡϭΩ ωϮϧ ̵ΎτΧ 1 ϥΎϨϴϤσ τγ 1 ϥϮϣί ϥϮΗ H 0 Ωέ ϡΪϋ H 0 Ωέ ϝϭ ωϮϧ ̵ΎτΧ ϭ ̵έΩ ̶Ϩόϣ τγ 1 ϡϭΩ ωϮϧ ̵ΎτΧ ϴΤλ H 0 ςϠϏ H 0 1 ΪΘϓ ̶ϣ ϥΎϨϴϤσ ϪϠλΎϓ έΩ x ϥ ̵ΎΗ ̂˾ Ϧϴ̴ϧΎϴϣ ϢҨήϴ̴Α ϪϧϮϤϧ ˺˹˹ ή̳ ̶ϨόҨ ̂˾ ϥΎϨϴϤσ τγ έΩ ϭ ˹ ˹˺ ̶̰ηΰ̡ έΩΪϨηΎΑ ϪΘηΩ ̵ήΘϤ̯ ϝϭ ωϮϧ ̵ΎτΧ Ϫ̯ ϢϴϨ̯ ϩΩΎϔΘγ ΪҨΎΑ ̶ҨΎϬϧϮϣί ί ˹ ˹˾ ̶ΘόϨλ ϥ ϥΩϮΑ ςϠϏ ρήη ϪΑ ήϔλ νήϓ Ωέ ̶ϨόҨ ΪηΎΑ ϦҨήΘθϴΑ ϥ 1 Ϫ̯ Ζγ ̶ϧϮϣί ϥϮϣί ϦҨήΗ ϥϮΗή̡
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ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ ήҨί έϮΘγΩ ί SPSS έΩ ̵ήΘϣέΎ̡Ύϧ ̵Ύϫ ϥϮϣί ϡΎΠϧ ̵ήΑ Analyze – Nonparametric Tests – 1-sample k-s…
Analyze – Nonparametric Tests – 2 Independent samples
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Ϫ̯ ΪϫΪϴϣ ϥΎθϧ Kolmogorov- Smirnov Z ϑϮϧήϴϤγ ± ϑϭή̳ϮϤϟϮ̯ εϭέ ί ϩΩΎϔΘγ ΎΑ ϥϮϣί ϦҨ ̶ҨΎϤϧ ΎҨ ϥϮγϮ̡ ˬΖΧϮϨ̰Ҩ ˬϝΎϣήϧ ˮΖγ ̶ϋϮϧ Ϫ̩ ϩΪη ΏΎΨΘϧ ήϴϐΘϣ ϊҨίϮΗ ϪΒΗέ ϑϼΘΧ ϭ Ωέϭ ̶ϣ ΖγΪΑ έ ϪϧΎϴϣ ˬΪϨ̯ ̶ϣ ϩΩΎϔΘγ ϪϧΎϴϣ ϭ Ύϫ ϩΩΩ ̵ΪϨΑ ϪΒΗέ ί ̵ήΘϣέΎ̡Ύϧ ϥϮϣί Most έΩ ϥϮϣί ϪΠϴΘϧ έΩ ΪηΎΑ ϪΘηΩ ϑϼΘΧ ̶ϠϴΧ ή̳ ϭϭ ΪϨ̯ ̶ϣ ϪΒγΎΤϣ Ύϫ ϩΩΩ ΎΑ έ ϪϧΎϴϣ ϑϼΘΧ έΪϘϣ ϭΩ ϦҨήΘ̳έΰΑ ϖϠτϣ έΪϘϣ Absolute ϥϮϨϋ ΎΑ ϑϼΘΧ έΪϘϣ Ϧϴϟϭ ΪδҨϮϧ ̶ϣ Extreme ΩέΩ ϡίϻ ΪηΎΑ ̶ϣ ϥ ήҨί έΩ ϩΪη ̟Ύ̩ ήҨί έϮΘγΩ ίΪηΎΒϧ ϝΎϣήϧ ϊҨίϮΗΎҨ Ϣ̯ ϪϧϮϤϧ ΩΪόΗ ̶Θϗϭ ϪϧϮϤϧ ϭΩ ϦϴΑ Ϧϴ̴ϧΎϴϣ ϪδҨΎϘϣ ϥϮϣί ̵ήΑ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ
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ϥϮϣί ωϮϧ Kolmogorov- Smirnov Z ϑϮϧήϴϤγ ± ϑϭή̳ϮϤϟϮ̯ ˺ έΩ ϪδҨΎϘϣ ̵ήΑ ΰϴϧ έ ϊҨίϮΗ Ϟ̰η ̵ΰ̯ήϣ κΧΎη ήΑ ϩϭϼϋ ΪηΎΒϧ ϪϴΒη ϪϧϮϤϧ ϭΩ ήϫ ϊҨίϮΗ Ϫ̯ ̶ΗέϮλέΩ Ωήϴ̳ ̶ϣ ήψϧ Ζγ ήΘΒγΎϨϣ ϩέΎϣ ϦҨ Man-Whitney U ̶ϨΘҨϭ Ϧϣ ˻ ΎΑ ϭ Ζγ ΐγΎϨϣ ̵ΰ̯ήϣ ̵Ύϫ κΧΎη ϪδҨΎϘϣ ̵ήΑ ΪϨ̯ ̶ϣ ΏΎδΣ έ ϩέΎϣ Ύϫ ϪΒΗέ ϊϤΟ ί ϩΩΎϔΘγ Moses extreme reactions αίϮϣ ˼ ϩϭή̳ ϦϴΑ ϪδҨΎϘϣ ϭ ΩέΩ Ϊϴ̯ΎΗ ΎϬϧ ϪΒΗέ ϭ ̶ҨΎϬΘϧ έΪϘϣ ήΑ ΪϫΩ ̶ϣ ϡΎΠϧ ̶ҨΎϬΘϧ έΪϘϣ ̵ϭέ έ ζҨΎϣί ϭ ΪϫΎη Wald wolfowitz runs Ϊϟϭ ̊ ΪϨ̯ ̶ϣ ϪδҨΎϘϣ έ ϊҨίϮΗ Ϟ̰η ϑϭή̳ϮϤϟϮ̯ ΪϨϧΎϣ ϭ ΪϨ̯ ̶ϣ ϩΩΎϔΘγ ρϮϠΨϣ ̵Ύϫ ϪΒΗέ ί
ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ ήҨί έϮΘγΩ ί ϩϭή̳ ΪϨ̩ ϦϴΑ Ϧϴ̴ϧΎϴϣ ϪδҨΎϘϣ ϥϮϣί ̵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 ϞϘΘδϣ ̵Ύϫ ϪϧϮϤϧ ήϔϧ ϭΩ ̵ήΑ ϭέΩ ήϴΛΎΗ ζҨΎϣί ˱ϼΜϣ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ ήҨί ΕέϮΘγΩ ί ΪϨηΎΑ ςΒΗήϣ ΪϧήҨά̡ ̶ϣ ήϴΛΎΗ Ϣϫ ί ϭ Ζγ ςΒΗήϣ ϭέΩ ί ΪόΑ ϭ ϞΒϗ ήϔϧ ̮Ҩ ̵ήΑ Ύϣ Ζγ 47
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Ʊřźƿř żƿř ƺŤǀŤƀƳř Ύϫ ΖϔΟ Ϧϴ̴ϧΎϴϣ ϪδҨΎϘϣ ϥϮϣί
Analyze – Nonparametric Tests –2 Related samples
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ϥϮϣί ωϮϧ Ύϫ ϩΩΩ 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˰ϬΟ ς˰Ϙϓ ̶˰ϟϭ Ζγ ̶ҨϻΎΑ ϞΜϣ Sign ˻ ΩϮη ̶ϣ ϪΘϓή̳ ήψϧ έΩ Ύϫ ϪΒΗέ ϑϼΘΧ Ζ˰γέΩ ΕέϮ˰λ ϪΑ Ύϫ ϩΩΩή̳ McNemar ˼ Ϧ˰˰Ҩ ί Ζ˰˰γ ή˰˰ΘϬΑ ˺ϭ˹ ΪϨΘδ˰˰ϫ ς˰˰ϠϏ ϭ ϥϮϣί Ϧϳ ί ϻϮϤόϣ ΪϴϨϛ ϩΩΎϔΘγ ϥϮϣί ΩϮηϲϣ ϪΘϓή̳ έΎϛ ϪΑ έΎΘϓέ Ϛϳ ί ΪόΑ ϭ ϞΒϗ ϪδϳΎϘϣ ϱήΑ ϩϭή˰̳ ˬΎ˰ϫϩΩΩ Ϊ˰ϴϨϛ ϩΩΎϔΘ˰γ εϭέ Ϧ˰ϳ ί McNemar εϭέ ί ϲ˰ϤϴϤόΗ ΪϨηΎΑ ϲϳΎΗΪϨ̩ ϱΎϫ ϩΩΩ ή̳ ΪϨΘδϫ ϱΪϨΑ ϲϳΎΗΪϨ̩ ϱΎϫϪϧϮϤϧ Ϧϴ̴ϧΎϴϣ ϪδϳΎϘϣ ϥϮϣί Analyze – Nonparametric Tests – K Related samples
ϥϮϣί ωϮϧ
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ϱήΘϣέΎ̡ ϱΎϬηϭέ ΪϨϧΎϣ ϦϣΪϳήϓ ϥϮϣί ˺ έ ήϴϐΘϣ ΪϨ̩ ϊϳίϮΗ Ϧϴ̴ϧΎϴϣ ϪδϳΎϘϣ ϱέά̳ ϪΒΗέ ί ϩΩΎϔΘγ ΎΑ ΪϨϛ ϲϣ ϥϮϣί ϥϮϣί Ύϫ ϪΒΗέ ϪδϳΎϘϣ ϭ ϩϭή̳ ήϫ ϱήΑ ΩϮη ϲϣ ϡΎΠϧ ϖϓϮΗ ί ϩΩΎϔΘγ ΎΑ ϝΪϨϛ ϥϮϣί ˻ ϡΎΠϧ έ ϥϮϣί Ϧϳ ϱ ϪΒΗέ ϲ̴ΘδΒϤϫ
ΪϫΩϲϣ ϦϣΪϳήϓ ϥϮϣί ΪϨϧΎϣ ΰϴϧ ϥήϛΎϛ ϥϮϣί ˼ ˺ϭ˹ ϲϳΎΗϭΩ ϱΎϫ ϩΩΩ ΎΑ ϲϟϭ ϩΩήϛ ϞϤϋ ϪδϳΎϘϣ ϱήΑ ΪϫΩ ϲϣ ϡΎΠϧ έ ΕΎΒγΎΤϣ Ζγ ΐγΎϨϣ ςϠϏ ϭ ΖγέΩ
48
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Ʊřźƿř żƿř ƺŤǀŤƀƳř
m
ΖΒΛ ϪϴϧΎΛ ΐδΣ ήΑ Ϊϧέά̴Α ΩϮΧ ̵ΎΟ έΩ έ ΐγΎϨϣ Ϫότϗ ΎΗ ΪϨϨ̰ϴϣ ϑήλ Ϫ̪Α ˺˹ Ϫ̯ έ ̶ϧΎϣί ϝΎΜϣ ϢҨ ϩΩή̯ ΪϴҨΎϤϧ ϪΒγΎΤϣ έ ˯Ύϴη κϴΨθΗ ϥΎϣί ̵ήΑ ̶ϔϴλϮΗ ̵ΎϫέΎϣ ˺ ŦƬŨƯ Ƶźƿřŵ ƖŝźƯ ̶̳ΪϨ̯ή̡ ϭ ΰ̯ήϤΗ ̵ΎϫέΎϴόϣ ççå èåå çëå ΪϴϨ̯ Ϣγέ ϥΎϣί Ϫγ ϦҨ ̵ήΑ ̵ ϪδҨΎϘϣ έΩϮϤϧ ̮Ҩ ˻ çêå çîå èåå ˮΖγ ϝΎϣήϧ ϊҨίϮΗ ̵έΩ Ϟ̰η Ϫγ ήϫ ̵ήΑ κϴΨθΗ ϥΎϣί ΎҨ ˼ ϥΎϣί Ϧϴ̴ϧΎϴϣ ϦϴΑ ΪϴϫΩ ϥΎθϧ Boxplot έΩϮϤϧ ί ϩΩΎϔΘγ ΎΑ ˽ çëå çíå çîå ΩέΩ ΩϮΟϭ ̵έΩ ̶Ϩόϣ ϑϼΘΧ ϩϭή̳ Ϫγ ϦҨ έΩ κϴΨθΗ çèå èéå æîå ϭ ̵ήΘϣέΎ̡ ΪϴϨ̯ ϥϮϣί έ ϻΎΑ νήϓ ̵έΎϣ ̵Ύϫ ϥϮϣί ί ϩΩΎϔΘγ ΎΑ ̋ æîå èåå çêå ̵ήΘϣέΎ̡Ύϧ ççå çìå çéå çêå èçå çìå çíå çîå çëå
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ΏϮΟ ΩέΩ ΩϮΟϭ ϩέ ϭΩ ΎϫήϴϐΘϣ ΖϴϴόΗ ϭ Ύϫ ϩΩΩ ΖΒΛ ̵ήΑ έΩ Ϫ̯ ϥΎϣί ̶ϣϭΩ ϭ ΚϠΜϣ ˬϩήҨΩ ˬϊΑήϣ ϞϣΎη Ϟ̰η ήϴϐΘϣ ̶ϟϭ ϢҨήϴ̴Α ήψϧ έΩ ήϴϐΘϣ ϭΩ ϝϭ ΖϟΎΣ ϢҨέΩ ΩέϮϣ ˼˹ ΕέϮμϨҨ ϢҨέΩ ΩέϮϣ ˺˹ ΖϟΎΣ ϦҨ έΩ Ϫ̯ ΚϠΜϣ ˬϩήҨΩ ˬ ϊΑήϣ ϢҨήϴ̴Α ήψϧ έΩ ήϴϐΘϣ Ϫγ ϡϭΩ ΖϟΎΣ Ωή̯ ϩΩΎϔΘγ ϥϮΗ ̶ϣ ϩέϭΩ ί ̶ϔϴλϮΗ ̵ΎϫέΎϣ ̵ήΑ ˺ Analyze – Descriptive statistic- descriptive Analyze – Descriptive statistic- Explore objects ΖϤδϗ Ϫγ έΩ έ ϥΎϣί ήϴϐΘϣ ϥϮΘϴϣ Explore ί ϩΩΎϔΘγ ΎΑ Ϫ̯ Ζγ ϦҨ ϩέϭΩ ϦҨ ϕήϓ ΩϮϤϧ ϩΪϫΎθϣ ϪϧΎ̳ΪΟ 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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Ʊřźƿř żƿř ƺŤǀŤƀƳř
:̵ ϪδҨΎϘϣ έΩϮϤϧ ˻ 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
ϝΎϣήϧ ϊҨίϮΗ ΩϮΟϭ ˼
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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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
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Analyze – Nonparametric Tests – 1-sample k-s…
̵ΪϨΑ ϩϭή̳ ϥϭΪΑ ϝϭ ΖϟΎΣ ϒϟ
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
ΩϮη ̶Ϥϧ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ
̵ΪϨΑ ϩϭή̳ ΎΑ ϡϭΩ ΖϟΎΣ Ώ
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 ϥΩϮΑ ήΘ̳έΰΑ ϪΑ ϪΟϮΗ ΎΑ ˺
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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ϊΑήϣ ϩϭή̳ έΩ ΩϮη ̶Ϥϧ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ Sig = p value 0.784 ! 0.05 =
ϪΠϴΘϧ ί p value ϥΩϮΑ ήΘ̳έΰΑ ϪΑ ϪΟϮΗ ΎΑ ˺
ϩήҨΩ ϩϭή̳ έΩ ΩϮη ̶Ϥϧ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ ί p value ϥΩϮΑ ήΘ̳έΰΑ ϪΑ ϪΟϮΗ ΎΑ ˻ Sig = p value 0.556 ! 0.05 =
ΚϠΜϣ ϩϭή̳ έΩ ΩϮη ̶Ϥϧ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ ί p value ϥΩϮΑ ήΘ̳έΰΑ ϪΑ ϪΟϮΗ ΎΑ ˼ Sig = p value 0.995 ! 0.05 =
m
Boxplot έΩϮϤϧ ΎΑ ϩϭή̳ Ϫγ έΩ ϥΎϣί Ϧϴ̴ϧΎϴϣ ϑϼΘΧ ˽ ΪηΎΒϧ split files ΖϟΎΣ έΩ ϪΘ̰ϧ 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 έΩϮϤϧ ΎΑ ΕϭΎπϗ ̶ϟϭ ΪϫΪϴϣ ϥΎθϧ ΕϭΎϔΘϣ ϝΎ̰η ϩϭή̳ Ϫγ έΩ ϥΎϣί ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ ϪΠϴΘϧ Ωή̯ ϥϮϣί ΪҨΎΑ Ζδϴϧ 52
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Ʊřźƿř żƿř ƺŤǀŤƀƳř ϥϮϣί ˾ ̵ήΘϣέΎ̡Ύϧ ϝϭ ΖϟΎΣ
H 0 : m1 m 2 m 3
Square ϊΑήϣ ϩϭή̳ έΩ ϥΎϣί ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1 H 1 : m1 m 2 m 3 circle ϩήҨΩ ϩϭή̳ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2 Triangle ΚϠΜϣ ϩϭή̳ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ 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 -
̵ήΘϣέΎ̡ ϡϭΩ ΖϟΎΣ
ab
Sig = p value 0.000 0.05 =
lin
ϪΠϴΘϧ ΩϮη ̶ϣ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ ί p value ϥΩϮΑ ήΘ̰̩Ϯ̯ ϪΑ ϪΟϮΗ ΎΑ ˺
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 ΩϮη ̶ϣ Ωέ ϥΎϣί ήϴϐΘϣ ϊҨίϮΗ ϥΩϮΑ ϝΎϣήϧ H 0 νήϓ ˬ ί p value ϥΩϮΑ ήΘ̰̩Ϯ̯ ϪΑ ϪΟϮΗ ΎΑ ˺ Sig = p value 0.000 0.05 =
ΖγΎϴη ̵ΪϨΑ ϩϭή̳ ί ̶ηΎϧ ϭ Ζγ έΩ ̶Ϩόϣ ϑϼΘΧ ˻ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ Test of Homogeneity of Variances ϝϭΪΟ έΩ ΎϬδϧΎҨέϭ ϪδҨΎϘϣ ϥϮϟ ϥϮϣί ί ϩΪϣ ΖγΪΑ ΞҨΎΘϧ ˼ p value 0.936 = Sig ! ϭ ΪϧέΪϧ ̵έΩ ̶Ϩόϣ ϑϼΘΧ ΎϴηήϴϐΘϣ ϩϭή̳ ˼ έΩ ϥΎϣί ήϴϐΘϣ ̵Ύϫ βϧΎҨέϭ ̵ήΑήΑ νήϓ ΎΑ ΎϬϨϴ̴ϧΎϴϣ ΎϬϓϼΘΧ ϝΎΒϧΩ ϪΑ ΪҨΎΑ Ϫ̯ ΪϫΪϴϣ ϥΎθϧ ANOVA ϝϭΪΟ έΩ βϧΎҨέϭ ΰϴϟΎϧ ί ϩΪϣ ΖγΪΑ ΞҨΎΘϧ ̊ ϢϴϨ̯ ϪδҨΎϘϣ Ϣϫ ΎΑ ϭΩ ϪΑϭΩ Post HOC ϪϨҨΰ̳ ί ϩΩΎϔΘγ ΎΑ ϭ ϢϴηΎΑ ΎϬδϧΎҨέϭ
53
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
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ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ postHoc έϮΘγΩ ί ϑϼΘΧ ΖϠϋ ϦΘϓΎҨ ̵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.
.
ϭΩ ϪΑ ϭΩ ϪδҨΎϘϣ ϥϮϣί ϪΠϴΘϧ Ζδϴϧ ήΑήΑ ϩήҨΩ ϭ ϊΑήϣ ̵Ύϫ ϩϭή̳ έΩ ϥΎϣί Ϧϴ̴ϧΎϴϣ ˺ Ζδϴϧ ήΑήΑ ϩήҨΩ ϭ ΚϠΜϣ ̵Ύϫ ϩϭή̳ έΩ ϥΎϣί Ϧϴ̴ϧΎϴϣ ˻ Ζγ ήΑήΑ ΚϠΜϣ ϭ ϊΑήϣ ̵Ύϫ ϩϭή̳ έΩ ςϘϓ ϥΎϣί Ϧϴ̴ϧΎϴϣ ˼ ΪηΎΑ ̶ϣ ϩήҨΩ ϩϭή̳ ϑϼΘΧ ΖϠϋ ̊ Square ϊΑήϣ ϩϭή̳ έΩ ϥΎϣί ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m1
lin
m1 m3
ab
m1 m2
.K
et
m2 m3
circle ϩήҨΩ ϩϭή̳ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m2 Triangle ΚϠΜϣ ϩϭή̳ έΩ ΪϣέΩ ήϴϐΘϣ Ϧϴ̴ϧΎϴϣ m3
w
w w
54
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íì æ çî ± º Ä ¸m 2 Chi-square ƱƺƯŻō
w
w w
.K
et
ab
lin
e.
co
m
Ωϭήϴϣ έΎ̯ ϪΑ έϮψϨϣ ϭΩ ̵ήΑ ϥϮϣί ϦҨ ˮϪϧ ΎҨ ΪϨ̯ ̶ϣ ̵ϭήϴ̡ ̶λΎΧ ϊҨίϮΗ ί έΩϮϤϧ ϊҨίϮΗ ϖΑΎτΗ ˺ ϪΘδδ̳ ̵Ύϫ ήϴϐΘϣ ̶ϓΩΎμΗ ήϴϐΘϣ ϭΩ ϝϼϘΘγ ˻ ϩΪη ϩΪϫΎθϣ ήҨΩΎϘϣ Oi έΎψΘϧ ΩέϮϣ ήҨΩΎϘϣ Ei 2 ( O E ) i έΎψΘϧ ΩέϮϣ ήҨΩΎϘϣ ϪϧϮϤϧ ̵Ϯγ ί ϩΪη ϩΪϫΎθϣ ϑϼΘΧ ςγϮΘϣ
2 i Ei H 0 : ΪϨ̯ ̶ϣ ΪϴҨΎΗ έ ΖϴόϤΟ ήΘϣέΎ̡ ϪϧϮϤϧ H 1 : ΪϨ̯ ̶Ϥϧ ΪϴҨΎΗ έ ΖϴόϤΟ ήΘϣέΎ̡ ϪϧϮϤϧ 2 ΖϴόϤΟ ϪϧϮϤϧ έΪϘϣ ϥΩϮΑ Ϣ̯ ϭ ΩϮη ̶ϣ Ϣ̯ ΕΎϓϼΘΧ β̡ ΖηΩ έ ΖϴόϤΟ ϊҨίϮΗ ϥΎϤϫ ϪϧϮϤϧ ή̳ ΩϮη ̶ϣ ϪδҨΎϘϣ ̵έΎϣ ϝϭΪΟ ΎΑ ϥ έΪϘϣ ϪΘΒϟ ΪϨ̯ ̶ϣ ΪϴҨΎΗ έ είήΑ ϲϳϮϜϴϧ ± ϝϼϘΘγ ϥϮϣί ΎϬοήϓ ϲϓΩΎμΗ ϪϧϮϤϧ ± ΩέΪϧ ΖϴϤϫ Ύϫ ϩΩΩ ϊϳίϮΗ Ύϳ ϞϜη ± ˺ ϞϗΪΣ ϩϭή̳ ήϫ έΩ ϲϧϭήϓ ϞϗΪΣ ± ΪϨηΎΑ ϪΘηΩ ˾ ί ζϴΑ ϲϧϭήϓ Ύϫ ϩϭή̳ ˻˹ ± ΪϨϛϲϣ ϪδϳΎϘϣ έΎψΘϧ ΩέϮϣ ήϳΩΎϘϣ ΎΑ έ ϩΪη ϩΪ ϫΎθϣ ήϳΩΎϘϣ ˻ ϲΧ ϩέΎϣ ί ϩΩΎϔΘγ ΎΑ ϥϮϣί Ϧϳ ϥϮϣί ϪΑ ϲγήΘγΩ εϭέ - Descriptive Statistics - Crosstabs... ... Analyze ΪϴϨϛ ΏΎΨΘϧ έ chi square ϪϨϳΰ̳ ϭ ΪϴϧΰΑ έ statistic ϪϤϛΩ ΎϫήϴϐΘϣ ϦϴϴόΗίΪόΑ Analyze – nonparametric – chi-square ΎҨ 55
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˾ Ζγ ήΑήΑ ̵ή̴ҨΩ ΎΑ Ύϫ ϩΩΩ ί ̮Ҩ ήϫ ϩΪϫΎθϣ ϝΎϤΘΣ ϭ Ζγ ΖΧϮϨ̰Ҩ ϊҨίϮΗ ΪϴϨ̯ νήϓ ϝΎΜϣ ϢҨέΩ ̵ίΎΑ ΏΎΒγ ϩϭή̳ ˮϪϧ ΎҨ Ζγ ϥΎδ̰Ҩ ΎϬϨҨ ϥΪҨήΧ ϪΑ Ύϫ Ϫ̪Α ϞҨΎϤΗ ϢϴϧΪΑ ϢϴϫϮΧ ̶ϣ ϝϭ ϝϮΌγ ΩϮη ϡΎΠϧ ϝϭ ϩϭή̳ Ϫγ ϦϴΑ ϪδҨΎϘϣ ϢϴϫϮΧ ̶ϣ ± ϡϭΩ ϝϮΌγ ˮΖγ ϡϮγ ϭΩ ϪΑ ϡϮγ ̮Ҩ ήΗϮϴ̢ϣΎ̯ ̵ίΎΑ ϭ ̲ϨϔΗ ϦϴΑ ΪҨήΧ ΖΒδϧ ΎҨ ϡϮγ ϝϮΌγ ̵ίΎΑ ΏΎΒγ ωϮϧ ϩΪη ̵έΪҨήΧ έΪϘϣ
̲ϨϔΗ
ήΗϮϴ̢ϣΎ̯ ̵ίΎΑ
̮γϭήϋ
ϦϴηΎϣ
ϪΧή̩ Ϫγ
˽˾
˻˹
˺˾
˺˾
˾
co
m
ϝϭ ΏϮΟ ϢϴϨ̯ ̶ϣ ϒҨήόΗ num ϭ Typeoftoy ϡΎϧ ϪΑ ήϴϐΘϣ ωϮϧ ϭΩ ˺ ϢϴϫΩ ̶ϣ Ϊ̯ ̵ίΎΑ ΏΎΒγ ωϮϧ ̋ έΩ έ ϝϭ ήϴϐΘϣ ˻ ϢϴϫΩ ̶ϣ ϥίϭ num ήϴϐΘϣ ϪΑ ˼ Analyze – nonparametric – chi-square – Test variable ………Typeoftoy ̊ Ζγ ϩΪη ̊̋ ΩΪϋ Chi-Square(a) έΪϘϣ ̋
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 0.05 ί ήΘ̰̩Ϯ̯ ϭ Asymp. Sig. = 0.000 έΪϘϣ ̌ Ζδϴϧ ̶̰Ҩ ΖϴόϤΟ ήΘϣέΎ̡ ΎΑ ϪϧϮϤϧ ϊҨίϮΗ ϭ ΪϨ̯ ̶ϣ Ωέ ήϔλ νήϓ ϪϧϮϤϧ 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.
ϡϭΩ ΏϮΟ ̵ήΑ ˬ Used specified range ϪϨҨΰ̳ ˬ Expected Range ΖϤδϗ έΩ Chi-Square ϥϮϣί ϩήΠϨ̡ έΩ Ωήϴ̳ ΕέϮλ ˼ ΎΗ ˺ ϩϭή̳ ϦϴΑ ϪδҨΎϘϣ ΎΗ ϩΩή̯ ΏΎΨΘϧ έ upper=3 ̵ήΑ ϭ lower=1 ϢϴϨ̯ ϥΎθΒΗήϣ ϝϭ ̶ΘδҨΎΑ ΩϮΒϧ ΐΗήϣ Ύϣ ήτϧ ΩέϮϣ ̵ΎϬϫϭή̳ ή̳ ΩΩ ϡΎΠϧ έ ϥϮϣί ΪόΑ Ωή̯ ΏΎΨΘϧ ϝϭ select cases έϮΘγΩ ΎΑ έΎϫ ϩΩΩ ϥϮΘϴϣ Ϊη Ωέ ϪϧϮϤϧ ςγϮΗ ήϔλ νήϓ ϥΎϨ̪Ϥϫ ϥϮϣί ϦҨ ϡΎΠϧ ΎΑ 56
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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
ϡϮγ ΏϮΟ ̌̋ ήΗϮϴ̢ϣΎ̯ ̵ίΎΑ ϭ ̲ϨϔΗ ΪҨήΧ ωϮϤΠϣ Ϫ̯ ϢϴϨ̯ ̶ϣ ΏΎδΣ έϮτϨҨ έ ϡϮγ ϭΩ ϪΑ ϡϮγ ̮Ҩ ΖΒδϧ ϢϴϨ̯ ̶ϣ ϢϴδϘΗ ϡϮγ ϭΩ ϪΑ ϡϮγ ̮Ҩ ΖΒδϧ ϪΑ έ ΩΪϋ ϦҨ Ζγ 1 65* 21.7 ˬ Expected Range ΖϤδϗ έΩ Chi-Square ϥϮϣί ϩήΠϨ̡ έΩ 3 ̵ήΑ ϭ lower=1 ̵ήΑ ˬ Used specified range ϪϨҨΰ̳ 2 Ωήϴ̳ ΕέϮλ ˻, ˺ ϩϭή̳ ϦϴΑ ϪδҨΎϘϣ ΎΗ ϩΩή̯ ΏΎΨΘϧ έ upper=2 65* 43.3 3 ΩΪϋ Expected Value ΖϤδϗ έΩ Chi-Square ϥϮϣί ϩήΠϨ̡ έΩ ϢϴϨ̯ ̶ϣ Ωέϭ έ ̊˼ ˼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 0.05 ί ήΘ̳έΰΑ ϭ Asymp. Sig. = 0.655 έΪϘϣ ΪϨ̯ ̶Ϥϧ Ωέ ήϔλ νήϓ ϪϧϮϤϧ 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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Ʊřźƿř żƿř ƺŤǀŤƀƳř
Ζγ ϩΪη ϩΩέϭ ϡΎϧ ΖΒΛ έΎϣ ϩΪϜθϧΩ ϚϴϜϔΗ ϪΑ ϥΎΘγήϬη ϭ ϥΎΘγ ΰϛήϣ ϱΎϬϫΎ̴θϧΩ έΩ ϝΎΜϣ ̶ϨόҨ Ϫϧ ΎҨ ΩέΩ ΖΧϮϨ̰Ҩ ϊҨίϮΗ ̶ϟϮΒϗ ΖΒδϧ ˮϪϧΎҨ ΩέΩ ήϴΛΎΗ ϮΠθϧΩ ΏΎΨΘϧ έΩ ϩΪ̰θϧΩ ωϮϧ ΎҨ ϩΪ̰θϧΩ ϦϴΑ ̶ϟϮΒϗ ΖΒδϧ ΎҨ ˮΪϧϮη ̶ϣ ϢϴδϘΗ ΎϬϫΎ̴θϧΩ ϦϴΑ ΖΒδϧ ̮Ҩ ϪΑ ̶ϟϮΒϗ ΩΪόΗ ΏΎΨΘϧ ϥΎϳϮΠθϧΩ ςγϮΗ ˻˹ ήϨϫ ϭ ́˹ ϲγΪϨϬϣ ϲϨόϳ ˮΖγ ˻˹ ϪΑ ́˹ ήϨϫ ϭ ̶γΪϨϬϣ ΩϮη ϲϣ 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
Ύϫ ήϴϐΘϣ ΏΎΨΘϧ
Missing column Align
Measure
-
10
left
Scale
-
10
left
Scale
-
10
left
Scale
e.
Type
ab
Name
co
m
ϩΪ̰θϧΩ ωϮϧ
-
w w
.K
et
ϢϴϫΩ ̶ϣ ϥίϭ frequency ήϴϐΘϣ ϪΑ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ chi-square ϥϮϣί ί ΖΧϮϨ̰Ҩ ϊҨίϮΗ ϥϮϣί ̵ήΑ Analyze – nonparametric – chi-square – Test variable ……… faculty
w
ΪϨ̯ ̶ϣ ϥϮϣί έ ϥ ϊҨίϮΗ ϭ Ϊϧί ̶ϣ ϊϤΟ έΎϫ ̶ϟϮΒϗ ΩΪόΗ εΩϮΟ ΩϮη ̶ϣ ϩΪϫΎθϣ Ϫ̯ έϮτϧΎϤϫ ̶ϣ ΪϴҨΎΗ έ ΖϴόϤΟήΘϣέΎ̡ ϪϧϮϤϧ Ζϔ̳ ϥϮΗ ̶ϣ ϭ ΪϨ̯ ̶Ϥϧ Ωέ έ ήϔλ νήϓ ϪϧϮϤϧ Ϫ̯ Ζγ 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.
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50*0.8 40 50*0.2 10
Ʊřźƿř żƿř ƺŤǀŤƀƳř
˻˹ ϪΑ ́˹ ΖΒδϧ ϥϮϣί ̵ήΑ Chi-Square ϥϮϣί ϩήΠϨ̡ έΩ 1,2 ˬ Expected Range ΖϤδϗ έΩ ΩΪϋ Expected Value ΖϤδϗ έΩ ϢϴϨ̯ Ωέϭ έ ˹ ˻ ˹ ́ ΩϮΧ ̶ϨόҨ ΪλέΩ ϪΑ έ ΎϬϧ ΖΒδϧ ΎҨ ϢϴϨ̯ ̶ϣ Ωέϭ έ˺˹ϭ ̊˹ 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.
Ϊη Ωέ ϪϧϮϤϧ ςγϮΗ νήϓ ϦҨ Ϫ̰ϨҨ ϪΠϴΘϧ įř ƶƬưūƹŵ Śƿ Binomial ƱƺƯŻō
w
w w
̵ ϪϠϤΟ ϭΩ ̶ϓΩΎμΗ ήϴϐΘϣ ϒҨήόΗ ΪηΎΑ ϪΘηΩ ̶ΗϭΎϔΘϣ ΞҨΎΘϧ ΩϮη έή̰Η ΎϫέΎΑ ϥΎδ̰Ҩ ςҨήη έΩ ή̳ Ϫ̯ Ζγ ̶θҨΎϣί ̶ϓΩΎμΗ ζҨΎϣί ˮϪϧ ΎҨ ΩϮη ̶ϣ ΪϴҨΎΗ ϪϧϮϤϧ ςγϮΗ ΖϴόϤΟ ̵ϭέ ί ϩΪη ϩΩί αΪΣ P ΩΪΧέ ϡΪϋ Ύϳ ΩΪΧέ Ϫϛ ϲϳΎΠϧ ί ΩϮηϲϣ ϩΪϴΠϨγ Ϊϣ ζϴ̡ Ϛϳ ΩΪΧέ ϝΎϤΘΣ ΖΒδϧ ϥϮϣί Ϧϳ έΩ ΩϮηϲϣ ϩΩΎϔΘγ ϥϮϣί ϱήΑ ϊϳίϮΗ Ϧϳ ί Binomial ϱ ϪϠϤΟϭΩ ϊϳίϮΗ Ϊϣ ζϴ̡ ϥϮϣί Ϧϳ ϪΑ ϲγήΘγΩ εϭέ Analyze - Nonparametric Tests - Binomial... ˮΖγ ˾˹ ϪΑ ˾˹ ΎϬϧΎΘγήϬη ϭ ϥΎΘγ ΰ̯ήϣ έΩ ϥΎҨϮΠθϧΩ ̶ϟϮΒϗ ΖΒδϧ ΎҨ ϝΎΜϣ 1 H 0 : p 2 1 H 1 : p 2 Analyze - Nonparametric Tests - Binomial... variable…………………....place Test propertion………….0.5
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Ʊřźƿř żƿř ƺŤǀŤƀƳř
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. ΪϨ̯ ̶Ϥϧ Ωέ έ ήϔλ νήϓ ϪϧϮϤϧ ϪΠϴΘϧ
lin
e.
co
m
Chi-Square ϥϮϣί ί ϩΩΎϔΘγ ή̴ҨΩ ϩέ 1,2 ˬ Expected Range ΖϤδϗ έΩ ϢϴϨ̯ ̶ϣ Ωέϭ έ 0.5ϭ 0.5 ΩΪϋ Expected Value ΖϤδϗ έΩ Ζγ ήΗΩϭΪΤϣ ̵ ϪϠϤΟ ϭΩ ̶ϟϭ ΩέΩ ̵ήΘθϴΑ ΖϴϣϮϤϋ Chi-Square ϥϮϣί ϪΘ̰ϧ ̀˹ ΖΒδϧ ϪΑ ήҨΎγ ϭ ΩΎμΘϗ ̵ΎϬϫϭή̳ ΎΑ ήϨϫ ϭ ̶γΪϨϬϣ ̵ΎϬϫϭή̳ ϥΎҨϮΠθϧΩ ̶ϟϮΒϗ ΖΒδϧ ΎҨ ϝΎΜϣ ˮϪϧ ΎҨ Ζδϫ ˼˹ ϪΑ H 0 : p ΪϨ̯ ̶ϣ ΏΎΨΘϧ έ ήϨϫ ϭ ̶γΪϨϬϣ ϮΠθϧΩ H 1 : q ΪϨ̯ ̶ϣ ΏΎΨΘϧ έ ήҨΎγ ϭ ΩΎμΘϗ ϮΠθϧΩ Analyze - Nonparametric Tests - Binomial... variable…………………....place Test propertion………….0.7 Cutpoint ………………….2
ab
ΪϨ̯ ΏΎδΣ ή̴ҨΩ ϩϭή̳ ̮Ҩ έ ˻ ί ΪόΑ ϭ ϩϭή̳ ̮Ҩ έ ˻ϭ˺ ϩϭή̳ ̶ϨόҨ Ζγ cutpoint=2 ̶Θϗϭ 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
ΪϨ̯ ̶ϣ Ωέ έ ήϔλ νήϓ ϪϧϮϤϧ Ϫ̰ϨҨ ϪΠϴΘϧ Runs ƱƺƯŻō
ΩϮηϲϣ ϥϮϣί Ύϫ ϩΩΩ ϥΩϮΑ ϲϓΩΎμΗ ϥΎ Ϝϣ Ύϫ ϩΩΩ ϱΪϨΑ ϪΒΗέ ϭ ϥϮϣί Ϧϳ ί ϩΩΎϔΘγ ΎΑ ί ήΗ ϦϴҨΎ̡ ̶̰Ҩ ϭ Ϧϴ̴ϧΎϴϣ ̵ϻΎΑ ̶̰Ҩ ̶ϟϮΘϣ ϩΩΩ ϭΩ Ϫ̯ ΩϮη ̶ϣ ϞλΎΣ ̶ϧΎϣί Ύϫ ϩΩΩ ϥΩϮΑ ̶ϓΩΎμΗ ΪηΎΑ ϥ ϥϮϣί Ϧϳ ϪΑ ϲγήΘγΩ εϭέ Analyze – Nonparametric Tests – Runs Ϧϳ Ϫϛ ΪϴϨϛ ϥϮϣί spss ΕΎϧΎϜϣ ΎΑ ϭ ΪϴϨϛ ΩΎΠϳ ϲϓΩΎμΗ ΩΪϋ ˺˹˹ Rand() ϊΑΎΗ Ϟδϛ ί ϩΩΎϔΘγ ΎΑ ΪϨΘδϫ ϲϓΩΎμΗ ΩΪϋ έΩ data ϲΗΎϋϼσ ΪϨγ ϥΩήϛ ίΎΑ ϡΎ̴Ϩϫ έΩ Ζγ ϲϓΎϛ ΪϴϨϛ έϭήϣ έ Ϟδϛ ί ΕΎϋϼσ Ωϭέϭ ϩϮΤϧ ΪϴϨϛ ΏΎΨΘϧ έ excel ϪϨϳΰ̳ file type ΖϤδϗ 60
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ϢϴϨϴΒΑ ϢϴϫϮΧ ̶ϣ Ϊϧ ϪΘϓή̳ ϩίΪϧ έ ϩΪη ϪΘΧΎγ ̵Ύϫ ϪϟϮϟ ήτϗ ςγϮΘϣ ̵έΎ̯ Ζϔϴη έΎϬ̩ έΩ ϝΎΜϣ ΪҨΎΑ Ύϫ ϪϟϮϟ ήτϗ ΩέΩ ϥΪη ϩήΒϴϟΎ̯ ϪΑ ίΎϴϧ ϩΎ̴ΘγΩ ΎҨ ΪϨΘδϫ ̶ϓΩΎμΗ ϥΎθϨϴ̴ϧΎϴϣ ϪΑ ΖΒδϧ ΩΪϋ ϦҨ ΪηΎΑ ̊ Ύϫήτϗ ςγϮΘϣ 3.8 4.2 3.3 4.5 ˺Ζϔϴη ˻Ζϔϴη
˼Ζϔϴη ̊Ζϔϴη İĮŤƀŞưƷ ƱƺƯŻō
ab
lin
e.
co
m
̶ϣ ϪΒγΎΤϣ έ ϥ ΐҨήο ϭ ΩίΩή̡ ̶ϣ ήϴϐΘϣ ΪϨ̩ ΎҨ ϭΩ ϦϴΑ ρΎΒΗέ ̶γέήΑ ϪΑ ̶̴ΘδΒϤϫ ϪτΑέ ήϴϐΘϣ ΕήϴϴϐΗ ΎΑ ήϴϐΘϣ ̮Ҩ ΕήϴϴϐΗ ή̳ ΪηΎΑ ̶ϔϨϣ ΎҨ ΖΒΜϣ Ζγ Ϧ̰Ϥϣ ΎϫήϴϐΘϣ ϦϴΑ ̶̴ΘδΒϤϫ ΪϨ̯ ϩήϤϫ ̵ή̴ҨΩ ζϫΎ̯ ΎΑ ̶̰Ҩ ζϫΎ̯ β̰όϟΎΑ ΎҨ ̵ή̴ҨΩ ζҨΰϓ ΎΑ ̶̰Ҩ ζҨΰϓ ϭ ΪηΎΑ ϩήϤϫ ̵ή̴ҨΩ +1 ΎΗ 0 ί Ϫ̯ ΪҨήΧ ̵ήΑ ΎοΎϘΗ ϭ ΪϣέΩ ζҨΰϓ ϞΜϣ Ζγ ΖΒΜϣ ΎϬϧ ϦϴΑ ̶̴ΘδΒϤϫ ϢϴҨϮ̳ ̶ϣ ΩϮθΑ ΩέΩ ϥΎγϮϧ ϦϴΑ ̶̴ΘδΒϤϫ Ϫ̯ ΩϮη ̶ϣ ϪΘϔ̳ ΩϮη ϩήϤϫ ̵ή̴ҨΩ ήϴϐΘϣ ζϫΎ̯ ΎΑ ήϴϐΘϣ ̮Ҩ ζҨΰϓ ϭ ήϴϴϐΗ ή̳ ΪϨ̯ ̶ϣ ήϴϴϐΗ -1 ΎΗ 0 ί ϭ Ζγ ̶ϔϨϣ ΎϬϧ Ζγ ήϔλ ΎϬϧ ϦϴΑ ̶̴ΘδΒϤϫ ΐҨήο ΪηΎΑ ϪΘηΪϧ ΩϮΟϭ ̵ ϪτΑέ ήϴϐΘϣ ϭΩ ϦϴΑ ή̳ ΩέΩ ΩϮΟϭ ήϴϐΘϣ ϭΩ ϦϴΑ ϲτΧ ϪτΑέ ΪηΎΑ ̮ϳΩΰϧ -1 Ύϳ1 ϪΑ ϲ̴ΘδΒϤϫ ΐϳήο ή̳ ̶̴ΘδΒϤϫ ΐҨήο
w w
.K
et
Ζγ ήϴϐΘϣ ϭΩ ϦϴΑ ρΎΒΗέ ΩϮΟϭ ϩΪϨϫΩ ϥΎθϧ ΐϳήο Ϧϳ Ζγ ήΘϜϳΩΰϧ ˺ Ύϳ ϭ ˺ ϪΑ ΐϳήο έΪϘϣ ΪηΎΑ ΪϳΪη ήϴϐΘϣ ϭΩ ρΎΒΗέ Ϫ̩ήϫ ΩϮηϲϣ ϚϳΩΰ ϧ ήϔλ ϪΑ ΐϳήο έΪϘϣ ήϴϐΘϣ ϭΩ ϦϴΑ ρΎΒΗέ ζϫΎϛ ΎΑ ΩϮΑ ΪϫϮΧ ήϔλ ήΑήΑ ϲ̴ΘδΒϤϫ ΐϳήο έΪϘϣ ΪϨηΎΑ ϞϘΘδϣ ήϴϐΘϣ ϭΩ ή̳ έΩ Ζϓή̳ ϪΠϴΘϧ έ ϝϼϘΘγ ϥϮΗϲϣ ΪηΎΑ ήϔλ ήΑήΑ ϲ̴ΘδΒϤϫ ΐϳήο ή̳ ΪϨηΎΑ ϝΎϣήϧ ήϴϐΘϣ ϭΩ ή̳ ήϴΧ ΕέϮλ Ϧϳ ήϴϏ
w
̶̴ΘδΒϤϫ ΐҨήο ϪΒγΎΤϣ
ΩϮηϲϣ ϩΩΎϔΘγ Covariance έΪϘϣ ί ϥϮγήϴ̡ ϲ̴ΘδΒϤϫ ΐϳήο ϪΒγΎΤϣ ϱήΑ αΎϴϘϣ ϭ ΪΣϭ ΎΑ Ϫϛ ϲϳΎΠϧ ί ϲϟϭ Ζγ ΐγΎϨϣ ρΎΒΗέ ϥΰϴϣ ϱήϴ̳ϩίΪϧ ϱήΑ CovarianceέΪϘϣ ϥϭΪΑ ΐҨήο ̮ϳ ί ̶̴ΘδΒϤϫ ϥΰϴϣ ϪδҨΎϘϣ ϱήΑ ΪϳΎΑ Ϊϳϲϣ ΖγΪΑ ϩΪη ϱήϴ̳ ϩίΪϧ ϪμΨθϣ Ωήϛ ϩΩΎϔΘγ ΪλέΩ ΪΣϭ ΩέΩ έ ΖϴλΎΧ Ϧϳ ̶̴ΘδΒϤϫ ΐϳήο Ύϳ Correlation ΩϮηϲϣ ϩΩΎϔΘγ ϱ ϪτϘϧ έΩϮϤϧ ί ήϴϐΘϣ ϭΩ ρΎΒΗέ ϩΪϫΎθϣ ϱήΑ ϻϮϤόϣ p ( y / x ) p ( y ) ̶ϨόҨ ̶ϓΩΎμΗ ήϴϐΘϣ ϭΩ Independent ϝϼϘΘγ Ϣϫ ΎΑ ϥΎθϧΩΪҨϭέ Ϫ̰ϠΑ ϥίϭ ϭ Ϊϗ ϞΜϣ ΪϨηΎΑ Ϣϫ ϝϮϠόϣ ϭ ΖϠϋ ϪΘδΒϤϫ ˱ϼϣΎ̯ ήϴϐΘϣ ϭΩ Ζδϴϧ ϡίϻ ΩέΩ ρΎΒΗέ
61
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̶̴ΘδΒϤϫ ςΧ έΩϮϤϧ
co
m
e.
ϥϮγήϴ̡ ̶̴ΘδΒϤϫ ΐҨήο ϪΒγΎΤϣ
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 ΪηΎΑ ϪΘηΩ ΩϮΟϭ ̶̴ΘδΒϤϫ ή̳
w
Analyze –Corrolate – Bivariate … ΪϴϨϛ ϲσ έ ήϳί ήϴδϣ έϮΘγΩ Ϧϳ ϪΑ ϲγήΘγΩ ϱήΑ ΪϳέΩ ΝΎϴΘΣ ϱΩΪϋ ήϴϐΘϣ ϭΩ ϪΑ ϞϗΪΣ έϮΘγΩ Ϧϳ ϱήΟ ϱήΑ ϲ̴ΘδΒϤϫ ΐϳήο ωϮϧ ί Ϊ˰ҨΎΑ ̶Ϥ˰γ ϭ ̵ ϪΒΗέ ˬ ̵ΩΪϋ ̵ΎϬγΎϴϘϣ ί ̮Ҩ ήϫ ̵ήΑ Ωή̯ ϩΩΎϔΘγ ϥΎηΩϮΧ ϪΒγΎΤϣ εϭέ Covariance Correlation ϲ̴ΘδΒϤϫ ϱήΘϣέΎ̡ ϲ̴ΘδΒϤϫ ΐϳήο Pearson ± ϥϮγήϴ̡ ϲ̴ΘδΒϤϫ ΐϳήο ϲτΧ ϲ̴ΘδΒϤϫ
ϝΎ˰ϣήϧ ϊ˰ϳίϮΗ ϱέΩ ϱΩΪ˰ϋ ήϴϐΘϣ ϭΩ ή˰ϫ Ϫϛ Ζγ Ϧϳ ήΑ νήϓ ΪϨΘδϫ ϩήϴϐΘϣ ϭΩ
62
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Ʊřźƿř żƿř ƺŤǀŤƀƳř ϱήΘϣέΎ̡Ύϧ ϲ̴ΘδΒϤϫ ΐϳήο
Spearman Ϧϣήϴ̢γ ϲ̴ΘδΒϤϫ ΐϳήο ϱϪΒΗέ ϲ̴ΘδΒϤϫ Kendall’s Tau_b ±ϝΪϨϛ ϲ̴ΘδΒϤϫ ΐϳήο ϲΒϴΗήΗ ϲ̴ΘδΒϤϫ
H0 : 0 H1 : 0
ϲ̴ΘδΒϤϫ ΐϳήο ϥϮϣί ϲ̴ΘδΒϤϫ ΐϳήο έΪϘϣ ϱήΑ Ϫϓήσ ϭΩ ϥϮϣί Ϛϳ two tail ϪϨϳΰ̳ ΏΎΨΘϧ ΎΑ ΩϮη ϲϣ ϡΎΠϧ
H1 : 0
H1 : 0
co
H0 : 0
e.
H0 : 0
m
One ϪϓήτϜϳ ϥϮϣί ί Ζγ ήΘϬΑ Ζγ ήψϧ έΩ ΰϴϧ ϥ ΖϬΟ ϲ̴ΘδΒϤϫ ΐϳήο έΪϘϣ ήΑ ϩϭϼϋ ή̳ ΩϮη ϩΩΎϔΘγ Tail ϪϓήτϜϳ ϥϮϣί ϪϓήτϜϳ ϥϮϣί
lin
ΩϮηϲϣ ϩΩΩ ϲΟϭήΧ έΩ ϲ̴ΘδΒϤϫ ΐϳήο έΪϘϣ ϥΩϮΑ έΩΎϨόϣ ϦϴϴόΗ ϥΎϜϣ flag… ϪϨϳΰ̳ ΏΎΨΘϧ ΎΑ-
w w
.K
et
ab
Ϧϴ̴ϧΎϴϣ ϲϔϴλϮΗ έΎϣ ζϳΎϤϧ ϥΎϜϣ option ΏΎΨΘϧ ΎΑ Ώήπϣ ϭ βϧΎϳέϮϛ έΪϘϣ ζϳΎϤϧ ϭ ΩέΪϧΎΘγ ϑήΤϧ ϭ ΕέϮλ ϥΎϤϫ ΕϼοΎϔΗ ΐϳήο ΩέΩ ΩϮΟϭ ΕϼοΎϔΗ Ζγ βϧΎϳέϮϛ ϪΒγΎΤϣ ϱήΑ ϪΒγΎΤϣ Exclude Cases pairwise ϪϨϳΰ̳ ΏΎΨΘϧ ΎΑ ή̴ϳΩ έΩ ϩΪθϤ̳ ϩΩΩ ϦΘϓή̳ ήψϧ έΩ ϥϭΪΑ Ύϫ ΖϔΟ ΩϮη ϲϣ ϪΒγΎΤϣ ΩέϮϣ ήϫ ϱΎϫήϴϐΘϣ
w
Ϛϳ ΩϮΟϭ ΎΑ Exclude cases listwise ϪϨϳΰ̳ ΏΎΨΘϧ ΎΑ Ϊη ΪϫϮΧ ϪΘϓή̳ ϩΪϳΩΎϧ ΩέϮϣ Ϟϛ ΩέΩ ϲγέήΑ έΩ ϩΪη Ϣ̳ έΪϘϣ Ϫϛ ήϴϐΘϣ
ϱήΑ ϱΪόΑ ϭΩ βϳήΗΎϣ Ϛϳ ΐϴΗήΗ Ϧϳ ϪΑ ΩέΩ ΩϮΟϭ ϲγέήΑ Ϧϳ έΩ ΰϴϧ ήϴϐΘϣ ϭΩ ί ζϴΑ ΏΎΨΘϧ ϥΎϜϣ Ϊη ΪϫϮΧ ΩΎΠϳ ΎϫήϴϐΘϣ ΖϔΟ ΖϔΟ ϲ̴ΘδΒϤϫ ΐϳήο έΪϘϣ ϪΒγΎΤϣ ϭ ϲγέήΑ ϢϴϨ̯ ̶ϣ ϩΩΎϔΘγ ήҨί έϮΘγΩ ί ̶̴ΘδΒϤϫ έΩϮϤϧ Ϣγέ ̵ήΑ Graph – Legacy dialogs – scatter plot– simple ΎҨ Matrix
63
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íì ç ê ± ºf¨Å Ä ¸m ϭΩ ϝϼϘΘγ ϥϮϣί ̵ήΑ Ϣϫ ϭ ϊҨίϮΗ είήΑ ̵ήΑ ϢϬΑ Chi-square ϥϮϣί Ϫ̯ Ϊη ϪΘϔ̳ ϞΒϗ ϪδϠΟ έΩ ήϴϐΘϣ ϭΩ ϦϴΑ ϪτΑέ ΩϮΟϭ ϪϧΎθϧ ΩϮΑ 0 ή̳ (x,y) ΐΗήϣ Νϭί ̵ήΑ ̶ϨόҨ Ωϭέ ̶ϣ έΎ̰Α ̶ϓΩΎμΗ ήϴϐΘϣ ΪηΎΑ ̶ϣ ϭ ϥΎΘγ ΰ̯ήϣ ί ϒϠΘΨϣ ̵Ύϫ ϩΪ̰θϧΩ έΩ ϥΎҨϮΠθϧΩ ̶ϟϮΒϗ ΖΒδϧ ϪδҨΎϘϣ ϞΒϗ ϪδϠΟ ϝΎΜϣ έΩ ̶Ϥγ ήϴϐΘϣ ϭΩ ήϫ Ϫ̯ ϒϠΘΨϣ ̵Ύϫ ϪΘηέ ϭ ϩΪ̰θϧΩ ϞΤϣ Ϫ̯ ϢϴϧΪΑ ϢϴϫϮΧ ̶ϣ ΎϬϧΎΘγήϬη ˮϪϧ ΎҨ ΪϨϠϘΘδϣ ΪϨΘδϫ
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 ϭ Ϊϧ ϩΩή̯ ̶ΜϨΧ έ ή̴ҨΪϤϫ ΕΎϓϼΘΧ ̶ϨόҨ Ζγ ̮̩Ϯ̯ Pearson Chi-Square=1.524 έΪϘϣ ̶Θϗϭ Ζγ ήϴϐΘϣ ϭΩ ϦϴΑ ϪτΑέ ϩΪϨϫΩ ϥΎθϧ ΪηΎΑ ̱έΰΑ Ωέ έ ήϔλ νήϓ ϪϧϮϤϧ Ϫ̯ ΪϫΩ ̶ϣ ϥΎθϧ ϭ ϩΩϮΑ 0.05 ί ήΘ̳έΰΑ Ϫ̯ Asymp. Sig. (2-sided)=0.677 ήϴϐΘϣ ϭΩ ϦϴΑ ϝϼϘΘγ ΩϮΟϭ ̶ϨόҨ ΪϨ̯ ̶Ϥϧ Ζγ Ρήη ϦҨ ϪΑ ϪΒγΎΤϣ ϝϮϣήϓ ˮΖγ ϩΪϣ ΖγΩ ϪΑ ΎΠ̯ ί 1.524 ΩΪϋ Ϫ̯ ϢϴϧΪΑ ϢϴϫϮΧ ̶ϣ outer ϥϮΘγ ϭ center ϥϮΘγ ϩΪη ϩΪϫΎθϣ ήҨΩΎϘϣ Oi έΎψΘϧ ΩέϮϣ ήҨΩΎϘϣ Ei (Oi Ei ) 2 2 έΎψΘϧ ΩέϮϣ ήҨΩΎϘϣ ϪϧϮϤϧ ̵Ϯγ ί ϩΪη ϩΪϫΎθϣ ϑϼΘΧ ςγϮΘϣ
Ei outer ϥΎΘγήϬη ϥϮΘγ ̵ήΑ center ϥΎΘγ ΰ̯ήϣ ϥϮΘγ ̵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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̶ϣ ϩΩΎϔΘγ ήҨί ϝϮϣήϓ ί ΕϮΘγ ϭ ήτγ ̵Ύϫ ̶ϧϭήϓ ωϮϤΠϣ ί έΎψΘϧ ΩέϮϣ ̶ϧϭήϓ ϪΒγΎΤϣ ̵ήΑ ΩϮη ήτγ έΩ ϩΪη ϩΪϫΎθϣ ̶ϧϭήϓ ϊϤΟ A A* B Ei ϥϮΘγ έΩ ϩΪη ϩΪϫΎθϣ ̵Ύϫ ̶ϧϭήϓ ϊϤΟ B N ϩΪη ϩΪϫΎθϣ ̶ϧϭήϓ ϊϤΟ N ϡ i έΎψΘϧ ΩέϮϣ ̶ϧϭήϓ 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
ΪϨ̯ ΏΎδΣ έ Ύϫ EI ̶ϨόҨ έΎψΘϧ ΩέϮϣ ήҨΩΎϘϣ ΪϧϮΗ ̶ϣ SPSS έΰϓ 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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ΪϴϨ̯ ϲγέήΑ έ ed,carcat ϦϴηΎϣ ωϮϧ ϭ ΕϼϴμΤΗ ϥΰϴϣ ήϴϐΘϣ ϭΩ ϝϼϘΘγ demo ϞϳΎϓ έΩ ϝΎΜϣ 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.
ϥΰϴϣ ΩϮη ̶ϣ Ωέ ήϴϐΘϣ ϭΩ ϝϼϘΘγ ϭ ΪϨ̯ ̶ϣ Ωέ έ ήϔλ νήϓ ϪϧϮϤϧ β̡ p value 0 ϪΠϴΘϧ ϥΎθϧ έ 0.105 ΩΪϋ Ϫ̯ Ζγ ΐγΎϨϣ Ϧϣήϴ̢γ ΪϨΘδϫ ϲϤγ ήϴϐΘϣ ϭΩ ϥϮ̩ ϝϭΪΟ έΩ ϲ̴ΘδΒϤϫ ΪϫΩ ϲϣ 66
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w w
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ab
lin
e.
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m
ήϴϐΘϣ Ϛϳ ϝήΘϨϛ ΕέϮλ έΩ ήϴϐΘϣ ϭΩ ϦϴΑ ϲτΧ ϲ̴ΘδΒϤϫ ϥΰϴϣ ζΠϨγ ϥΎϜϣ ΐϳήο Ϧϳ ί ϩΩΎϔΘγ ΎΑ ΩέΩ ΩϮΟϭ ϥ ήϴΛΎΗ ϑάΣ Ύϳ ϡϮγ ϲ̴ΘδΒϤϫ ΐϳήο ϪΒγΎΤϣ ΎΑ ΪϨηΎΑ ϪΘηΩ ϪτΑέ ΰϴϧ ϱή̴ϳΩ ήϴϐΘϣ Ϫτγϭ ϪΑ ήϴϐΘϣ ϭΩ Ζγ ϦϜϤϣ Ϊη ΪϫϮΧ ϪΒγΎΤϣ ϡϮγ ήϴϐΘϣ ήΛ ϑάΣ ΎΑ ήϴϐΘϣ ϭΩ ϦϴΑ ϲτΧ ϪτΑέ ϥΰϴϣ ϲΰΟ Ζγ ήϳί ΕέϮλ ϪΑ ϪΒγΎΤϣ εϭέ rAB rAC rBC rABC (1 r 2 AC )(1 r 2 BC ) Ζγ ϱΩΪϋ ήϴϐΘϣ ˼ ϞϗΪΣ ϪΑ ΝΎϴΘΣ έϮΘγΩ Ϧϳ ϡΎΠϧ ϱήΑ ΪϴϨϛ ϲσ έ ήϳί ήϴδϣ έϮΘγΩ Ϧϳ ϪΑ ϲγήΘγΩ ϱήΑ Analyze - Correlate - Partial... ϩΩΎγ ϲ̴ΘδΒϤϫ ΐϳήο ϝϭΪΟ ζϳΎϤϧ ϭ ϲϔϴλϮΗ ϱΎϫ ϩέΎϣ ζϳΎϤϧ ϥΎϜϣ Option ϪϨϳΰ̳ ΏΎΨΘϧ ΎΑ ΩέΩ ΩϮΟϭ ΰϴϧ Zero-order…
w
67
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ή˰˰ψϧ έΩ έ age,income,car Ϧϴ˰˰ηΎϣ Ϊ˰˰ϳήΧ Ϫ˰˰Ϩϳΰϫ ϭ Ϊ˰˰ϣέΩ ˬ Ϧ˰˰γ ή˰˰ϴϐΘϣ Ϫ˰˰γ demo Ϟ˰˰ϳΎϓ έΩ ϝΎ˰˰Μϣ Ϊϴθ̰Α ήϴϐΘϣ Ϫγ Ϧϳ ϱήΑ ϲδϳήΗΎϣ έϮσ ϪΑ έ scatterplot έΩϮϤϧ ΪΘΑΪϳήϴ̴Α Grafh - scatterplot - matrix ϢϴϨ̯ ϲϣ ΏΎδΣ έ ήϴϐΘϣ Ϫγ ϦϴΑ ϲ̴ΘδΒϤϫ ΐϳήο Analyze - correlate- bivariate … ή˰ϴϐΘϣ ϭΩ Ϧϴ˰Α Ϫ˰˰τΑέ ΩΪ˰˰Πϣ ϭ Ϫ˰Θϓή̳ Ζ˰ΑΎΛ έ income ή˰ϴϐΘϣ ϲ˰ϳΰΟ ϲ̴Θδ˰˰ΒϤϫ ΐϳή˰ο ί ϩΩΎϔΘ˰γ Ύ˰Α Ϣϳέϭ ϲϣ ΖγΪΑ έ age,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
0.335 income ϭ age ̶̴ΘδΒϤϫ ΐҨήο 0.376 ....... car ϭ age ̶̴ΘδΒϤϫ ΐҨήο 0.792 « income ϭ car ̶̴ΘδΒϤϫ ΐҨήο income ϝήΘϨ̯
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 ....... car ϭ age ̶̴ΘδΒϤϫ ΐҨήο ή˰ϴϐΘϣ ϝή˰ΘϨ̯ ί β˰̡ ϲ˰ϟϭ ΩϮ˰Α 0.376 ˬ age,car ή˰ϴϐΘϣ ϭΩ Ϧϴ˰Α ϲ̴Θδ˰ΒϤϫ ΐϳήο ϝϭ ΖϟΎΣ έΩ ϪΠϴΘϧ ΪϫΩ ϲϣ ϥΎθϧ έ κϟΎΧ ρΎΒΗέ ΩΪϋ Ϧϳ Ϫ̯ Ωή̯ Ϊϴ̡ ήϴϴϐΗ 0.193 ϪΑ ˬ income 68
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e.
lin
ab
et
.K
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w
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m
ΩέΩ ΩϮΟϭ ϩέ Ϫγ ήϴϐΘϣ ϭΩ ρΎΒΗέ ζΠϨγ ϱήΑ scatterplot έΩϮϤϧ Ϣγέ ˺ Corrolation ϲ̴ΘδΒϤϫ ΐϳήο ϱήϴ̳ ϩίΪϧ ˻ Ζθ̳ήΑ ςΧ ϥϮϴγή̳έ ϪΒγΎΤϣ ˼ Ωϭέ ϲϣ έΎ̰Α ήϴϐΘϣ ϭΩ ρΎΒΗέ ϝΪϣ Ϫέ ϭ ζΠϨγ ϱήΑ Ϫ̯ Ζγ ϱέΎϣ εϭέ ϦϳήΗΩήΑέΎ̯ή̡ ϥϮϴγή̳έ Ζγ έΩ έ ϩΪη ϩΪϫΎθϣ ϱΎϫϩΩΩ ΎΑ ϲ̴ϨϫΎϤϫ ϦϳήΘϜϳΩΰϧ Ϫϛ ϲτΧ ϪϟΩΎόϣ ΐϳήο ˬϲτΧ ϥϮϴγή̳έ ΪϨϛϲϣ ΩέϭήΑ ΩέΩ ΩϮΟϭ ΰϴϧ ϱΪόΑ ήϳΩΎϘϣ ϲϳϮ̴θϴ̡ ϥΎϜϣ Ζθ̳ήΑ ςΧ ϪϟΩΎόϣ ί ϩΩΎϔΘγ ΎΑ ϢϳέΩ Dependent ϪΘδΑϭ ήϴϐΘϣ Ϛϳ ϭ Independent ϞϘΘδϣ ήϴϐΘϣ Ϛϳ ϥϮϴγή̳έ ϪϟΩΎόϣ έΩ Ζγ ϲϜϳ ί ζϴΑ ϞϘΘδϣ ϱΎϫήϴϐΘϣ ΩΪόΗ ϩήϴϐΘϣ ΪϨ̩ ϥϮϴγή̳έ ϪϟΩΎόϣ έΩ ϝΎΜϣ έϮθϛ Ϛϳ έϮϧΎΧ ΪϣέΩ αΎγήΑ βϛϮϟ ϱϻΎϛ Ϛϳ εϭήϓ ϥΰϴϣ ΩέϭήΑ ΕϼϴμΤΗ ϥΰϴϣ ˬϪΑήΠΗ ˬϦγ ˬαΎγήΑ ϩΎ̴ηϭήϓ Ϛϳ ϥΎ̳ΪϨηϭήϓ εϭήϓ ϥΰϴϣ ΩέϭήΑ ιϮμΨϣ ϱΎϬηϭέ ί ϩΩΎϔΘγ ϭ έάΑ ωϮϧ ˬΩϮϛ ωϮϧ ˬϲ̳ΪϧέΎΑ ϥΰϴϣ αΎγήΑ ϝϮμΤϣ ϥΰϴϣ ΩέϭήΑ ΕΎϓ ϊϓέ ήϴΗ ωΎϔΗέ αΎγήΑ ϕήΑ ύή̩ ήϴΗ ςγϮΗ Ύπϓ Ϛϳ ϥΪη Ϧηϭέ ΖΣΎδϣ ΩέϭήΑ ϩΩΎγ ϲτΧ ϝΪϣ Y / x a bX 2 (Y Yˆ ) 2 ΖγΎϣ ϝήΘϨ̯ ΖΤΗ ϭ ϞϘΘδϣ ήϴϐΘϣ x ϥ έΩ Ϫϛ ΩϮηϲϣ ϩΪϴϣΎϧ constant Ύϳ ΪΒϣ ί νήϋ έΪϘϣ a ΩϮηϲϣ ϩΪϴϣΎϧ ϞϘΘδϣ ήϴϐΘϣ ΐϳήο Ύϳ ςΧ ΐϴη έΪϘϣ b Ζγ ϥϮϴγή̳έ ϝΪϣ ϭ ϩΪϫΎθϣ ϱΎτΧ e Ζγ 2 βϧΎϳέϭ ϭ a+bx Ϧϴ̴ϧΎϴϣ ϱέΩ ϭ Ζγ x ϪΑ ϪΘδΑϭ ϲϓΩΎμΗ ήϴϐΘϣ Y|x Ζγ 2 βϧΎϳέϭ ϭ ήϔλ Ϧϴ̴ϧΎϴϣ ϱέΩ ϭ Ζγ ϲϓΩΎμΗ ϱΎτΧ ϲϓΩΎμΗ ήϴϐΘϣ a, b, 2 ί ΪϨΗέΎΒϋ ϝΪϣ Ϧϳ ϱΎϫήΘϣέΎ̡ Ζγ ϩΪη ΩέϭήΑ έΪϘϣ Yˆ ϭ ϲόϗϭ έΪϘϣ Y ΕΎοϭήϔϣ ΎϫϩΪϧΎϣ ϲϗΎΑ ϊϳί ϮΗ Ύϳ Ζγ ϝΎϣήϧ ϊϳίϮΗ Ϛϳ ϪΘδΑϭ ήϴϐΘϣ ϊϳίϮΗ ˬϞϘΘδϣ ήϴϐΘϣ έΪϘϣ ήϫ ϱήΑ ΪηΎΑ ή̴ϳΩ ΕΪϫΎθϣ ί ϞϘΘδϣ ϭ ϝΎϣήϧ ΪϳΎΑ ΪηΎΑ ΖΑΎΛ ϪΘδΑϭ ήϴϐΘϣ βϧΎϳέϭ ΪϳΎΑ ϞϘΘδϣ ήϴϐΘϣ ήϳΩΎϘϣ ϪϤϫ ϱήΑ ΪϨηΎΑ ϲϓΩΎμΗ ΪϳΎΑ ΕΪϫΎθϣ ϪϤϫ ΪηΎΑ ϲτΧ ΪϳΎΑ ϞϘΘδϣ ϭ ϪΘδΑϭ ήϴϐΘϣ ϭΩ ρΎΒΗέ
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̮Ϥϧ ϑήμϣ ϭ ϞϘΘδϣ ήϴϐΘϣ ϥϮΧ έΎθϓ Ϫ̯ Ϊϳήϴ̴Α ήψϧ έΩ έ ̮Ϥϧ ϑήμϣ ϭ ϥϮΧέΎθϓ ήϴϐΘϣ ϭΩ ˱ϼΜϣ Ζγ ϪΘδΑϭ ήϴϐΘϣ Ζγ ϩΩήϛ ϝΪϣ έ ΕΪϫΎθϣ ήΘϬΑ ϥϮϴγή̳έ ϪϟΩΎόϣ ΪηΎΑ ήΘϤϛ ΎτΧ ΕΎόΑήϣ ςγϮΘϣ ϩίΪϧ Ϫ̩ήϫ sy ΪϧϮηϲϣ ϪΒγΎΤϣ ήϳί ΕέϮλ ϪΑ ϥϮϴγή̳έ ΐϳήο ΩέϭήΑ ˆ ˆ
br
eˆ
sx
aˆ y b x
ΪϧϮηϲϣ ϪΒγΎΤϣ ήҨί ΕέϮλ ϪΑ Residual ΎτΧ έΪϘϣ ΩέϭήΑ yi yˆ y i aˆ bˆxi ΪϧϩΪϣ ΖγΪΑ εϭέ ϦϴϤϫ ϪΑ ΰϴϧ ΎϫΩέϭήΑ Ϫϛ
Ζγ ΎτΧ ΕΎόΑήϣ ωϮϤΠϣ ϥΩήϛ ϪϨϴϤϛ ϑΪϫ ΪϴϨϛ ϲσ έ ήϳί ήϴδϣ έϮΘγΩ Ϧϳ ϪΑ ϲγήΘγΩ ϱήΑ
m
Analyze - Regression - Linear...
w
w w
.K
et
ab
lin
e.
co
έ ήϴϐΘϣ ϦϳΪϨ̩ ϞϘΘδϣ ήϴϐΘϣ ϥϮϨϋ ϪΑ Ϫϛ ϲΗέϮλ έΩ ΪϨ̩ ϥϮϴγή̳έ ϪΑ ρϮΑήϣ ΕΎΒγΎΤϣ spss ΪϴϨϛ Ωέϭ Ωήϛ ΪϫϮΧ ϪΒγΎΤϣ έ ϩήϴϐΘϣ ΪϴϨϛ Ωέϭ έ ϪΘδΑϭ ήϴϐΘϣ dependent ΖϤδϗ έΩ ϱΎϫήϴϐΘϣ Ύϳ ήϴϐΘϣ independent ΖϤδϗ έΩ ΪϴϨϛ Ωέϭ έ ϞϘΘδϣ ί ϱέΪϘϣ ϭ ήϴϐΘϣ selection variable ΖϤδϗ έΩ Ωέϭ Ζγ ΎϤη ήψϧ ΩέϮϣ ϲγέήΑ ϱήΑ Ϫϛ ϥ ήϴϐΘϣ ϥϮϨϋ ϪΑ ϥϮϴγή̳έ έΩ ΪϳΎΒϧ ήϴϐΘϣ Ϧϳ ΪϴϨϛ ΪηΎΑ ϪΘϓέ έΎϛ ϪΑ ϞϘΘδϣ ϥϮϨϋ ϪΑ Ϫϛ ϱήϴϐΘϣ Case lable ΖϤδϗ έΩ ΪϴϨϛ κΨθϣ Ζγ ϪΘϓέ έΎϛ ϪΑ ΎϫΩέϮϣ ϲϣΎγ ϪΑ ϲϫΩ ϥίϭ ήϴϐΘϣ WLS Wieght ΖϤδϗ έΩ Ζϓέ ΪϫϮΧ έΎϛ ϪΑ βϧΎϳέϭ έΪϘϣ ϱίΎγ ϥΎδϜϳ ϱήΑ ϥίϭ Ϧϳ ΪϴϨϛ Ωέϭ έ ΎϫΩέϮϣ ΩέΩ ΩϮΟϭ ϲΟϭήΧ έΩ ϱή̴ϳΩ ϱΎϫϩέΎϣ ήϳΩΎϘϣ ζϳΎϤϧ ϥΎ Ϝϣ Statistic ϪϤϛΩ ΏΎΨΘϧ ΎΑ ΩέΩ ΩϮΟϭ ϥϮϴγή̳έ ϪϟΩΎόϣ ϲγέήΑ ϱΎϫέΩϮϤϧ ζϳΎϤϧ ϥΎϜϣPlot ϪϤϛΩ ΏΎΨΘϧ ΎΑ ΩέΪϧΎΘγ ϩΪϧΎϣ ϲϗΎΑ ϭ ϪΘδΑϭ ήϴϐΘϣ ί ϩΪη ϩΩέϭήΑ ήϳΩΎϘϣ ϱίΎγ ϩήϴΧΫ ϥΎϜϣ Save ϪϤϛΩ ΎΑ ΩέΩ ΩϮΟϭ ϲόϗϭ
70
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H 0 : ΩέΪϧ X ϪΑ ΖΒδϧ Y ΕήϴϴϐΗέΩ ϱήϴΛΎΗϭ Ζδϴϧ ΐγΎϨϣ ϝΪϣ H 1 : Ζγ ΐγΎϨϣ ϥϮϴγή̳έ ϝΪϣ ςϘϓ ή̴ҨΩ έΎΒ̰Ҩ έ ϝΪϣ ΪҨέϭ ΖγΪΑ car ϭ income ̵Ύϫ ήϴϐΘϣ ̵ήΑ έ ϥϮϴγή̳έ ̶τΧ ϪϟΩΎόϣ ϝΎΜϣ ΪϴϨ̯ ΏΎδΣ Ύϫ ΩήΠϣ ̵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 ΪϨ̯ ̶ϣ ΏΎδΣ έ ϥϮϴγή̳έ ̵ΎτΧ έΪϘϣ βϧΎҨέϭ ΰϴϟΎϧ ϝϭΪΟ H0 : a 0 H0 : b 0 Ζγ ϩΩΩ ϡΎΠϧ έ ϥϮϣί ϦҨ ΐήο ί ̮Ҩ ήϫ ̵ήΑ H1 : a 0 H1 : b 0 ΪϨ̯ ̶ϣ Ωέ έ ήϔλ νήϓ ϪϧϮϤϧ β̡ p value 0.000 ϥϮ̩ ̶ϣ έή̰Η έ ϥϮϴγή̳έ ςΧ ϪΒγΎΤϣ ϞΣήϣ ΪόΑ ϢϴϨ̯ ̶ϣ ΪΟ έ martial ήϴϐΘϣ split file ΎΑ ϝϭ Ύϫ ΩήΠϣ ̵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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έ ϦϴηΎϣ ϪϨҨΰϫ έΪϘϣ x=73 ΪϣέΩ έΪϘϣ ̵ήΑ ϩΪϣ ΖγΪΑ ϝΪϣ ϪΑ ϪΟϮΗ ΎΑ ϢϴϫϮΧ ̶ϣ ΪϴϨ̯ νήϓ ΪηΎΑ ̶Ϥϧ Ύϣ ̵Ύϫ ϩΩΩ ˯ΰΟ ϩΩΩ ϦҨ Ϫ̯ ̶ϟΎΣ έΩ ϢϴϨ̯ ΏΎδΣ ϢϴϨ̯ ̶ϣ Ωέϭ income ϥϮΘγ έΩ ϒҨΩέ ϦҨήΧ Dataview ϪΤϔλ έΩ έ ̀˼ ΩΪϋ ˺ ˻ Analyze - Regression - Linear...... Dependent……………car
Independent………..income Save ……predicted value…..standardized
ab
lin
e.
co
m
y=0.3208 ϒҨΩέ ϦҨήΧ έΩ ZPR ϡΎϧ ϪΑ ΪҨΪΟ ϥϮΘγ έΩ ϩΪη ̶ϨϴΑ ζϴ̡ έΪϘϣ ϩΪϫΎθϣ ˼ Ύϫ ϩΪϧΎϣ ̶ϗΎΑ ϩΪϫΎθϣ ̵ήΑ ̊ Save ………Residuals ..…..standardized ΩϮη ̶ϣ ϩΩΩ ϥΎθϧ Dataview ϪΤϔλ έΩ Ύϫ ϩΪϧΎϤϴϗΎΑ ί ̶ϧϮΘγ ΖΑΎΛ έΪϘϣ ϥϭΪΑ ϥϮϴγή̳έ ςΧ ΐҨήο ϪΒγΎΤϣ 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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ab
lin
e.
co
m
ΕήϴϴϐΗ ΚϋΎΑ ϥ ̮̩Ϯ̯ ήϴϴϐΗ ̶ϨόҨ ΩέΩ ̵ήΘθϴΑ ΖϴϤϫ ήϴϐΘϣ ϡΪ̯ ϩήϴϐΘϣ ΪϨ̩ ̶τΧ ϝΪϣ έΩ ϝϮΌγ ˮΩϮη ̶ϣ ϪϟΩΎόϣ ̱έΰΑ Y a b1 X 1 b2 X 2 ... bk X k νϮϋ ΰϴϧ ΐҨήο ϢϴϨ̯ νϮϋέΎϫ ήϴϐΘϣ ΪΣϭ ή̳ ϥϮ̩ Ζδϴϧ ΎϬϧ ΖϴϤϫ ϪϧΎθϧ ΐҨήο ̶̰̩Ϯ̯ ΎҨ ̶̳έΰΑ ΩϮη ̶ϣ ΖΤΗ Standardized Coefficients ϥϮΘγ ,Coefficients ϝϭΪΟ έΩ ϥ ΐҨήο ΪηΎΑ ΪΣϭ ϥϭΪΑ ή̳ ήϴϐΘϣ ΪҨ ̶Ϥϧ ϝΪϣ έΩ Ϫ̯ Ζγ ΕήϴϴϐΗ ΖϴϤϫ ΐҨήο ϦҨ ΩϮη ̶ϣ ΝέΩ Beta ϥϮϨϋ ή̳ Ϫ̯ Ζγ ϩΪη ΝέΩ ϝϭΪΟ έΩ Ϣϫ Beta ̮Ҩ ΩέΩ ΩϮΟϭ ϞϘΘδϣ ήϴϐΘϣ ̮Ҩ ςϘϓ Ϫ̯ ϻΎΑ ϝΎΜϣ έΩ ˱ϼΜϣ Ϊη ̶ϣ ϢϴδϘΗ ϒϠΘΨϣ ̵Ύϫ Beta ϪΑ ΪηΎΑ ήϴϐΘϣ ΪϨ̩ ϢϴϧΪΑ ϢϴϫϮΨϴϣ Ϊϧ ϪΘϓή̳ ϩίΪϧ έ Ϊϧ ϩΩή̯ ϑΩΎμΗ Ϫ̯ ̶ϧΎ̳ΪϨϧέ ϥϮΧ Ϟ̰ϟ ϥΰϴϣ ϝΎΜϣ ΪϴϫΩ ϥΎθϧ έΩϮϤϧ ΎΑ ˮϪϧ ΎҨ Ζδϫ ΕΎϓΩΎμΗ ΩΪόΗ ϭ Ϟ̰ϟ ϥΰϴϣ ϦϴΑ ̶σΎΒΗέ ΎҨ ˺ ΪϴϨ̯ ΪϴҨΎΗ έ ΕΎϴοήϓ ϭ ΪϴϨ̯ ΏΎδΣ έ ήϴϐΘϣ ϭΩ ϦϴΑ ̶τΧ ̶̴ΘδΒϤϫ ΐҨήο ˻ ΩΪόΗ ϥϮΧ Ϟ̰ϟ ϒϠΘΨϣ ΡϮτγ ̵ήΑ ϭ ΪϴϨ̯ ΩέϭήΑ έ ήϴϐΘϣ ϭΩ ϦϴΑ ̶τΧ ϪτΑέ spss ί ϩΩΎϔΘγ ΎΑ ˼ ΪϴϨ̯ ΩέϭήΑ έ ΕΎϓΩΎμΗ ˮΪϨϟΎϣήϧ ϊҨίϮΗ ̵έΩ Ύϫ ϩΪϧΎϤϴϗΎΑ ΎҨ ̊ ˮΪϨΘδϫ ̶ϓΩΎμΗ ΕΪϫΎθϣΎҨ ̋ ϥϮΧ Ϟ̰ϟ ̋ ˺˹ ˺̋ ˻˹ ˻̋ ˼˹ ˼̋ ΕΎϓΩΎμΗ ΩΪόΗ ˺˹ ˺̀ ˻̌ ˼˹ ˼˻ ˼́ ̊˻ ˺ΏϮΟ ΩέΩ ΩϮΟϭ ̶τΧ ϪτΑέ Ϊγέ ̶ϣ ήψϧ ϪΑ ϪΠϴΘϧ Graph – Legacy dialogs – scatter plot– simple
.K
et
50 00
w w
accidentno
40 00
30 00
Ζγ ΕΎϓΩΎμΗ ΩΪόΗ ϪΘδΑϭ ήϴϐΘϣ Y ϭ Ϟ̰ϟ ϞϘΘδϣ ήϴϐΘϣ X ˻ ΏϮΟ
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 ΩϮη ̶ϣ Ωέ ϪϧϮϤϧ ςγϮΗ ήϴϐΘϣ ϭΩ ϝϼϘΘγ ΖγϻΎΑ ̶̴ΘδΒϤϫ ϪϧΎθϧ Ϫ̯ 0.984
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Ŷŝ įŹ įŚƣō ŵŚŤſř SPSS1
Ʊřźƿř żƿř ƺŤǀŤƀƳř ˼ ΏϮΟ
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 ϩΩΩ ϡΎΠϧ έ ϥϮϣί ϦҨ ΐήο ί ̮Ҩ ήϫ ̵ήΑ βϧΎҨέϭ ΰϴϟΎϧ ϝϭΪΟ έΩ ΪϨΘδϴϧ ήϔλ ΐҨήο ̶ϨόҨ ΪϨ̯ ̶ϣ Ωέ έ ήϔλ νήϓ ϪϧϮϤϧ β̡ 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)
Ύϫ ϩΪϧΎϤϴϗΎΑ ϥϮΘγ ΩΎΠҨ ̵ήΑ ̊ ΏϮΟ
Analyze - Regression - Linear...... Dependent……………accidentnor
Independent………..alckol Save …… Residuals …..standardized
Runs Test 2 ̋ΏϮΟ Runs ϥϮϣί 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
̶ϓΩΎμΗ Ύϫ ϩΪϧΎϤϴϗΎΑ ϭ ΩϮη ̶Ϥϧ Ωέ ϪϧϮϤϧ ςγϮΗ ήϔλ νήϓ β̡ Ϊϧ N (0,1) Standard Residual Unstandard Residual N (0, 2 ) a Mean
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