DIGITAL SKILLS & GRAPHIC REPRESENTATION USING SPSS AND MICROSOFT EXCEL

ELECTIVE IV - DIGITAL SKILLS & GRAPHIC REPRESENTATION (Course Code - 19MEL-6CR22S)

Faculty In-charge : Mr. Antim Dev Mishra

MID & END TERM SUBMISSION Submitted By : PARAS MONGIA Roll No. : 200MPLUP021 MASTERS IN URBAN PLANNING 2nd Year, 4th SEMESTER School Of Planning & Development (SPD)

CONTENT OF ASSIGNMENTS TASKS

TITLE

DATE

-

Introduction to Data Analytics and Visualization Types of Data, Data Statistics, Statistical Parameters

16.02.2022

SIGNATURE

1-2

To prepare a marksheet and then calculate:

1 2

3

Maxima, Minima, Average and median of the given data of students

Using Data Analysis tools to calculate: Mean, Median, Mode and Standard Deviation

3

02.03.2022

09.03.2022

4

16.03.2022

5-6

23.03.2022

7

To calculate the grade sheet of 10 students using MS Excel formulae or function (IF) with following conditions: = or > 80% - Grade A = or > 70% - Grade B = or > 60% - Grade C = or > 50% - Grade D

Analysing Data using :

PAGE NO.

4

Countif, Countifs, Sumif, Sumifs, Concatenate, Len

5

Calculate and Interpret Chi Square in SPSS

20.04.2022

8

6

Calculate and Interpret One Sample T Test in SPSS

27.04.2022

9

7

Calculate and Interpret Independent T Test in SPSS

11.05.2022

10

ASSIGNMENT 1 – MID TERM

ASSIGNMENT 2 – END TERM

ASSIGNMENT 1- BRIEF The Difference Between Data and Statistics

I N T R O D U C T I O N

While the terms ‘data’ and ‘statistics’ are often used interchangeably, in scholarly research there is an important distinction between them data are individual pieces of factual information recorded and used for the purpose of analysis.

It is the raw information from which statistics are created. Statistics are the results of data analysis - its interpretation and presentation.

In other words some computation has taken place that provides some understanding of what the data means.

Statistics are often, though they don’t have to be, presented in the form of a table, chart, or graph.

Both statistics and data are frequently used in scholarly research. Statistics are often reported by government agencies - for example, unemployment statistics or educational literacy statistics. Often these types of statistics are referred to as 'statistical data'.

1

Mid Term Assignment

ASSIGNMENT 1- BRIEF I N T R O D U C T I O N

1. Mean The mean is also referred to as the average, and it is the most commonly used among the three measures of central tendency.

The mean is obtained by summing and dividing the values by the number of scores. For example, in five households that comprise 5, 2, 1, 3, and 2 children, the mean can be calculated as follows: = (5+2+1+3+2)/5 = 13/5 = 2.6

2. Median The median is used to calculate variables that are measured with ordinal, interval, or ratio scales. It is obtained by arranging the data from the lowest to the highest and then picking the number(s) in the middle. If the total number of data points is an odd number, the median is usually the middle number. If the numbers are even, the median is obtained by summing the two numbers in the middle and dividing them by two to get the mean.

Median is mostly used when there are a few data points that are different. 17, 17, 18, 19, 19, 20, 21, 25, 28, 32 The median of the values above is (19+20)/2 = 19.5.

Mode The mode is the most occurring number within a data distribution. It shows what number or value is the highest in number or most common in the data distribution. The mode is used for any type of data.

For example, let’s take the example of a college class with about 40 students. The students are given a test exam, graded, and then grouped on a scale of 1-5, starting with students with the lowest number of marks.

The marks are graded as follows: •Cluster 1: 5 •Cluster 2: 7 •Cluster 3: 13 •Cluster 4: 12 •Cluster 5: 3 Cluster 3 shows the highest number of students and, therefore, the mode is 13. It reveals that out of 40 students, most of the students were graded in cluster 3.

2

Mid Term Assignment

TASK 1 S. NO. Name of Student

A S S I G N M E N T 1

Subject Wise Marks English

Maths Science S.ST. Hindi

Score/ Grand Total

Percentage (%)

Computer Science

Out of 600

Out of 100

1

Paras

94

85

96

92

98

95

560

93%

2

Aisha

74

70

56

63

78

80

421

70%

3

Ambika

60

56

74

66

85

82

423

71%

4

Manav

90

87

88

83

94

89

531

89%

5

Ellora

69

40

33

54

66

70

332

55%

6

Kanishk

58

66

40

38

39

48

289

48%

7

Ram

44

54

59

45

75

65

342

57%

8

Juhi

85

95

75

85

81

78

499

83%

9

Sanya

79

77

72

84

85

77

474

79%

10

Amrit

87

59

60

66

92

91

455

76%

MEAN

74

68.9

65.3

67.6

79.3

77.5

432.6

MAX.

94

95

96

92

98

95

560

MIN.

44

40

33

38

39

48

289

MEDIAN

76.5

68

66

66

83

79

439

PASS

Greater than 50 Less than 50

3

Mid Term Assignment

TASK 2 Sr. NO. Designation Salary Pf (20% of salary)

A S S I G N M E N T

1

Urban Planner 100000

2 3 4

Architect Engineer

Column2

20000

200000 50000

40000 10000

Urban Designer 25000

5000

Mean Median Mode Maxima Minima

Column1 Mean

93750 Mean 18750 38696.1992 Standard Error Standard Error 7739.239842 1 Median 75000 Median 15000

93750 75000 #N/A

Mode Standard Deviation

#N/A 77392.3984 2

Sample Variance 5989583333 Kurtosis

250000 200000

Skewness

150000 100000

Range Minimum Maximum Sum Count

50000 0

1 Salary

Pf (20% of salary)

Expon. (Salary)

Expon. (Pf (20% of salary))

4

0.75765595 5 1.13762436 7 175000 25000 200000 375000 4 0

Mode Standard Deviation Sample Variance

#N/A 15478.47968

239583333.3

Kurtosis

0.757655955

Skewness

1.137624367

Range Minimum Maximum Sum Count

35000 5000 40000 75000 4

Mid Term Assignment

TASK 3 A S S I G N M E N T

Sr. No.

Name of Student

Hindi

Computer Science

1

Paras

94

85

96

92

98

95

560

93%

A

2

Aisha

74

70

56

63

78

80

421

70%

B

3

Ambika

60

56

74

66

85

82

423

71%

B

4

Manav

90

87

88

83

94

89

531

89%

A

5

Ellora

69

40

33

54

66

70

332

55%

FAIL

6

Kanishk

58

66

40

38

39

48

289

48%

FAIL

7

Ram

44

54

59

45

75

65

342

57%

FAIL

8

Juhi

85

95

75

85

81

78

499

83%

A

1

9

Sanya

79

77

72

84

85

77

474

79%

B

10

Amrit

87

59

60

66

92

91

455

76%

B

English Maths Science S.ST.

5

Total Percentage Grade

Mid Term Assignment

A S S I G N M E N T

Row Labels

Sum of sr. no.

Aisha B Ambika B Amrit B Ellora FAIL Juhi A Kanishk FAIL Manav A Paras A Ram FAIL Sanya B Grand Total

2 2 3 3 10 10 5 5 8 8 6 6 4 4 1 1 7 7 9 9 55

Sum of English 74 74 60 60 87 87 69 69 85 85 58 58 90 90 94 94 44 44 79 79 740

Sum of Maths 70 70 56 56 59 59 40 40 95 95 66 66 87 87 85 85 54 54 77 77 689

Sum of Science 56 56 74 74 60 60 33 33 75 75 40 40 88 88 96 96 59 59 72 72 653

Sum of S.ST. 63 63 66 66 66 66 54 54 85 85 38 38 83 83 92 92 45 45 84 84 676

Sum of Hindi 78 78 85 85 92 92 66 66 81 81 39 39 94 94 98 98 75 75 85 85 793

Sum of Computer Science 80 80 82 82 91 91 70 70 78 78 48 48 89 89 95 95 65 65 77 77 775

Sum of Total 421 421 423 423 455 455 332 332 499 499 289 289 531 531 560 560 342 342 474 474 4326

Sum of Percentage 0.701666667 0.701666667 0.705 0.705 0.758333333 0.758333333 0.553333333 0.553333333 0.831666667 0.831666667 0.481666667 0.481666667 0.885 0.885 0.933333333 0.933333333 0.57 0.57 0.79 0.79 7.21

300 200 100 0

1 English

Maths

6

Science

Mid Term Assignment

TASK 4 A S S I G N M E N T 1

Sr. No. First Name Paras 1 Sheena 2 Ellora 3 Parth 4 Lokesh 5 6 7 8 9 10

Last Name Mongia Sharma Ghosh Lekhi Meena

Combine Paras_Mongia Sheena_Sharma Ellora _Ghosh Parth_Lekhi Lokesh_Meena

Length 12 13 13 11 12

Product Quantity Cost Sofa 5 500 Chair 4 440 tv 2 200 ac 7 655 fridge 1 141

Anisha

Sharma

Anisha_Sharma

13

refrigirat or

3

485

NW

Shubhra Diksha Rahul Yashika

Sharma Tiwari Kasat Singhal

Shubhra_Sharma Diksha_Tiwari Rahul_Kasat Yashika_Singhal

14 13 11 15

fan cooler table table fan

2 8 9 1

185 159 946 154

NW NW W W

count if product is working

4

cont if product is TV and working

1

sum if value is > 300 sumifs

Status W NW W NW NW

3026 200

7

Mid Term Assignment

TASK 1 Chi Test - Calculate and Interpret Chi Square in SPSS

A S S I G N M E N T

RELEVANCE : A chi-square (χ2) statistic is a test that measures how a model compares to actual observed data. The data used in calculating a chi-square statistic must be random, raw, mutually exclusive, drawn from independent variables, and drawn from a large enough sample.

Chi-square tests are often used in hypothesis testing. The chi-square statistic compares the size of any discrepancies between the expected results and the actual results, given the size of the sample and the number of variables in the relationship.

QUESTION : In the sample dataset, respondents were asked their gender and whether they were a cigarette smoker. We have to check the association between the two using Chi-Square Test of Independence (using α = 0.05).

STEPS OF THE TEST : 1.

2.

3.

4.

5.

Click on Analyze -> Descriptive Statistics -> Crosstabs.

Drag and drop (at least) one variable into the Row(s) box (ex. Smoking), and (at least) one into the Column(s) box (ex. Gender).

Click on Statistics, and select Chi-square.

Press Continue, and then OK to do the chi square test.

The result will appear in the SPSS output viewer.

HYPOTHESIS : The approach is to test assumed/ observed values in the data to expected values to check the null value’s truthfulness. Null Hypothesis is accepted when the value of alpha is less than 0.05

ASSUMPTIONS

STEP 1

STEP 2

DATASET

Just like any other statistical test, the chi-square test comes with a few assumptions of its own: •

•

The χ2 assumes that the data for the study is obtained through random selection, i.e. they are randomly picked from the population The categories are mutually exclusive i.e. each subject fits in only one category. For e.g.- from our above example – the number of people who • lunched in your restaurant on Monday • can’t be filled in the Tuesday category •

The data should be in the form of frequencies or counts of a particular category and not in percentages The data should not consist of paired samples or groups or we can say the observations should be independent of each other When more than 20% of the expected frequencies have a value of less than 5 then Chi-square cannot be used. To tackle this problem: Either one should combine the categories only if it is relevant or obtain more data

OUTPUTS OF THE TEST

2

RESULT Since, the value of α is greater than 0.05. So, hypothesis is not rejected. There is a relationship between smoking & gender variable.

8

End Term Assignment

TASK 2

One Sample T Test A S S I G N M E N T 2

Calculate and Interpret

RELEVANCE : The one-sample t-test is a statistical hypothesis test used to determine whether an unknown data mean is different from a specific value.

There are three types of t-tests we can perform based on the data at hand: • One sample t-test • Independent two-sample t-test • Paired sample t-test

Hence, we can perform a onesample t-test. Here’s the formula to calculate this:

t = t-statistic m = mean of the group µ = theoretical value or population mean s = standard deviation of the group n = group size or sample size

QUESTION : There is a tire making company which claims that their tires can give an average of 35-45km. Make an Analysis to check will the tire at 40k will be workable or not.

STEPS OF THE TEST : 1. Click on Analyze -> Compare Means -> One-Sample T Test 2. Drag and drop the variable you want to test against the mean into the Test Variable(s) box.

3. Specify a mean in the Test Value box 4. Click OK 5. Results will appear in the SPSS output viewer

HYPOTHESIS : If H0 > 0.05, then the hypothesis is correct

DATASET

OUTPUTS OF THE TEST

Normality Test is not significantly related.

The value of α is > 0.05, hence the hypothesis is accepted and null is verified.

ASSUMPTIONS There are certain assumptions we need to heed before performing a ttest: 1. The data should follow a continuous or ordinal scale (the IQ test scores of students, for example) 2. The observations in the data should be randomly selected 3. The data should resemble a bell-shaped curve when we plot it, i.e., it should be normally distributed.

4. Large sample size should be taken for the data to approach a normal distribution (although t-test is essential for small samples as their distributions are non-normal)

RESULT

9

End Term Assignment

TASK 3 INDEPENDENT T Test

A S S I G N M E N T 2

-

Calculate and Interpret

RELEVANCE : The Independent Samples t Test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. The Independent Samples t Test is a parametric test.

The Independent Samples t Test is commonly used to test the following: • Statistical differences between the means of two groups • Statistical differences between the means of two interventions • Statistical differences between the means of two change scores

QUESTION : A construction company plans to construct property at Delhi and Mumbai, but the company needs to know that if the property rates are same in both the cities or different.

STEPS OF THE TEST : 1. Click on Analyze -> Compare Means -> Independent-Samples T Test 2. Drag and drop the dependent variable into the Test Variable(s) box, and the grouping variable into the Grouping Variable box 3. Click on Define Groups, and input the values that define each of the groups that make up the grouping variable (i.e., the coded value for Group 1 and the coded value for Group 2) 4. Press Continue, and then click on OK to run the test 5. The result will appear in the SPSS data viewer

ASSUMPTIONS 1.

2.

3.

The dependent variables should be measured on a continuous scale (either interval or ratio).

There should be two dependent variables present which are measured from independent (non-related) groups.

There are no outliers present in the variables.

HYPOTHESIS : If H0 > 0.05, Paying Capacity of Delhi and Mumbai people are same

DATASET (179 samples)

OUTPUTS OF THE TEST Independent T Test Total Samples = 179 Delhi – 1 (80 people) Mumbai – 2 (90 people)

RESULT

10

City Amount

Statistic

df

Shapiro-Wilk Sig.

Statistic

df

Sig.

Delhi

.219

80

.000

.864

80

.000

Mumbai

.153

99

.000

.824

99

.000

Group Statistics City Amount

N

Mean

Std. Deviation

Std. Error Mean

Delhi

80

4587500.00

842596.199

94205.119

Mumbai

99

4632727.27

1031639.094

103683.630

Independent Samples Test

The value of α is > 0.05, the hypothesis is verified.

4. The dependent variables should be normally distributed.

5. The dependent variables should have homogeneity of variances. In other words, their standard deviations need to be approximately the same. This can be investigated with the Levene’s Test for Equality of Variances.

Tests of Normality Kolmogorov-Smirnova

Levene's Test for

t-test for

Equality of

Equality of

Variances

Means

F Amo Equal unt

.234

Sig.

t

df

.629 -.316

177

-.323

176.

variances assumed Equal variances not

975

assumed

End Term Assignment