234 mayur kriplani

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Statistics

Case Study

Evaluation

By- Mayur Kriplani (234)

HP


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Frequency distribution:-

c)Here Key variable is type of gender:-

Frequency Distribution:-

0

10

20

30

40

50

60

70

80

Promotional Regular

Frequency Percent

Frequency Percent

Variable Frequency Percent

Female 93

Male 7

0

20

40

60

80

100

Female Male

Frequency Percent

Frequency Percent


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d)Here key variable is marital status:-

Frequency Distribution:-

2) A bar or pie chart showing the number of customer purchases

attributable to the method of payment.

Pie Chart:-


Married 84

Single 16

0

50

100

Married Single

Frequency Percent

Frequency

Percent


American Express 2Discover 5

Master Card 13

Proprietary Card 70

Visa 10

American

Express

2%Discover5%

Master Card

13%

Proprietary

Card

70%

Visa

10%

Frequency Percent


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Case 2

1. Tabular and graphical summaries for each of the four variables alongwith a discussion of what each summary tells us about the motionpicture industry.

The data is quantitative data and hence we apply descriptive statistics which comprises of the

following:

1. Frequency Distribution

2. Relative Frequency and Percent Frequency Distributions

3. Histogram

4. Cumulative Distributions

5. Ogive

(i) Opening Gross Sales :

1. Frequency Distribution :Number of classes : We Select the number of classes (n=10)

Width of Class = (Largest Data Value Smallest Data Value) / n.

(108.44-0.01)/10 = 10.84

Assuming W=10.

2. Relative Frequency and Percent Frequency Distributions

Class Limits Frequency Relative Frequency Percent Frequency

Class

Mid-

Point

0 10 70 0.70 70 510 20 15 0.15 15 15

20 30 8 0.08 8 25

30 40 2 0.02 2 35

40 50 1 0.01 1 45

50 60 1 0.01 1 55

60 70 0 0.00 0 65

70 80 1 0.01 1 75

80 90 0 0.00 0 85

90 - 100 0 0.00 0 95100 - 110 2 0.02 2 105


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3. A scatter diagram to explore the relationship between Total GrossSales and Number of Theatres

The total gross sales dont only depend on number of theatres but there should be

substantial no of theatres showing the movie for total gross sales to be good.

4. A scatter diagram to explore the relationship between Total GrossSales and Number of Weeks in the Top 60

No of weeks in top 60 affect the sales but anything above 10-15 weeks can be a good number for

total sales to be good.

(50.00)

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

0 1,000 2,000 3,000 4,000 5,000

Total Gross

Total Gross

Linear (Total Gross)

l

l

i

0.00

50.00

100.00150.00

200.00

250.00

300.00

350.00

400.00

0 5 10 15 20 25 30

Total Gross

Total Gross

Number of Weeks

TotalGrossSales(in$

Millions)


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Case 3

Pelican Stores II (Pelican stores- CD file)

1. Descriptive Statistics on Net salesand descriptive statistics on net sales by variousclassifications of customers

Descriptive Statistics on Net sales

Sl No Head Value

1 Mean 77.6

2 Median 59.71

3 Mode 31.64 Range 274.36

5 First Quartile 39.6

6 Third Quartile 101.4

7 Inter Quartile Range 61.8

8 Variance 3667.6

9 Standard Deviation 55.39

10 Co-efficient of Variation 71.38

Majority of the sales have come from the high spending customers as it is evident from the

difference in the values of Mean and Median and also from the coefficient of variation.

There is a stark difference between the low spending customers and the high spending

customers. It can be seen from the difference in the Range and the Interquartile Range.

Descriptive statistics on net sales by various classifications of customers

A. Type of customers Promotional or RegularSl

No

Head Promotional Combined Regular

1 Mean 84.29 77.6 61.99

2 Median 63.42 59.71 51

3 Mode 31.6 31.6 44.54 Range 274.36 274.36 137.25

5 First Quartile 44.8 39.6 39.5

6 Third Quartile 108.8 101.4 74

7 Inter Quartile Range 64 61.8 34.5

8 Variance 3777.61 3667.6 1229.76

9 Standard Deviation 61.46 55.39 35.07

10 Co-efficient of Variation 72.9 71.38 56.57

The number of sales has increased due to the promotional offers given to the promotional

customers. The average spending by the promotional customers is higher than the overallaverage spending.


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B. Comparison by Marital Status of CustomersSl

No

Head Married Combined Unmarried

1 Mean 78.03 77.6 75.35

2 Median 59.00 59.71 64.46

3 Mode 39.5 31.6 31.60

4 Range 274.36 274.36 163.3

5 First Quartile 39.6 39.6 40.55

6 Third Quartile 101.40 101.4 98.25

7 Inter Quartile Range 61.8 61.8 58.30

8 Variance 3325.3 3667.6 2040.68



The overall statistics for the customers based on their marital status is similar. It is a very

uniformly distributed data based on the customers marital status. Since, the number ofunmarried customers were less, the overall statistics dont differ much even though there is

a difference in the Standard deviation in the two types of customers.

C. Comparison by Gender of CustomerSl

No

Head Female Combined Male

1 Mean 79.19 77.6 56.49

2 Median 62.40 59.71 47.20

3 Mode 31.60 31.6 39.50

4 Range 274.36 274.36 89.305 First Quartile 39.60 39.6 39.50

6 Third Quartile 102.40 101.4 49.50

7 Inter Quartile Range 52.80 61.8 10.00

8 Variance 3237.59 3667.6 924.30



Since, the Pelican Stores is a womens apparel stores, majority of the purchases have been

done by the females. Most of the purchases done by the males are similar. It can be said

from the low Interquartile Range and the low value of standard deviation as compared to

those of the females.

2. Descriptive statistics concerning the relationship between age and net salesSl No Head Value

1 Mean of Age 42.78

2 Mean of Net Sales 77.38

3 Standard Deviation of Age 12.39

4 Standard Deviation of Net Sales 55.66

5 Covariance -7.33

6 Corelation Co-efficient -0.01


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Case 5

Hamilton County Judges (Reference file: Judge CD File)

1. The probability of cases being appealed and reversed in the three differentcourts

Common Court:-

Total no of Cases Disposed: 43945Total no of Cases Appealed: 1762

Total no of Cases Reversed: 199

I) Probability of Cases being Appealed = 0.04II) Probability of Cases being Reversed = 0.0045

Domestic Court:-

Total no of Cases Disposed:- 30499

Total no of Cases Appealed:- 106

Total no of Cases Reversed :- 17


Municipal Court:-Total no of Cases Disposed:- 108464

Total no of Cases Appealed:- 500

Total no of Cases Reversed :- 104



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2. The probability of a case being appealed for each judgeJudge Disposed Appealed Reversed Court Probability of Cases

appealed

Fred Cartolano 3037 137 12 Common 0.0451

Thomas Crush 3372 119 10 Common 0.0353Robert Kraft 3138 127 7 Common 0.0405

William Mathews 2264 91 18 Common 0.0402

William Morrissey 3032 121 22 Common 0.0399

Norbert Nadel 2959 131 20 Common 0.0443

Arthur Ney Jr. 3219 125 14 Common 0.0388

Richard Niehaus 3353 137 16 Common 0.0409

Thomas Nurre 3000 121 6 Common 0.0403

John O'Connor 2969 129 12 Common 0.0434

Robert Ruehlman 3205 145 18 Common 0.0452

J. Howard Sundermann Jr. 955 60 10 Common 0.0628Ann Marie Tracey 3141 127 13 Common 0.0404

Ralph Winkler 3089 88 6 Common 0.0285

Penelope Cunningham 2729 7 1 Domestic 0.0026

Patrick Dinkelacker 6001 19 4 Domestic 0.0032

Deborah Gaines 8799 48 9 Domestic 0.0055

Ronald Panioto 12970 32 3 Domestic 0.0025

Mike Allen 6149 43 4 Muni 0.0070

Nadine Allen 7812 34 6 Muni 0.0044

Timothy Black 7954 41 6 Muni 0.0052

David Davis 7736 43 5 Muni 0.0056Leslie Isaiah Gaines 5282 35 13 Muni 0.0066

Karla Grady 5253 6 0 Muni 0.0011

Deidra Hair 2532 5 0 Muni 0.0020

Dennis Helmick 7900 29 5 Muni 0.0037

Timothy Hogan 2308 13 2 Muni 0.0056

James Patrick Kenney 2798 6 1 Muni 0.0021

Joseph Luebbers 4698 25 8 Muni 0.0053

William Mallory 8277 38 9 Muni 0.0046

Melba Marsh 8219 34 7 Muni 0.0041

Beth Mattingly 2971 13 1 Muni 0.0044Albert Mestemaker 4975 28 9 Muni 0.0056

Mark Painter 2239 7 3 Muni 0.0031

Jack Rosen 7790 41 13 Muni 0.0053

Mark Schweikert 5403 33 6 Muni 0.0061

David Stockdale 5371 22 4 Muni 0.0041

John A. West 2797 4 2 Muni 0.0014

Patrick Dinkelacker 7259 63 12 Com + Dom 0.0087

Timothy Hogan 4262 73 9 Com + Muni 0.0171

The last column provides the probability of case appealed against each judge respectively.


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3. The probability of a case being reversed for each judgeJudge Disposed Appealed Reversed Court Probability of

Cases reversedFred Cartolano 3037 137 12 Common 0.0040

Thomas Crush 3372 119 10 Common 0.0030

Robert Kraft 3138 127 7 Common 0.0022William Mathews 2264 91 18 Common 0.0080

William Morrissey 3032 121 22 Common 0.0073

Norbert Nadel 2959 131 20 Common 0.0068

Arthur Ney Jr. 3219 125 14 Common 0.0043

Richard Niehaus 3353 137 16 Common 0.0048

Thomas Nurre 3000 121 6 Common 0.0020

John O'Connor 2969 129 12 Common 0.0040

Robert Ruehlman 3205 145 18 Common 0.0056

J. Howard Sundermann Jr. 955 60 10 Common 0.0105

Ann Marie Tracey 3141 127 13 Common 0.0041Ralph Winkler 3089 88 6 Common 0.0019

Penelope Cunningham 2729 7 1 Domestic 0.0004

Patrick Dinkelacker 6001 19 4 Domestic 0.0007

Deborah Gaines 8799 48 9 Domestic 0.0010

Ronald Panioto 12970 32 3 Domestic 0.0002

Mike Allen 6149 43 4 Muni 0.0007

Nadine Allen 7812 34 6 Muni 0.0008

Timothy Black 7954 41 6 Muni 0.0008

David Davis 7736 43 5 Muni 0.0006

Leslie Isaiah Gaines 5282 35 13 Muni 0.0025

Karla Grady 5253 6 0 Muni 0.0000Deidra Hair 2532 5 0 Muni 0.0000

Dennis Helmick 7900 29 5 Muni 0.0006

Timothy Hogan 2308 13 2 Muni 0.0009

James Patrick Kenney 2798 6 1 Muni 0.0004

Joseph Luebbers 4698 25 8 Muni 0.0017

William Mallory 8277 38 9 Muni 0.0011

Melba Marsh 8219 34 7 Muni 0.0009

Beth Mattingly 2971 13 1 Muni 0.0003

Albert Mestemaker 4975 28 9 Muni 0.0018

Mark Painter 2239 7 3 Muni 0.0013Jack Rosen 7790 41 13 Muni 0.0017

Mark Schweikert 5403 33 6 Muni 0.0011

David Stockdale 5371 22 4 Muni 0.0007

John A. West 2797 4 2 Muni 0.0007

Patrick Dinkelacker 7259 63 12 Com + Dom 0.0017

Timothy Hogan 4262 73 9 Com + muni 0.0021

The last column provides the probability of case Reversed against each judge respectively.


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234 mayur kriplani

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