# Sample Data Statistical Analysis Term Paper

Statistical Analysis of the Sample Data

Identification of Discreet Variables and Continuous Variables

A discrete variable is obtained by counting, and the continuous variable is obtained by measuring. The study selects the following discreet variables for the analysis:

• Number of cars

• Number of Children

• Mail Buyer

The study also selects the following continuous variables for the analysis:

• Length of residence

• Athletic Dimension

• Wealth Rating.

Numerical Distribution of Discreet and Continuous Variable

The study uses the frequency distribution to compare the selected discreet and continuous variables using the central tendency tools and graph. As being revealed in the frequency table below, THE variables Lengthofresidence and Athleticdimension do not have the mean values showing the limitation of the data.

Frequencies

Comparison of Discreet and Continuous Variables

MailBuyer

LengthOfResidence

AthleticDimension

WealthRating

N

Valid

2000

2000

2000

2000

Missing

0

0

0

0

Mean

1,03

0,59

1,25

7,39

Median

1

0

1

8

Mode

1

0

2

9

The bar graph of all the six variables are presented below

Bar Graph

T-Test

The section presents the T-Test for the discreet and continuous variables. The p-value of the sample four populations is 0 revealing that the difference between the four means of the sample population is not statistically significant. While the T-test presents the results of four variables, the results of variables Length of residence and Athletic Dimension are missing because of the missing values. Thus, the missing values have affected the reliability and validity of tests of differences and statistical estimations.

One-Sample Statistics

N

Mean

Std. Deviation

Std. Error Mean

1,03

1,056

,035

2000

,59

1,020

,023

MailBuyer

2000

1,25

,808

,018

WealthRating

7,39

1,703

,076

One-Sample Test

Test Value = 0

t df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

Lower

Upper

29,742

0

1,031

0,96

1,1

25,684

1999

0

0,586

0,54

0,63

MailBuyer

69,395

1999

0

1,254

1,22

1,29

WealthRating

97,762

0

7,393

7,24

7,54

Chi Square

The study also presents the chi-square of all the six variables. The results of the chi-square show that there is a statistically significant association between Numberofcars and Lengthofresidence because their chi-square is 115,441. As being revealed in the table below, the chi-squares of all the variable relationships are statistically significant. However, lots of missing values in some of the variables affect the validity and reliability of the results.

Variables Relationships

Pearson chi square

NumberOfCars and Lengthofresidence

115,441.

NumberOfCars and Athleticdimension

2.704

NumberOfCars * Wealthrating

27.960

NumberOfChildren * Lengthofresidence

NumberOfChildren * Athleticdimension

30.089

NumberOfChildren * Wealthrating

27.683

MailBuyer * Lengthofresidence

MailBuyer * Athleticdimension

24.293

MailBuyer * Wealthrating

19.716

Case Processing Summary

Cases

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

NumberOfCars * Lengthofresidence

46,4%

53,7%

2000

100,0%

NumberOfCars * Athleticdimension

46,4%

53,7%

2000

100,0%

NumberOfCars * Wealthrating

11,0%

89,0%

2000

100,0%

NumberOfChildren * Lengthofresidence

2000

100,0%

0

0,0%

2000

100,0%

NumberOfChildren * Athleticdimension

2000

100,0%

0

0,0%

2000

100,0%

NumberOfChildren * Wealthrating

25,4%

74,7%

2000

100,0%

MailBuyer * Lengthofresidence

2000

100,0%

0…