Descriptive Statistics for Income ($000s) and Amount Charged ($)
The analysis investigates the relation of credit card charges with Income and house hold size of 50 consumers. Table 1 presents the descriptive statistics of the Income ($000s) and amount charged on the credit card ($) of the client.
Table 1: Descriptive statistics for Income ($000s) and Amount Charged ($)
Descriptive Statistics 
Income ($1000s) 
Amount Charged ($) 
Mean 
43.48 
3963.86 
Median 
42 
4090 
Mode 
54 
3890 
Standard Deviation 
15 
934 
Kurtosis 
1.25 
0.74 
Skewness 
0.10 
0.13 
Range 
46 
3814 
Minimum 
21 
1864 
Maximum 
67 
5678 
Sum 
2174 
198193 
Count 
50 
50 
The average (standard deviation) income ($000s) of the consumers is $43.48 ($15). The analysis shows that the minimum and maximum income of the consumers is $21 and $67 respectively. Hence the range of income of the consumers is $46. The median income of the consumers is $42. Maximum number of consumers of the organzation has an income of $54 (000s).
The Average (Standard Deviation) of the amount charged by the client on credit cards is $3963.8 ($934). The analysis shows that the minimum and maximum amount charged for credit cards to the consumers is $1864 and $5678 respectively. Thus consumers are charged in the range of $3814 for credit card usage. $4090 is the median value charged on the credit card. Maximum number of consumers of the organization is charged $3890. (000s).
Table 2: Descriptive statistics for Household Size
House Hold Size 
Frequency 
1 
5 
2 
15 
3 
8 
4 
9 
5 
5 
6 
5 
7 
3 
Table 2 presents the house hold size of the consumers of the client. The analysis shows that most of the consumers (15) have a household size of 2. The maximum household size is 7, 3 consumers have the household size. The least household size is 1, there are 5 consumers which have the household size.
To investigate the relation between credit card charge by the client and income of the consumers a linear regression is carried out.
Table 3: Regression Statistics 

Multiple R 
0.6308 
R Square 
0.3979 
Adjusted R Square 
0.3853 
Standard Error 
731.9025 
Observations 
50 
Table 4: Regression Coefficients
Coefficients 
Standard Error 
t Stat 
Pvalue 

Intercept 
2204.241 
329.134 
6.697 
0.000 
Income ($1000s) 
40.470 
7.186 
5.632 
0.000 
Table 4 presents the regression coefficients for the relation between credit card charge and income of the consumers. The regression equation is represented as
Amount charged ($) = 2204.24 + 40.470*Income ($000s)
Table 3 presents the regression statistics for the relation. The analysis shows that 39.79% of the variability in the amount charged on the credit card can be explained by the independent variable Income.
To investigate the relation between amounts charged on credit card by the client and the household size of the consumer a linear regression is carried out.
Table 5: Regression Statistics 

Multiple R 
0.7529 
R Square 
0.5668 
Adjusted R Square 
0.5578 
Standard Error 
620.8163 
Observations 
50 
Table 6: Regression Coefficients
Coefficients 
Standard Error 
t Stat 
Pvalue 

Intercept 
2581.644 
195.270 
13.221 
0.000 
Household Size 
404.157 
51.000 
7.925 
0.000 
Table 6 presents the regression coefficients for the relation between credit card charge and household size of the consumers. The regression equation is represented as
Descriptive Statistics for Household Size
Amount charged ($) = 2581.644 + 40.157*Household size
Table 5 presents the regression statistics for the relation. The analysis shows that 56.68% of the variability in the amount charged on the credit card can be explained by the independent variable Household size.
The comparison of the R^{2} values shows that household size is a better predictor for amount charged on credit card.
To investigate the relation between income and household size of the consumers and amount charged on credit card by the client a multiple linear regression is carried done.
Table 7: Regression Statistics 

Multiple R 
0.9085 
R Square 
0.8254 
Adjusted R Square 
0.8179 
Standard Error 
398.3249 
Observations 
50 
Table 8: Regression Coefficients
Coefficients 
Standard Error 
t Stat 
Pvalue 

Intercept 
1305.034 
197.771 
6.599 
0.000 
Income ($1000s) 
33.122 
3.970 
8.343 
0.000 
Household Size 
356.340 
33.220 
10.727 
0.000 
Table 8 presents the regression coefficients for the relation between amount charged on credit card and income and household size of the consumers. The regression equation is represented as
Amount charged ($) = 1305.034 + 33.122*Income ($000s) + 356.340*Household size
Table 7 presents the regression statistics for the relation. The analysis shows that 82.54% of the variability in the amount charged on the credit card can be explained through the independent variables Income ($000s) and Household size.
The regression equation for amount charged on credit card
Amount charged ($) = 1305.034 + 33.122*Income ($000s) + 356.340*Household size
Thus the amount that would be charged on a consumer whose household size is 3 as well as has an income of $40,000 is
Amount charged ($) = 1305.03 + 33.122*40 + 356.340*3
= 3698.93 ≈ 3699
Independent variables in a regression equation are helpful for predicting the variability in the dependent variable. The higher the value of R^{2} the better the model. Thus other independent variables which may help in developing a better model are
 Maximum amount that the consumer spends with credit cards
 The frequency of use of credit card by the consumer.
The variable Student ID is an identity variable. Thus the histogram for student ID cannot be created.
Table 9: Descriptive Statistics
Mean 
Standard Deviation 
Minimum 
Maximum 

Year Enrolled 
2013.04 
0.81 
2012 
2014 
HI001 FINAL EXAM 
31.72 
6.75 
0 
45 
HI001 ASSIGNMENT 01 
17.21 
1.99 
8 
22 
HI001 ASSIGNMENT 02 
15.46 
2.31 
8 
21 
HI002 FINAL EXAM 
26.50 
5.91 
0 
40 
HI002 ASSIGNMENT 01 
17.82 
3.44 
4 
22 
HI002 ASSIGNMENT 02 
12.42 
1.99 
4 
16 
HI003 FINAL EXAM 
25.99 
8.27 
4 
43 
HI003 ASSIGNMENT 01 
18.19 
3.91 
10 
30 
HI003 ASSIGNMENT 02 
13.54 
1.76 
8 
20 
Activity 03
Table 10: Correlation between variables
Sl. No. 
Correlation 
KarlPearson Correlation (r) 
Significance (pvalue)* 
1 
HI001 Final Exam and HI002 Final Exam 
0.049 
0.630 
2 
HI001 Final Exam and HI003 Final Exam 
0.122 
0.232 
3 
HI002 Final Exam and HI003 Final Exam 
0.116 
0.257 
4 
HI001 Assignment 01 and HI001 Assignment 02 
0.659 
0.000 
5 
HI002 Assignment 01 and HI002 Assignment 02 
0.549 
0.000 
6 
HI003 Assignment 01 and HI003 Assignment 02 
0.520 
0.000 
7 
HI001 Final Exam and HI001 Assignment 01 
0.093 
0.364 
8 
HI002 Final Exam and HI002 Assignment 01 
0.177 
0.081 
9 
HI003 Final Exam and HI003 Assignment 01 
0.197 
0.052 
10 
HI003 Final Exam and HI003 Assignment 02 
0.120 
0.239 
pvalue = 0.05 level of significance
The Karlpearsons correlation between HI001 Final Exam and HI002 Final Exam is r = 0.049. Thus the correlation can be said to be positive, very weak and linear. In addition the correlation is not statistically significant pvalue = 0.630 > 0.05, level of significance.
The correlation between HI001 Final Exam and HI003 Final Exam is r = 0.122. Thus the correlation can be said to be positive, weak and linear. In addition the correlation is not statistically significant pvalue = 0.232 > 0.05, level of significance.
The correlation between HI002 Final Exam and HI003 Final Exam is r = 0.116. Thus the correlation can be said to be positive, weak and linear. In addition the correlation is not statistically significant pvalue = 0.257 > 0.05, level of significance.
Linear Regression for Income and Credit Card Charges
The correlation between HI001 Assignment 01 and HI001 Assignment 02 is r = 0.659. Thus the correlation can be said to be positive, moderate and linear. In addition the correlation is statistically significant pvalue < 0.001.
The correlation between HI002 Assignment 01 and HI002 Assignment 02 is r = 0.549. Thus the correlation can be said to be positive, moderate and linear. In addition the correlation is statistically significant pvalue < 0.001.
The correlation between HI003 Assignment 01 and HI003 Assignment 02 is r = 0.520. Thus the correlation can be said to be positive, moderate and linear. In addition the correlation is statistically significant pvalue < 0.001.
The correlation between HI001 Final Exam and HI001 Assignment 01 is r = 0.093. Thus the correlation can be said to be positive, very weak and linear. In addition the correlation is statistically not significant pvalue =0.364 > 0.05, level of significance.
The correlation between HI002 Final Exam and HI002 Assignment 01 is r = 0.177. Thus the correlation can be said to be positive, weak and linear. In addition the correlation is statistically not significant pvalue =0.081 > 0.05, level of significance.
The correlation between HI003 Final Exam and HI003 Assignment 01 is r = 0.197. Thus the correlation can be said to be positive, weak and linear. In addition the correlation is statistically not significant pvalue =0.052 > 0.05, level of significance.
The correlation between HI003 Final Exam and HI003 Assignment 02 is r = 0.120. Thus the correlation can be said to be positive, weak and linear. In addition the correlation is statistically not significant pvalue =0.239 > 0.05, level of significance.
Table 11: Depression Scores for individuals with reasonably good health
Depression Scores 
Florida 
New York 
North Carolina 
2 
2 
0 
0 
3 
3 
0 
4 
4 
1 
1 
1 
5 
3 
1 
1 
6 
3 
2 
1 
7 
4 
4 
3 
8 
3 
7 
5 
9 
1 
1 
1 
10 
0 
1 
1 
11 
0 
1 
2 
12 
0 
1 
1 
13 
0 
1 
0 
From the state of Florida individuals with reasonable good health had a depression score from 2 to 9. From the state of New York individuals with reasonable good health had a depression score from 4 to 13. From the state of North Carolina individuals with reasonable good health had a depression score from 3 to 12.
Table 12: Depression Scores for individuals with chronic health condition
Depression Scores 
Florida 
New York 
North Carolina 
8 
0 
0 
1 
9 
1 
2 
0 
10 
1 
0 
1 
11 
1 
2 
2 
12 
3 
1 
3 
13 
3 
1 
2 
14 
1 
4 
3 
15 
2 
2 
2 
16 
2 
1 
1 
17 
4 
2 
2 
18 
0 
1 
2 
19 
0 
1 
1 
20 
1 
1 
0 
21 
1 
0 
0 
22 
0 
0 
0 
23 
0 
1 
0 
24 
0 
1 
0 
Individuals with chronic health condition from Florida had a depression score from 9 to 21. Similarly individuals from New York had a depression score range of 9 to 24. Likewise individuals from North Carolina had a depression score in the range of 8 to 19.
Analysis of Variance is carried out to check for the relation between depression scores and geographical location
ANOVA 1  Individuals with reasonably good health
Null Hypothesis: The average depression scores of individuals from Florida, New York and North Carolina are equal
Alternate Hypothesis: The average depression scores of individuals from Florida, New York and North Carolina are not equal
Table 13: ANOVA for individuals with reasonably good health
Source of Variation 
SS 
df 
MS 
F 
Pvalue 
F crit 
Between Groups 
61.033 
2 
30.517 
5.241 
0.008 
3.159 
Within Groups 
331.900 
57 
5.823 

Total 
392.933 
59 
The analysis of variance for individuals with reasonably good health shows that F(2,57) = 5.241. The pvalue for the ANOVA = 0.008 < 0.05, level of significance. Thus, we reject the Null hypothesis.
Thus, it can be inferred that depressions scores of individuals with reasonably good health varies across geographical locations.
ANOVA 2  Individuals with chronic health condition
Null Hypothesis: The average depression scores of individuals from Florida, New York and North Carolina are equal
Alternate Hypothesis: The average depression scores of individuals from Florida, New York and North Carolina are not equal
Table 14: ANOVA for individuals with chronic health condition
Source of Variation 
SS 
df 
MS 
F 
Pvalue 
F crit 
Between Groups 
17.03 
2 
8.517 
0.714 
0.494 
3.159 
Within Groups 
679.70 
57 
11.925 

Total 
696.73 
59 
The analysis of variance for individuals with chronic health conditions shows that F(2,57) = 0.714. The pvalue for the ANOVA = 0.494 > 0.05, level of significance. Hence we do not reject the Null Hypothesis.
Thus, it can be inferred that depressions scores of individuals with chronic health conditions does not vary across geographical locations.
From the analysis of variance of depressions scores of individuals with good health and chronic health conditions it can be inferred:
 The depression scores of individuals with good health do not vary across geographical locations.
 The depression scores of individuals with chronic health conditions vary across geographical locations.
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