Get Instant Help From 5000+ Experts For

Writing: Get your essay and assignment written from scratch by PhD expert

Rewriting: Paraphrase or rewrite your friend's essay with similar meaning at reduced cost

## 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 P-value 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 P-value 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 R2 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 P-value 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 R2 the better the model. Thus other independent variables which may help in developing a better model are

1. Maximum amount that the consumer spends with credit cards
2. 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 Karl-Pearson Correlation (r) Significance (p-value)* 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

p-value = 0.05 level of significance

The Karl-pearsons 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 p-value = 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 p-value = 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 p-value = 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 p-value < 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 p-value < 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 p-value < 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 p-value =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 p-value =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 p-value =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 p-value =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 P-value 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 p-value 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 P-value 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 p-value 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:

1. The depression scores of individuals with good health do not vary across geographical locations.
2. The depression scores of individuals with chronic health conditions vary across geographical locations.
Cite This Work

My Assignment Help. (2022). The Essay Explores The Relationship Between Credit Card Charges, Income, And Household Size. (70 Characters). Retrieved from https://myassignmenthelp.com/free-samples/buacc5932-corporate-accounting/average-standard-deviation-file-A8A48E.html.

"The Essay Explores The Relationship Between Credit Card Charges, Income, And Household Size. (70 Characters)." My Assignment Help, 2022, https://myassignmenthelp.com/free-samples/buacc5932-corporate-accounting/average-standard-deviation-file-A8A48E.html.

My Assignment Help (2022) The Essay Explores The Relationship Between Credit Card Charges, Income, And Household Size. (70 Characters) [Online]. Available from: https://myassignmenthelp.com/free-samples/buacc5932-corporate-accounting/average-standard-deviation-file-A8A48E.html
[Accessed 20 September 2024].

My Assignment Help. 'The Essay Explores The Relationship Between Credit Card Charges, Income, And Household Size. (70 Characters)' (My Assignment Help, 2022) <https://myassignmenthelp.com/free-samples/buacc5932-corporate-accounting/average-standard-deviation-file-A8A48E.html> accessed 20 September 2024.

My Assignment Help. The Essay Explores The Relationship Between Credit Card Charges, Income, And Household Size. (70 Characters) [Internet]. My Assignment Help. 2022 [cited 20 September 2024]. Available from: https://myassignmenthelp.com/free-samples/buacc5932-corporate-accounting/average-standard-deviation-file-A8A48E.html.

Get instant help from 5000+ experts for

Writing: Get your essay and assignment written from scratch by PhD expert

Rewriting: Paraphrase or rewrite your friend's essay with similar meaning at reduced cost