Comprehensive Statistical Analysis Report: Credit Card Data

This report presents a statistical analysis of credit card data, employing descriptive statistics and regression analysis to explore relationships between variables such as income, household size, and credit card charges. The analysis includes regression equations, predicted credit card charges, and model modifications. Task 2 focuses on exam and assignment scores, utilizing histograms, descriptive statistics, and correlation analysis to identify relationships between different assessments. Task 3 delves into depression levels across different cities, employing descriptive statistics and ANOVA to assess the impact of health status on depression. The report concludes with key findings and implications derived from the statistical analyses.

STATS

TABLE OF CONTENTS
INTRODUCTION.......................................................................................................................................................................................3
TASK 1........................................................................................................................................................................................................3
1 Descriptive statistics.............................................................................................................................................................................3
2 Regression analysis...............................................................................................................................................................................4
3 Equation of regression..........................................................................................................................................................................8
4 Predicted credit card charge when family size is three.........................................................................................................................8
5 Modification of model..........................................................................................................................................................................9
TASK 2......................................................................................................................................................................................................10
Activity 01.............................................................................................................................................................................................10
Activity 02.............................................................................................................................................................................................10
Activity 03.............................................................................................................................................................................................22
TASK 3......................................................................................................................................................................................................25
1 Descriptive statistics...........................................................................................................................................................................25
2 ANNOVA...........................................................................................................................................................................................25
3 Appropriateness of treatment..............................................................................................................................................................27
CONCLUSION..........................................................................................................................................................................................27

INTRODUCTION
In the current time period credit card business is running by the number of firms. In the current report data set related to credit
card is analyzed and in this regard descriptive statistical tool are used to analyze the data along with regression analysis tools. In
middle part of the report, correlation tool is applied to explore relationship among the variables. Apart from this, ANOVA tools is
used to identify whether there is relationship between geographic location and depression level. In this way, entire research is carried
out.
TASK 1
1 Descriptive statistics
Figure 1Descriptive statistics details
Interpretation

In case of variable income it is observed that on average basis people are earning $43000 followed by standard deviation which is
14.55. On other hand, in case of variable household on average basis size of the entire family is 3.42 which means that on average
basis there are 3 to 4 members in each family from whom data is gathered. Standard deviation is 1.73 which means that family size is
approx. same over time period and it does not change at rapid pace. Mean value of amount charged is 3963.86 which means that there
are large number of respondents which are making annual charge of mentioned value in respect to credit card. Results are clearly
reflecting that the difference between minimum and maximum amount charged is high if same is compared with relevant studied
variables which are given in the table. This is reflecting that people become more habitual of using credit card to meet their needs.
Even people are making overuse of credit card if we compare and take in to account their income level.
2 Regression analysis
H0: There is no significant mean difference between income level, household size and amount charged.
H1: There is significant mean difference between income level, household size and amount charged.

Figure 2Regression analysis

Figure 3Income residual chart
Figure 4Household size chart

Figure 5Income line fit plot
Figure 6Household size line fit plot
Interpretation

There is high degree of correlation among the variables income level, household and amount charged. This is proved from the
fact the correlation value is 0.90 which is very high and is near to 1. Results are also indicating that big percentage change comes in
the amount spend with slight change that happened in the values of the variables namely income level and household. As per facts
82% variation comes in the amount spend with variation in the mentioned variables. Adjusted R square is 0.81 which strongly
indicating that 81% variation comes in the amount charged when any new variable will be added in the data set. Level of significance
is 1.54>0.05 and this is clear that in terms of impact both independent variables does not have significant impact on the dependent
variable. Results are reflecting that in case income of individual changed then amount charged may be impacted by 33 points. In same
way if household size altered then amount charged may change by 356 points.
3 Equation of regression
Regression equation for income level and household are prepared above. It can be observed that value of intercept is 1305 and same of
beta is different in case of income level and household which are 33.12 and 356. X is the dependent variable. When value of
dependent variable is placed in the above mentioned model value for dependent variable is computed. It can be said that regression
analysis is the one of the most important tool that is used to make prediction for dependent variable.
4 Predicted credit card charge when family size is three
Household size= a+ bx
= 1305+356*3=2373
Results are clearly indicating that on mean basis average charge may be of value $2373 in case people income is $40000.

5 Modification of model
Model can be modified by adding new variable namely household saving rate and by using relationship that exist between the
saving rate and credit card charge can be identified.

TASK 2
Activity 01
Data are arranged in variable view in excel sheet.
Activity 02
(a)Drawing histogram