Regression Analysis of Relationship between Annual Income and Credit Card Charges

This report evaluates the relationship between annual income and credit card charges using regression analysis. It also analyzes the impact of household size as an independent factor and the need for other independent variables. The report concludes that there is a significant relationship between household size and credit card charges.

Quantitative Methods

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
Developing regression equation with annual income as independent factor and household size
as well.........................................................................................................................................3
Developing regression equation with help of both as independent variable...............................4
Need for other independent variable which can be added..........................................................5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
Quantitative method involves the statistical tools and techniques which is existed in
interpreting the data relating to the research. The present report will focus on using the statistical
tool of regression for analysing the relationship between the two variables. With the help of the
regression analysis the relationship between variables like annual income size and the amount
charged on credit card will be analysed.
MAIN BODY
Developing regression equation with annual income as independent factor and household size as
well
Annual income as independent factor
Regression
Statistics
Multiple R 0.29154
R Square 0.08499
Adjusted R
Square 0.03416
Standard Error 1162.97
Observations 20
ANOVA
df SS MS F
Significance
F
Regression 1 2261393 2261393 1.672 0.21
Residual 18 2.4E+07 1352506
Total 19 2.7E+07
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 2741.39 893.483 3.06821 0.00662 864.252 4618.53 864.252 4618.53
Income
($1000s) 23.7446 18.3631 1.29306 0.21234 -14.835 62.324 -14.835 62.324
Regression equation Y= 23.7446 X + 2741.39

Analysis of the regression equation it is clear there is no significant relationship between
the annual income and the charges being paid. This is particularly because of the reason that the
significance value is 0.21 which is greater than the significant value of 0.05.
Household size as independent factor
Regression
Statistics
Multiple R 0.53236
R Square 0.28341
Adjusted R
Square 0.2436
Standard Error 1029.18
Observations 20
ANOVA
df SS MS F
Significance
F
Regression 1 7540602 7540602 7.11904 0.02
Residual 18 1.9E+07 1059216
Total 19 2.7E+07
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 2453.37 570.669 4.29911 0.00043 1254.44 3652.3 1254.44 3652.3
Household
Size 392.488 147.101 2.66815 0.01568 83.4402 701.536 83.4402 701.536
Regression equation Y = 392.488 X + 2553.37
In case when the household size was taken as the independent factor and the annual
charges as the dependent factor then there was a significant relationship between both the
variables (Stockemer, Stockemer and Glaeser 2019). This is particularly because the significant
value was 0.02 which is less than the standard that is 0.05. In addition to this the correlation was
also good that is 53%. This implies that correlation is also good and in case there is a change in
independent factor then there will be 28% change independent factor as well.
Developing regression equation with help of both as independent variable
Regression

Statistics
Multiple R 0.57233
R Square 0.32757
Adjusted R
Square 0.24846
Standard Error 1025.87
Observations 20
ANOVA
df SS MS F
Significance
F
Regression 2 8715403 4357702 4.14066 0.03
Residual 17 1.8E+07 1052417
Total 19 2.7E+07
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 1734.5 886.85 1.9558 0.06713 -136.59 3605.59 -136.59 3605.59
Income
($1000s) 17.3316 16.404 1.05655 0.3055 -17.278 51.9411 -17.278 51.9411
Household
Size 367.721 148.49 2.4764 0.02408 54.4342 681.008 54.4342 681.008
Regression equation Y= 17.3316 X + 367.721 X1 + 1734.5
In addition to this when both household and annual income was taken as the independent
factor then also the significant value was 0.03 which is less than the standard (Hosseini, Ivanov
and Dolgui, 2019). Hence it can be implied that there is a significant relationship between all the
three variables together. The multiple are was a 0.57 which implies that there is 57% correlation
between the variables. Along with this any change within the two independent factum will cause
a 32% of change in the dependent factor is well.
Need for other independent variable which can be added
For analysing the amount charged by credit card users another factor of variable which
will be assistive in undertaking is the trends of the shopping pattern done by consumers (Sablan,
2019). This is necessary because the credit card is majorly used at time of shopping only. Sensor
in case there will be a proper data relating to the shopping pattern and payments being made by

the credit card then it will be assisted in identifying the credit card charges in more better and
effective manner.
CONCLUSION
In the end it is concluded that quantitative data involves the techniques which are used to
analyse the data with help of some statistical tools. The use of statistical tool is very helpful for
the companies and researcher to effectively analyse the datum and draw some conclusions from
it. The above report evaluated that in case the annual income is the only independent factor then
there is no significant impact over the credit card charges. But in case the household size is being
taken as the independent factor then the change in one factor will cause change in another factor
as well. In the similar manner it was also evaluated that when both the factors were combined
and regulation was conducted it was evaluated that there is a significant relationship.

REFERENCES
Books and Journals
Stockemer, D., Stockemer, & Glaeser. (2019). Quantitative methods for the social sciences (Vol.
50, p. 185). Springer International Publishing.
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain
resilience analysis. Transportation Research Part E: Logistics and Transportation
Review. 125. 285-307.
Sablan, J. R. (2019). Can you really measure that? Combining critical race theory and
quantitative methods. American Educational Research Journal. 56(1). 178-203.