Discussion Assignment #2

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University Of Arizona *

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Information Systems

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Feb 20, 2024

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ANALYTICS REPORT TO: JOHN MCCLINTOCK FROM: MADDY RAKER SUBJECT: DISCUSSION 2 DATE: 02/06/2024 Introduction This data set was using 25,000 of our Taiwanese customers looking at differences in average bill amounts between males versus females and different payment amounts between the months of September and April. The purpose of this assignment is to make a recommendation the Marketing team on which demographic to target advertisements to based on data analysis and to assist the financial department with the best budgeting system for monthly payments between September and April. I will use two sample t- tests for this statistical analysis, first testing if the average bill amounts differ between men and women, and the second t-test to see if payments differ between September and April. I was able to confirm that the average bill amounts do differ between men and women. I was also able to conclude that average bill payments do differ between September and April. Data Analysis Average Bill Amounts Between Males and Females See Appendix for t-test output H 0 : There is not a difference in average bill amounts between males and females. H A : There is a difference in average bill amounts between males and females. Because the p-value of 7.99E-06 is less than the significance level, we can reject the null hypothesis and can conclude that there is a difference in average bill amount between males and females. Since we found a significant difference, we can look at the sample averages to see that males have higher average bill amounts. To determine a range of possible differences in bill amounts, we will calculate a 95% confidence interval. ( 1571.33 − 1444.97 ) ± 1.96 √ 4931902.52 9533 + 4177692.55 14765 [ 70.91,181.81 ] Since the interval does not include zero, we can conclude there is a difference. Since the interval was set up male minus female and is entirely positive, we can say that males spend more on average by $70.91 to $181.81.
Since men are spending more on average, we should target males with our advertising to try and influence them to use our credit cards as opposed to a competing company’s card. To increase the amount of female spending, we could promote our credit cards by offering cash back or discounts for signing up to support more women using our cards. Average Payment Amounts Between April and September See Appendix for t-test output H 0 : There is not a difference in average payments between the months of April and September. H A : There is a difference in average payments between the months of April and September. Because the p-value of 0.0004 is less than the significance level, we can reject the null hypothesis and can conclude that there is a difference in average payment amounts between April and September. Since we found a significant difference, we can look at the sample averages to see that September payments have a higher average than April amounts. 15.153 ± 1.96 ( 711.5 √ 24298 ) [ 6.206,24.099 ] Since the interval does not include zero, we can conclude there is a difference. This confidence interval for the mean difference is not overlapping, we can say that spending is higher in the month of September by 6.0206-24.099$. Since bill payments are higher in September than April, I would recommend budgeting money to be saved for months such as April where there is less profit from credit card purchases. We could collect and save revenue from the Fall when revenue is higher to cover expenses when there is less revenue in the Spring. Conclusion I was able to find that men and women average monthly bill spending differed. I was also able to find that monthly spending between September and April differed on average. My recommendation is to target ads towards men from a marketing standpoint, and to focus our budgets in September when spending is much higher.
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