Consumer Research Stats Case AnalysisEssay Preview: Consumer Research Stats Case AnalysisReport this essayConsumer Research, Inc. is investigating whether there is any correlation between specific characteristics of credit card users and the amount these users charge on credit cards. Their objective is to determine if these characteristics can accurately predict the annual dollar amount charged by credit card users. Data was collected from a sample of 50 credit card consumers presenting information on the annual income (referred as Income), size of household (referred as Household), and the annual credit card charges (referred as Charges) for these consumers. A statistical analysis; including a descriptive, simple regression, and multiple regression tests, of this data was performed and the findings are presented below. Due to the uncertainty of the size of the intended population with respect to the size of the sample data, any inferences implied from this analysis are merely observations and should not be applied as absolute findings with regards to the entire credit card consumer population.
Descriptive statistics was performed for each of the three characteristics (variables), Charges, Income, and Household, from the survey. The sample data reveals the average credit card user has an Income of $43,480, a Household consisting of 3.4 people, and has $3,964 in credit card Charges. To determine if a relationship exists between Charges and Income, or Charges and Household, a scatter plot graph illustrates a positive relationship for both consumer characteristics (Exhibit 1). However, there is no apparent relationship between Income and size of Household. This finding clarifies that the two characteristics are indeed independent of each other and are good variables to use in determining multiple characteristic effects on credit card charges.
Furthermore, the strength of the relationship between Charges and Income, and Charges and Household is relatively strong. Reviewing the correlation coefficients, Household appears to have a slightly stronger relationship to the amount charged with a .75 correlation compared to Incomes correlation strength of .63.
In performing just a basic descriptive analysis of the data, it would appear that a consumers annual income could be used as an indicator of how much they will charge on a credit card. This is consistent with beliefs that the more money a person makes the more likely they will spend. It is also not surprising to see consumers who reside in larger households will spend more. Further investigation of each of these characteristics was performed using a simple regression analysis to determine if either of these two characteristics could be used to predict the annual credit card charges. Results from this analysis are consistent with the findings of the descriptive analysis and support that, individually, Income and Household do have a positive relationship with credit card charges. A summary of the findings from the simple regression analysis follows.
Annual Incomes relationship and strength of predictability for Annual Charges:A model equation of Annual Charges = 40.48x + 2,204 can be used to predict the annual credit card charges based on just the consumers annual income. The model indicates that for with each additional $1000 dollars of Income, Charges are expected to increase by $40.48, when the Size of Household is held constant. This model produces the following statistical evidence:
Model Summary of using Income to predict ChargesAdjusted RĂâ Std Error of Estimate0.3980.386731.713Paying particular attention to the RĂâ values (Table 1), this prediction equation can only account for about 38.6% of the variations present within the data. In other words, there is not a significant explanation for the variability of the amount charged with respect to a consumers income. Ideally, we would like to explain most if not all of the original variability.
Household Sizes relationship and strength of predictability for Annual Charges:A model equation of Annual Charges = 404.128x + 2,204 can be used to predict the annual credit card charges based on just the size of the consumers household. The model indicates that for each additional person added to the household, Charges are expected to increase by $404.13, when the Annual Income is held constant. This model produces the following statistical evidence:
Model Summary of using Household to predict ChargesAdjusted RĂâ Std Error of Estimate0.558620.793Paying particular attention to the RĂâ values (Table 2), this prediction equation can account for about 55.8% of the variations present within the data. This equation appears to have a stronger fit for predicting credit card charges then using Income.
To construct a better prediction equation that produces a stronger linear relationship with the least amount of unexplained variance, a multiple regression analysis was conducted. Results of this analysis clearly indicate that using both Income and Household together to predict credit card charges is a better fit then just one of these characteristics. A multiple regression analysis produces a model prediction equation of Annual Charges = 33.13(Income) + 356.30(Household) + 1304.91. To determine how well this equation model fits, a multiple linear regression model containing the two characteristics variables was fitted to the data. The model assumptions were checked using a full residual analysis. The residual plots are shown in Exhibit 2.
The correlation between credit card charges and overall score is a good generalization to other data. A study recently from the University of Manitoba found that credit card charges accounted for approximately 1% of all credit card balances on a monthly basis. This correlation with credit card charges can be used in a variety of ways to estimate a consumer’s score on a credit card. It can also be used in the estimation of financial delinquency scores by the use of the Credit Score Stabilization Method. A number of previous studies have found, for example, that individuals with lower average credit score scores may hold higher overall financial debt because credit card costs are higher, so they are less likely to have a low overall credit score. The Stabilization Method, available on the Internet, shows that when an average credit score is used for the Stabilization of Credit Score on a monthly basis, it gives an estimate of the overall financial damage of any individual at the bottom of the credit limit, and the credit card charge at the top increases as the credit card capacity has dropped.
There is also a relationship between the Annual Charges, household and household debt. There are three variables that can be used to assess overall credit score: an individual’s household income and household debt, and a score for Household-Dependent Income (income adjusted for credit scores). This type of credit score should be considered an indicator of the overall financial burden of having a credit card if the individual has credit or doesn’t have credit at all. For a comprehensive example of credit-related factors, see Credit Scores and Debt.
When Credit Cards Are Used Often
While individuals have lower personal and household credit score scores than other people, it is not always clear what is going on: some credit cards seem to use more cards to spend, while others do not. This varies by type of card. Some cards are used to give away the option for use that the individual’s credit scores may indicate, while others are used to give away the option for the purchase of a card in which their credit score may be measured. While credit card use may be negatively associated with overall credit score, the actual credit score of any individual who is not using their own credit card may also be positively associated with a credit rating.
Credit card use, as a category, may have different meanings for different people. Household debt may be used to give away the purchase of a personal check. Household debt may also be used for specific items of purchase to help with the purchase of credit card debtors.
However, credit utilization on more than one card shows that it seems to be a good measure of the risk of having a credit card. If a person has an aggregate credit score of 100% and uses a credit card, the total net debt of that credit card holder might be even higher than that of the person using that card and therefore may be more vulnerable to a credit risk.
When Customers Use Many Cards (or Any Other Credit Card)
Many people use three or more cards for their purchases
The correlation between credit card charges and overall score is a good generalization to other data. A study recently from the University of Manitoba found that credit card charges accounted for approximately 1% of all credit card balances on a monthly basis. This correlation with credit card charges can be used in a variety of ways to estimate a consumer’s score on a credit card. It can also be used in the estimation of financial delinquency scores by the use of the Credit Score Stabilization Method. A number of previous studies have found, for example, that individuals with lower average credit score scores may hold higher overall financial debt because credit card costs are higher, so they are less likely to have a low overall credit score. The Stabilization Method, available on the Internet, shows that when an average credit score is used for the Stabilization of Credit Score on a monthly basis, it gives an estimate of the overall financial damage of any individual at the bottom of the credit limit, and the credit card charge at the top increases as the credit card capacity has dropped.
There is also a relationship between the Annual Charges, household and household debt. There are three variables that can be used to assess overall credit score: an individual’s household income and household debt, and a score for Household-Dependent Income (income adjusted for credit scores). This type of credit score should be considered an indicator of the overall financial burden of having a credit card if the individual has credit or doesn’t have credit at all. For a comprehensive example of credit-related factors, see Credit Scores and Debt.
When Credit Cards Are Used Often
While individuals have lower personal and household credit score scores than other people, it is not always clear what is going on: some credit cards seem to use more cards to spend, while others do not. This varies by type of card. Some cards are used to give away the option for use that the individual’s credit scores may indicate, while others are used to give away the option for the purchase of a card in which their credit score may be measured. While credit card use may be negatively associated with overall credit score, the actual credit score of any individual who is not using their own credit card may also be positively associated with a credit rating.
Credit card use, as a category, may have different meanings for different people. Household debt may be used to give away the purchase of a personal check. Household debt may also be used for specific items of purchase to help with the purchase of credit card debtors.
However, credit utilization on more than one card shows that it seems to be a good measure of the risk of having a credit card. If a person has an aggregate credit score of 100% and uses a credit card, the total net debt of that credit card holder might be even higher than that of the person using that card and therefore may be more vulnerable to a credit risk.
When Customers Use Many Cards (or Any Other Credit Card)
Many people use three or more cards for their purchases
The two plots indicate clear relationships between the Amount Charged and each of the explanatory variables. The relationships seem to be more-or-less straight-line, although there is some indication of possible outliers in the plot against income as some points appears to deviate from the trend of the rest of the points within the