Examine the data set for credit card bills. Create a regression model for credit card bills based on income. Compare this with the regression model for credit card bills based on income and the gender of the head of household. Does adding the gender variable improve the model significantly?
Answer to relevant QuestionsBuild a regression model, with indicator variables for battery brand, to assess whether there is a significant relationship between battery life in minutes and battery brand. This revisits the battery example in Chapter 11. Create a multiple regression model for the Salaries data set that includes years of postsecondary education and age as explanatory variables. Interpret the model. Check that the best model you selected in Exercise 9 meets the required conditions. In exercise Use Excel to create all possible Marks models, and then consider those that have two explanatory variables. Note that there are ...An MBA (Master of Business Administration) student decides to see if he can predict the Standard and Poor's Toronto Stock Exchange Composite Index from the price of one or more share prices of Canadian companies. a. The ...Use the model for credit card balances illustrated in Exhibit 14.32 to create a 95% prediction interval for the monthly credit card balance of a credit card holder where the age of the head of household is 45, income is ...
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