Ex Consider a credit card company that has a database of information provided by its customers when they apply for credit cards. An analyst has rated a multiple regression model for which the dependent variable in the model is credit card charges accrued by acti the dataset over the past year (y), and the independent variables are the customer's annual household income in thousands of dollars), number of members of the household (x) and number of years of post high school education (x). The figure provides Excel output for a multiple regression model estimated using a data set the company created. A SUMMARY OUTPUT 2 3 Regras Static M 324 5 RS Adjusted Square T Standard 3363162 4347 Observations 5.000 1 ANOVA 12 11 14 15 16 17 Hold . 4999 P C D E F 55 MS F Significance 47247 23 1341 5000 254672 4.20 Coeficient Standard P 2016 754556 120314 53314602 24301 33 13830 SOME 0000 O Years of Post High School Education-6324 - (a) the corresponding simple near regression with the atom's ad income in thesands of datars as the independent variable and credit card charges aced by a customer over the past year as the dependent variable (y) and your numenical values to the decimal places P=2001.639-120.632 Interpret the timed stationship between the castomer's annual household income and credit card charges accrued over the past year. Asual income decreases by $1,000, the credit card charges accrued by the customer over the past year all be highe O'lim decreases by $1,000, the credit card charges accrued by the customer over the past year all remained As a customer's annual income is by $1,000, the credit cart charges accrued by the customer over the past year will be higher O'lim by 1,000, the credit card changes acced by the customer over the past year