8.

A credit card company is trying to understand and even predict annual charges that a customer will make on their cards using 5 variables: annual income (in $1000), household size, years of post-high school education (labeled simply "Education"), hours per week watching television (labeled "TV Hours") and age of the primary card holder.

They collect data on 100 randomly selected customers and measure the variables described above. They then perform a multiple regression analysis and find the results shown in the questions below.

Important Note: While the scenario above applies to all of the questions below, the output shown in different questions may change from question to question. Please be careful that you use the appropriate output for each question.

For this question, use the following multiple regression output (which may differ from the output in other questions, even though the variables are the same).

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.645
R Square	0.416
Adjusted R Square	0.385
Standard Error	4599.979
Observations	100
ANOVA
	df	SS	MS	F	Significance F
Regression	5	1415098336	283019667	13.375	7.595E-10
Residual	94	1989021846	21159807
Total	99	3404120182
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	3176.846	2790.016	1.139	0.2577	-2362.796	8716.489
Annual Income ($1000)	119.665	16.134	7.417	5.240E-11	87.630	151.700
Household Size	592.620	220.534	2.687	0.0085	154.744	1030.496
Education	-617.648	307.172	-2.011	0.0472	-1227.546	-7.751
TV Hours	-7.574	29.487	-0.257	0.7978	-66.121	50.973
Age	-22.418	42.243	-0.531	0.5969	-106.293	61.456

How much variation in annual charges is explained by the model?

Group of answer choices

41.6%

38.5%

64.5%

7.595x10^-10

4599.979