4

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	0.507
R Square	0.257
Adjusted R Square	0.217
Standard Error	5319.659
Observations	100
ANOVA
	df	SS	MS	F	Significance F
Regression	5	918149733.6	183629946.7	6.489	3.175E-05
Residual	94	2660085040	28298777.02
Total	99	3578234773
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	9714.244	3289.053	2.954	0.0040	3183.751	16244.736
Annual Income ($1000)	87.421	18.063	4.840	5.082E-06	51.557	123.284
Household Size	-145.809	270.677	-0.539	0.5914	-683.244	391.627
Education	-676.427	335.456	-2.016	0.0466	-1342.483	-10.371
TV Hours	42.242	31.228	1.353	0.1794	-19.762	104.247
Age	-72.153	52.101	-1.385	0.1694	-175.601	31.295

Is the overall model significant at the .05 level of significance?

Group of answer choices

No, with a p-value of 0.0040.

No, with a p-value of 3.175x10^-5.

No, with a p-value of 3.175.

Yes, with a p-value of 0.0040.

Yes, with a p-value of 3.175x10^-5.