Q.6. Chapter 13. A company is trying to estimate the linear relationship between its total revenue per month (in thousands of dollars) and the number of advertisements they place on the radio every month. They have data from the past year, and it is shown in the following table.

Months	Revenue (in $1,000s)	Advertisements
Jan	8.30	21
Feb	22.70	180
Mar	13.40	50
Apr	21.10	195
May	15.00	96
Jun	12.50	44
Jul	20.70	171
Aug	19.72	135
Sep	16.12	120
Oct	13.10	75
Nov	15.67	106
Dec	25.30	198

Run a regression analysis using the Revenues as the dependent variable and the number of advertisements as the independent variable and fill in the blanks below (round to 3 spaces after decimal point):

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.966
R Square	?
Adjusted R Square	0.927
Standard Error	1.340
Observations	12
ANOVA
	df	SS	MS	F	Significance F
Regression	1	?	251.954	?	0.000
Residual	10	17.965	?
Total	11	269.919
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	?	0.858	9.216	0.000	5.993	9.815
Advertisements	?	0.007	11.843	0.000	0.063	0.093

The boxes with question marks above are the blanks that need to be filled in.

If the company plans to air 100 radio advertisements next month, their expected revenue would be__________ $ (in $1,000s)