Refer to the partial regression output below, modeling website clicks (Q) as a function of time (in days) and days of the week (using dummy variables) Regression Statistics Multiple R 0 96688 R Square 0 934857 Adj R square 0 928249 Standard Error 216 2672 Observations 77 ANOVA df SS MS F Regression 7 46313735 6616248 141 4589 Residual 69 3227235 46771 52 Total 76 49540970 Coefficients Standard Error Intercept 839 3532 80 24137 Tues 640 508 92 45832 Wed 1657 135 92 38454 Thurs 1799 217 92 32414 Fri 1818 027 92 27714 Sat 1558 109 92 24355 Sun 870 3725 92 22338 t 20 28163 1 11339 What is the sample regression equation How much more are the predicted clicks on Saturday v clicks on Tuesday Comment on the power of the model and significance of the independent variables by calculating t statistics on each variable (assume the critical t is 2 0) On which day are the clicks the highest Predict clicks on day 100 when the day is a Friday Suppose instead you use ln(Q) as the dependent variable and t as the indendent variable, and find the following through regression In(Q) 3 46 (0 087)t Transform the above equation so that Q is on the left hand side and t is on the right hand side Predict clicks at t 100 What is the annual growth rate of clicks

The Answer is in the image, click to view ...

Answered step by step

Verified Expert Solution

Link Copied!

Question

1 Approved Answer

Posted on Jul 06, 2024

Refer to the partial regression output below, modeling website clicks (Q) as a function of time (in days) and days of the week (using dummy

Refer to the partial regression output below, modeling website clicks (Q) as a function of time (in days) and days of the week (using dummy variables).



Regression Statistics
Multiple R	0.96688
R Square	0.934857
Adj R square	0.928249
Standard Error	216.2672
Observations	77

ANOVA
	df	SS	MS	F
Regression	7	46313735	6616248	141.4589
Residual	69	3227235	46771.52
Total	76	49540970

	Coefficients	Standard Error
Intercept	839.3532	80.24137
Tues	640.508	92.45832
Wed	1657.135	92.38454
Thurs	1799.217	92.32414
Fri	1818.027	92.27714
Sat	1558.109	92.24355
Sun	870.3725	92.22338
t	20.28163	1.11339

What is the sample regression equation? How much more are the predicted clicks on Saturday v. clicks on Tuesday?
Comment on the power of the model and significance of the independent variables by calculating t-statistics on each variable (assume the critical t is 2.0). On which day are the clicks the highest? Predict clicks on day 100 when the day is a Friday.
Suppose instead you use ln(Q) as the dependent variable and t as the indendent variable, and

find the following through regression:

In(Q) = 3.46 + (0.087)t

Transform the above equation so that Q is on the left-hand side and t is on the right-hand side. Predict clicks at t = 100. What is the annual growth rate of clicks?