Adwords Revenue 50 588 75 438 100 452 125 518 150 534 175 525 A small Internet c

Question

Adwords Revenue 50 588 75 438 100 452 125 518 150 534 175 525 A small Internet company wants to determine how the money they spend on Google Adwords impacts their monthly revenue. Over 6 consecutive months, they vary the amount they spend on their Adwords campaign(n) and record the associated revenue (in $) for each month. The data is shown below. Assignment 10q2 data a) Develop a regression equation for predicting monthly revenue based on the amount spent with Adwords. What is the y- intercept? Give your answer to two decimal places b) What is the proper interpretation of the y-intercept in the regression equations? O The y-intercept describes the expected revenue if the company spends $25 in a given month on Adwords. O The y-intercept describes the expected increase in revenue for each additional dollar spent on Adwords. OThe y-intercept describes the expected revenue if the company does not spend any money in a given month on Adwords OThe y-intercept describes the expected decrease in revenue for each additional dollar spent on Adwords. c) What is the sample correlation between these two variables? Give your answer to two decimal places. d) What is the slope of your regression equation? Give your answer to two decimal places. e) Using a 0.05 level of significance, does this regression equation appear to have any value for predicting revenue based on Adwords expenditures? Yes because there is a significant linear relationship between the two quantities. No because there is not a significant linear relationship between the two quantities. O Yes because there is not a significant linear relationship between the two quantities. No because there is a significant linear relationship between the two quantities.

Explanation / Answer

The statistical software output for this problem is:

Simple linear regression results:
Dependent Variable: Revenue
Independent Variable: Adwords
Revenue = 504.15238 + 0.044571429 Adwords
Sample size: 6
R (correlation coefficient) = 0.037449857
R-sq = 0.0014024918
Estimate of error standard deviation: 62.191448

Parameter estimates:

Analysis of variance table for regression model:

Hence,

a) Intercept = 504.15

b) Interpretation: Option C is correct.

c) Sample correlation = 0.04

d) Slope = 0.04

e) Option B is correct.

Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 504.15238 71.555566 0 4 7.0456068 0.0021 Slope 0.044571429 0.59466398 0 4 0.074952292 0.9439

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