A manager at a roadside cafe is concerned that the weak economic environment has caused a decline in sales. To offset the decline in sales, the manager has pursued a strong advertising campaign (billboards up and down the road). She believes advertising expenditures have a positive influence on sales. To support her claim, the manager estimates the following linear regression model: Sales = β0 + β1Unemployment + β2Advertising + ε. A portion of the regression results is shown in the accompanying table. ANOVA df SS MS F Significance F Regression 2 72.6374 36.3187 8.760 0.003 Residual 14 58.0438 4.1460 Total 16 130.681 Coefficients Standard Error t Stat p-Value Intercept 17.5060 3.9817 4.397 0.007 Unemployment –0.6879 0.2997 –2.296 0.038 Advertising 0.0266 0.0068 3.932 0.002 What is the value of the test statistic regarding whether the predictor variables jointly influence sales (round to 3 decimal places)? At the 5% significance level, do the predictor variables jointly influence sales? (Y/N) Is Advertising negatively related with sales? (Y/N) Are both Unemployment and Advertising statistically significant in this model? (Y/N)

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A manager at a roadside cafe is concerned that the weak economic environment has caused a decline in sales. To offset the decline in sales, the manager has pursued a strong advertising campaign (billboards up and down the road). She believes advertising expenditures have a positive influence on sales. To support her claim, the manager estimates the following linear regression model: Sales = β₀ + β₁Unemployment + β₂Advertising+ ε. A portion of the regression results is shown in the accompanying table.