Question

A movie analyst wants to predict revenue ("revenue") in $millions using the length of the movie in minutes ("runtime") and the budget of the film ("budget") in millions of dollars. A multiple linear regression model, where Y = revenue, X1 = runtime, and X2 = budget, has been fit. The output, with some missing values shaded, is displayed below. Regression Statistics Multiple R 0.682240936 R Square Adjusted R Square 0.461124376 Standard Error 63.95889146 Observations 250 ANOVA df Significance F 2.54853E-34 Regression Residual Total 2 247 249 SS MS 879808.6199 439904.3 1010412.73 4090.74 1890221.35 Intercept runtime budget Coefficients Standard Error t Stat P-value -16.22893446 21.74792685 -0.74623 0.456239 0.355518869 0.197245185 1.802421 0.072698 1.130174551 0.085531683 13.21352 1.65E-30 Lower 95% Upper 959 -59.06397088 26.60610196 0.744015886 0.96171009 1.298639012 Refer to the information above regarding the movie analyst interested in predicting revenue. At the a= 0.05 level, what would be the conclusion of the test of the following hypotheses in the context of the problem? Hint: you can get the answer three different ways using the output. HO B = 0 H: 8,0 Reject Ho Can conclude that the mean domestic revenue is greater than 0.356 when budget is in the model. Fail to reject Ho Cannot conclude that the mean runtime of movies and domestic revenue are equal. O Reject Ho. Can conclude that budget is potentially useful for predicting revenue when runtime is in the model. O о Reject Ho. Can conclude that runtime is potentially useful for predicting revenue when budget is in the model. Fail to reject H, Cannot conclude that runtime is potentially useful for predicting revenue when budget is in the model. O

Answer 1