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Help is needed on question 1. The second picture is the data set “Showtime.xlsx” needed to answer the question .
Stat 351 Homework #5 (Section 15.8-16.1) Make sure to show your work if you did any caleulation, and Minitab output if you used Minitab. I. Please download the dataset Showtime.xlsx from Canvas. The dataset Showtime.xlsx gives the data on weekly gross revenue (y), television advertising (x1), and newspaper advertising (32) for Showtime Movie Theaters. Use Minitab to help you answer the following a) Develop an estimated regression equation to predict Weekly Gross Revenue (y) using b) Plot the standardized residuals against . What assumption about E you are checking here? c) Get a list of standardized residuals, standardized deleted residuals, leverages, and Cooks d) Are there any outliers? Please use each of the two methods of standardized residuals, e) Are there any influential observations? Please use each of the two methods of leverages, questions. Television Advertising (x) and Newspaper Advertising (x2) as the independent variables. Does the plot support the assumptions about ε? Explain. Distances. standardized deleted residuals SEPERATELY to detect outliers. Explain them clearly. and Cooks Distances SEPERATELY to detect influential observations. Explain them clearly 2. Please download the dataset Problem 16.3.xlsx from Canvas. Consider the date set and use Minitab to help you answer the following questions. Plot a scatter plot y against x. By making the primary conclusion based on the scatter plot, does there appear to be a linear relationship between r and y? Explain. a) b) Develop an estimated regression equation for the data of the form ý bot bx. c) Plot the residuals against y. What assumption about E you are checking here? Does the plot support the assumptions about ε? Explain. d) Perform a logarithmic (natural logarithm) transformation on the dependent variable y
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Welcome to Minitab, press F1 for help. Regression Analysis: y versus x1, x2 Analysis of Variance Source Regression 223435 11.7177 DE Adi SS Adj MS F-Value P-Value 28.38 56.73 16.46 0.002 0.001 0.010 1 23.425 23.4247 1 6.795 6.7953 5 2.065 0.4129 7 25.500 x2 Error Total Model Summary S R-sR-sq(adj) R-q (pred) 88. 66% 68 . 19 Coefficients Term CoefSE Coef T-Value P-Value VIF Constant 83.23 2.290 1.301 1.57 0.304 0.321 52.88 7.53 4.06 0.000 0.001 1.45 0.010 1.45 x1 ## a)! Regression Equation y83.23 + 2.290 x1 + 1.301 x2

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