The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $3 million. Is the result close to the actual number of viewers, 7.0 million? Use a significance level of 0.05. Salary (millions of $) Viewers (millions) 99 2 6 4.4 8.3 5 4. 9 15 4.1 2.2 1.4 8.8 4.9 4.3 E Click the icon to view the critical values of the Pearson correlation coefficient r. A Critical Values of the Pearson Correlation Coefficient r What is the regression equation? y=+ x (Round to three decimal places as needed.) Critical Values of the Pearson Correlation Coefficient r NOTE: To test H, p=0 against H,: p0, reject H, What is the best predicted number of viewers for a television star with a salary of $3 million? a =0.05 a= 0.01 4. 0.950 0.990 5 The best predicted number of viewers for a television star with a salary of $3 million is million. (Round to one decimal place as needed.) 0.878 0.959 6. 17 if the absolute value of ris greater than the critical value in the table. 0.811 0.917 0.754 0.875 Is the result close to the actual number of viewers, 7.0 million? 0.707 0.666 0.632 8 0.834 0.798 O A. The result is not very close to the actual number of viewers of 7.0 million. 10 11 12 13 0.765 0.735 0.708 O B. The result is exactly the same as the actual number of viewers of 7.0 million. 0.602 0.576 OC. The result is very close to the actual number of viewers of 7.0 million. 0.684 0.553 0.532 0.514 14 0.661 O D. The result does not make sense given the context of the data. 0.641 0.623 0.606 15 16 0.497 17 0.482 0.590 0.575 0.561 0.505 0.463 0.468 0.456 18 19 20 0.444 25 (0.396 Click to select your answer(s). 0.361 0.335 0.312 0.294 In279 30 35 0.430 40 0.402 45 50 0.378 0.361

The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly Gross Revenue ($1,000s) 96 90 ŷ = 95 92 95 94 94 94 Television Newspaper Advertising Advertising ($1,000s) ($1,000s) 5.0 2.0 4.0 2.5 3.0 3.5 2.5 3.0 1.5 2.0 1.5 2.5 3.3 2.3 4.2 2.5 (a) Develop an estimated regression equation with the amount of television advertising as the independent variable. (Round your numerical values to two decimal places. Let X₁ amount of television advertising in $1,000s and y represent the weekly gross revenue in $1,000s.) ŷ = represent the (b) Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables. (Round your numerical values to two decimal places. Let x₁ represent the amount of television advertising in $1,000s, x₂ represent the amount of newspaper advertising in $1,000s, and y represent the weekly…

Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported asy=−72.84x+79.07y=-72.84x+79.07and the r=−0.832r=-0.832.What proportion of the variation in y can be explained by the variation in the values of x?r² = %

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