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 $2 million. Is the result close to the actual number of viewers, 4.1 million Use a significance level of 0.05. Salary (millions of $) Viewers (millions) 101 8 3 12 5.6 12 8 23 5.5 7.4 6.8 4.7 6.6 9.1 E Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? Critical values of the pearson correlation coefficient r x (Round to three decimal places as needed.) What is the best predicted number of viewers for a television star with a salary of $2 million? Critical Values of the Pearson Correlation Coefficient r a=0.05 0.950 0.878 0.811 0.754 0.707 0.666 a-0.01 0.990 0.959 0.917 0.875 0.834 0.798 0.765 0.735 0.708 0.684 0.661 0.641 NOTE: To test H p=0 Jagainst H;: p#0, reject H. Jf the absolute value of r is greater than the critical value in the table. The best predicted number of viewers for a television star with a salary of $2 million is[ million. 4 (Round to one decimal place as needed.) Is the result close to the actual number of viewers, 4.1 million? 6 8 OA. The result is exactly the same as the actual number of viewers of 4.1 million. OB. The result is not very close to the actual number of viewers of 4.1 million. OC. The result is very close to the actual number of viewers of 4.1 million. OD. The result does not make sense given the context of the data. 10 11 12 13 14 15 0.632 0.602 0.576 0.553 0.532 0.514 16 0.497 0.482 0.468 0.623 17 0.606 18 19 20 25 30 35 40 45 50 60 70 80 90 100 0.590 0.456 0.444 0.396 0.361 0.335 0.312 0.294 0.279 0.575 0.561 0.505 0.463 0.430 0.402 0.378 0.361 0.330 0.305 0.286 0.269 0.256 a-0.01 0.254 0.236 0.220 0.207 0.196 a=0.05 Print Done