You are a decision maker for a web-based marketing company that makes short marketing videos designed for social media. You are studying the relationship between a video's production budget and the number of likes it gets on the day it drops. You are going to look at a random sample of 6 recent videos. You will note the video's production budget (denoted by x, in thousands of dollars) and the number of likes (denoted by y, in thousands) the video got across social media on the drop date. You will also note the product x -y of the budget and the number of likes for each video. (These products are written in the row labeled ") (a) Click on "Take Sample" to see the results for your random sample. Production budget, x (in thousands of dollars) 88.1 53.5 27.6 364 71.6 45.7 Take Sample Number of likes, y 85 73 (in thousands) 3.8 52 6.7 4.9 xy 748.85 | 390.55 | 104.88 | 189.28 | 479.72 | 223.93 Based on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table below, n is the sample size and the symbol Z xy means the sum of the values x).) Sample correlation coefficient (): Slope (b ): y-intercept (b ): (b) write the equation of the least-squares regression line for your data. Then on the scatter plot for your data, graph this regression equation by plotting two points and then drawing the line through them. Round each coordinate to three decimal places. ~ _ Regression equation: y = % s ' Number of likes {in thousands) . Production budget (in thousands of dollars) (c) Use your regression equation to predict the number of likes a video gets if its production budget is 41.7 thousand dollars. Round your answer to one decimal place. Predicted number of likes: |:| thousand likes