A global sponsor of the Winter Olympics wants to better unde…
A global sponsor of the Winter Olympics wants to better understand the factors that influence product revenue during the Olympic period. The analytics team believes that revenue may be influenced by multiple variables, not just advertising spending. The following data were collected from the past five Winter Olympics (all dollar amounts in millions): Winter Olympics Year Advertising Spending (X₁) TV Viewership (millions) (X₂) Digital Impressions (millions) (X₃) Product Revenue (Y) Year 1 12 80 150 30 Year 2 18 95 210 38 Year 3 20 110 250 45 Year 4 25 130 300 54 Year 5 30 150 360 65 The company wants to estimate the following multiple linear regression model: Y^=b0+b1X1+b2X2+b3X3\hat{Y} = b_0 + b_1X_1 + b_2X_2 + b_3X_3Y^=b0+b1X1+b2X2+b3X3 where: X1X_1 = Advertising Spending X2X_2 = TV Viewership X3X_3 = Digital Impressions YY = Product Revenue Question: Using EXCEL to estimate the regression equation and report the values of b0,b1,b2,b_0, b_1, b_2, and b3b_3. Interpret the coefficient for Advertising Spending (b1b_1) in context, holding the other variables constant. Briefly explain why multiple linear regression may provide better insight than simple linear regression in this scenario.