Question 1 answer is already available on Chegg. Please only answer 2,3,4 and possibly 5. Please use Excel where possible with complete screenshots of steps, formulae etc.

Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of stereo speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for their particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 10 years and was reduced to the data that follow: 1) Perform a regression analysis based on these data using Excel (Data Analysis ToolPack -> Regression). a) Record the regression equation. b) Interpret the regression output tables, including values of R,R2, Standard Error, Significance F, Coefficients, and their p-values. c) Predict average yearly speaker sales for Speakers and Company based on the regression results if the price was $300 per unit and the amount spent on advertising (in thousands) was $900. 2) What other factors (variables) could be included in the regression analysis to possibly improve accuracy of the regression model? Is there any other factors that should be considered regardless the mathematical model? 3) Use any other quantitative forecasting model to forecast annual sales for 2021. 4) Make a conclusion which model is better to use to forecast demand for the stereo speakers (Hint: use forecast errors to justify). Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of stereo speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for their particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 10 years and was reduced to the data that follow: 1) Perform a regression analysis based on these data using Excel (Data Analysis ToolPack -> Regression). a) Record the regression equation. b) Interpret the regression output tables, including values of R,R2, Standard Error, Significance F, Coefficients, and their p-values. c) Predict average yearly speaker sales for Speakers and Company based on the regression results if the price was $300 per unit and the amount spent on advertising (in thousands) was $900. 2) What other factors (variables) could be included in the regression analysis to possibly improve accuracy of the regression model? Is there any other factors that should be considered regardless the mathematical model? 3) Use any other quantitative forecasting model to forecast annual sales for 2021. 4) Make a conclusion which model is better to use to forecast demand for the stereo speakers (Hint: use forecast errors to justify)