MAN 3504 - Operations Management Assignment 1b 8 Points Objective Learn how to calculate and interpret Linear Regression, Time-Series Forecasting and Productivity. Instructions For all the following questions, -show your step-by-step calculations for each (Points will be deducted if there are no calculation steps) - write their explanations i.e., what does the resulted value suggest about the forecast 1. Amazon's supply chain manager Peter Baelish needs to forecast the demand for loungers and believes that the demand is related to digital advertising expenditures. Use linear regression to develop an equation and a forecast for this product. (.5 * 7) The following are sales and advertising data for the past 5 months: Month Sales (thousands of units) Advertising (thousands of $) 1 264 2.5 2 116 1.3 3 165 1.4 4 101 1.0 5 5 209 2.0 A. Which is the dependent and independent variable and why? B. Find the mean of sales and advertising. I and y. Also find the standard deviation of sales and advertising oy and 03 - What does it signify? C. What is the value of correlation coefficient, "1" ? What does it mean? D. What is the Value of coefficient of determination, ?" ? What does it mean? E. Find the intercept "a" and the value of slope "6" F. Write the regression equation. G. If the company spends $1,750 next month on advertising for the product, what is the estimate for the sales 2. The Incredible You Company are the leading Jewelers store in Albany, New York. They have decided to prepare a digital media budget for marketing, based on the next quarter's business plan. The manager wants to decide the mix of internet advertising (Adv.) needed to generate varying levels of Weekly Gross Revenue. The past five weeks of the collected data by the manager has recorded the following average Weekly Gross Revenues (in thousands of dollars) and expenditures for Weekly Radio advertising in thousands of dollars). The following is the Summary Output for the Linear Regression: 0 SUMMARY OUTPUT Regression Statistics Multiple R 0.86 R Square 0.74 Adjusted R Square 0.65 Standard Error 7.01 Observations 5 ANOVA of SS F Significance F 0.06 1 Regression Residual Total MS 422.50 49.17 8.59 422.50 147.50 570.00 3 4 Standard Coefficients Error 28 9.41 6.5 2.22 Intercept Adv. t Stat 2.98 2.93 P-value 0.06 0.06 Lower 95% -1.94 -0.56 Upper 95% 57.94 13.56 Use the information from the summary output to answer the following questions (565) A. Which is the independent variable? B. For every unit increase in advertising, the weekly gross revenues increase by C. Calculate the value of revenues when advertising is $4,300 ? D. To achieve revenues of $55,000 advertising spend should be: E. If there is no advertising, then how much the product revenues would be 2. The Incredible You Company are the leading Jewelers store in Albany, New York. They have decided to prepare a digital media budget for marketing, based on the next quarter's business plan. The manager wants to decide the mix of internet advertising (Adv.) needed to generate varying levels of Weekly Gross Revenue. The past five weeks of the collected data by the manager has recorded the following average Weekly Gross Revenues (in thousands of dollars) and expenditures for Weekly Radio advertising in thousands of dollars). The following is the Summary Output for the Linear Regression: 0 SUMMARY OUTPUT Regression Statistics Multiple R 0.86 R Square 0.74 Adjusted R Square 0.65 Standard Error 7.01 Observations 5 ANOVA of SS F Significance F 0.06 1 Regression Residual Total MS 422.50 49.17 8.59 422.50 147.50 570.00 3 4 Standard Coefficients Error 28 9.41 6.5 2.22 Intercept Adv. t Stat 2.98 2.93 P-value 0.06 0.06 Lower 95% -1.94 -0.56 Upper 95% 57.94 13.56 Use the information from the summary output to answer the following questions (565) A. Which is the independent variable? B. For every unit increase in advertising, the weekly gross revenues increase by C. Calculate the value of revenues when advertising is $4,300 ? D. To achieve revenues of $55,000 advertising spend should be: E. If there is no advertising, then how much the product revenues would be