Month/Yr. Jan. 2016 PERIOD PRICE AIP 1 5.4 5.9 2 5.5 6.6 3 6.0 6.4 4 6.1 6.1 5 5.9 6.4 6 5.9 6.3 7 5.9 6.0 8 6.8 6.0 9 6.8 5.8 10 6.4 6.3 11 6.5 6.3 12 6.3 6.2 13 6.1 6.5 14 6.1 6.6 15 6.0 6.3 16 6.4 6.7 17 6.2 6.5 18 6.0 6.8 19 6.1 6.6 20 6.4 6.1 21 6.0 6.1 22 6.2 6.2 23 6.1 6.0 24 6.0 6.2 25 6.1 6.7 DIFF 0.5 1.1 0.4 0.0 0.5 0.4 0.1 -0.8 -1.0 -0.1 -0.2 -0.1 0.4 0.5 0.3 0.3 0.3 0.8 0.5 -0.3 0.1 0.0 -0.1 0.2 0.6 ADV 5.3 6.8 7.3 7.3 7.2 6.5 6.8 5.0 5.8 5.5 6.5 6.3 7.0 7.7 6.8 6.8 7.1 7.0 7.2 7.5 7.8 8.2 8.3 8.4 8.9 DEMAND 13.9 14.5 14.7 14.9 14.9 14.6 14.1 12.0 14.2 13.9 13.9 13.8 14.0 14.5 16.0 15.7 15.8 15.2 15.9 16.2 15.0 16.9 17.1 16.9 17.4 Jan. 2017 Jan. 2018 Jan. 2018 0.6 Jan. 2019 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 6.1 5.9 6.0 6.3 6.0 5.7 5.6 6.2 6.4 6.5 6.2 6.7 6.9 6.9 6.7 7.0 7.1 7.2 7.2 7.3 7.2 7.1 6.9 7.2 7.3 6.7 6.9 5.8 5.8 6.0 6.7 6.4 7.0 7.2 5.9 6.0 6.2 6.0 6.3 6.5 6.0 6.1 6.3 6.4 6.5 6.0 6.2 5.9 6.0 6.4 1.0 -0.2 -0.5 0.0 1.0 0.8 0.8 0.8 -0.6 -0.2 -0.5 -0.9 -0.6 -0.2 -1.0 -1.0 -0.9 -0.8 -0.8 -1.2 -0.9 -1.0 -1.2 -0.9 8.9 9.1 9.3 9.4 9.3 9.4 9.5 9.6 9.7 9.9 9.8 9.9 10.1 10.2 10.5 10.3 9.9 10.5 10.6 10.5 11.6 10.1 10.3 10.7 10.9 17.4 17.7 17.6 18.4 18.6 17.4 18.4 17.6 16.7 18.2 18.5 19.1 19.0 19.0 19.8 19.8 20.0 20.9 19.6 19.5 18.4 19.3 19.3 19.9 20.0 Jan. 2020 Jan. 2020 49 7.3 10.9 10.8 11.1 11.2 11.6 50 51 52 53 54 55 56 57 58 59 60 61 62 63 7.4 7.5 7.0 6.8 7.4 7.3 7.3 7.2 7.5 7.5 7.5 6.4 6.5 6.5 6.2 6.8 6.9 6.5 6.9 7.0 6.8 6.8 6.5 -0.9 -0.9 -1.0 -0.8 0.0 -0.5 -0.8 -0.4 -0.2 -0.7 -0.7 -1.0 11.5 11.6 11.9 11.8 11.9 12.0 11.9 20.0 20.1 20.1 20.2 21.1 20.6 20.7 21.3 21.4 21.5 21.8 21.5 Jan. 2021 Feb. 2021 Mar. 2021 Forecasting Case Analysis MGT 3332 Spring 2021 8- Use regression to perform trend analysis on the de-seasonalized demand values. Is trend analysis suitable for this data? Find MAD and explain the Excel Regression output (trend equation. 1.1-squared, goodness of model). 9- Find the seasonally adjusted trend forecasts for January through March 2021 Important Notice: All team members must do all parts of the project and compare results. DO NOT divide up parts of the project as many parts are related. Teams must submit a copy of Word file and Excel file of their work through Blackboard Dropbox. Do not submit any file until all team members agree on the deliverables. Only one team member will submit the files. There is no second chance on submittal. 10- Perform simple linear regression analysis with ADV as the independent variable to predict demand. Write the complete equation, find MAD and explain the Excel Regression output. Make sure to use the de-seasonalized demand data for this model and all future models. The Fresh Detergent Case 11- Repeat part (11) with DIFF as the independent variable. 12- Construct multiple linear regression model with Period. AIP, DIFF, and ADV as independent variables. Formulate the equation, find MAD, and explain the output. Rank variables based on their degree of contribution to the model. Observe significant F, R- squared, and p-values and explain. Enterprise Industries produces Fresh, a brand of liquid detergent. In order to more effectively manage its inventory, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 48 sales periods. Each sales period is defined as one month. The variables are as follows: Demand = Y = demand for a large size bottle of Fresh (in 100,000) Price = the price of Fresh as offered by Ent. Industries AIP = the average industry price ADV = Ent. Industries Advertising Expenditure (in $100,000) to Promote Fresh in the sales period. DIFF = AIP - Price = the "price difference" in the sales period 1- Download the data from Course Blackboard site into Excel spreadsheet. 13- Perform multiple linear regression analysis with Period, DIFF, and ADV as independent variables. Formulate the equation and find MAD. Which variable is the most significant predictor of demand? Rank the independent variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain. 14- Use the model obtained in parts 14 and make forecasts for the following months. Make sure to seasonalize final forecasts. 2- Make time series scatter plots of all five variables (five graphs). Insert trend line, equation, and R-squared. Observe graphs and provide interpretation of results. Period Jan. Feb. Mar Price $10.00 $9.35 $9.10 2021 2021 2021 AIP $8.95 $8.60 $9.05 ADV $11.1 $11.3 $12.2 3- Construct scatter plots of Demand vs. DIFF and Demand vs. ADV. Demand vs. AIP, and Demand vs. Price. Insert fitted line, equation, and R-squared. Observe graphs and provide interpretation. Note that Demand is always on the Y axis. 15- Provide a case conclusion based on above analysis. 4- Obtain the correlation matrix for all six variables and list the variables that have strong correlation with Demand. High correlation is r> 0.70. Explain your findings in plain language. Grading Criteria Completeness/Correctness Quality of Interpretations/Analysis General Quality of Report graphs/writing 70% 20% 10% 5. Use 3-month and 6-month moving averages to predict the demand for January 2021. Find MAD for both forecasts and identify the preferred one based on each calculation. Is the moving average suitable method for forecasting for this data set? Explain your reasoning. 6- Use Exponential smoothing forecasts with alpha of 0.1, 0.2.0.5.0 0.9 to predict January 2021 demand. Identify the value of alpha that results in the lowest MAD. 7- Find the monthly seasonal indices for the demand values using Simple Average (SA) method. Find the de-seasonalized demand values dividing monthly demand by seasonal indices. Month/Yr. Jan. 2016 PERIOD PRICE AIP 1 5.4 5.9 2 5.5 6.6 3 6.0 6.4 4 6.1 6.1 5 5.9 6.4 6 5.9 6.3 7 5.9 6.0 8 6.8 6.0 9 6.8 5.8 10 6.4 6.3 11 6.5 6.3 12 6.3 6.2 13 6.1 6.5 14 6.1 6.6 15 6.0 6.3 16 6.4 6.7 17 6.2 6.5 18 6.0 6.8 19 6.1 6.6 20 6.4 6.1 21 6.0 6.1 22 6.2 6.2 23 6.1 6.0 24 6.0 6.2 25 6.1 6.7 DIFF 0.5 1.1 0.4 0.0 0.5 0.4 0.1 -0.8 -1.0 -0.1 -0.2 -0.1 0.4 0.5 0.3 0.3 0.3 0.8 0.5 -0.3 0.1 0.0 -0.1 0.2 0.6 ADV 5.3 6.8 7.3 7.3 7.2 6.5 6.8 5.0 5.8 5.5 6.5 6.3 7.0 7.7 6.8 6.8 7.1 7.0 7.2 7.5 7.8 8.2 8.3 8.4 8.9 DEMAND 13.9 14.5 14.7 14.9 14.9 14.6 14.1 12.0 14.2 13.9 13.9 13.8 14.0 14.5 16.0 15.7 15.8 15.2 15.9 16.2 15.0 16.9 17.1 16.9 17.4 Jan. 2017 Jan. 2018 Jan. 2018 0.6 Jan. 2019 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 6.1 5.9 6.0 6.3 6.0 5.7 5.6 6.2 6.4 6.5 6.2 6.7 6.9 6.9 6.7 7.0 7.1 7.2 7.2 7.3 7.2 7.1 6.9 7.2 7.3 6.7 6.9 5.8 5.8 6.0 6.7 6.4 7.0 7.2 5.9 6.0 6.2 6.0 6.3 6.5 6.0 6.1 6.3 6.4 6.5 6.0 6.2 5.9 6.0 6.4 1.0 -0.2 -0.5 0.0 1.0 0.8 0.8 0.8 -0.6 -0.2 -0.5 -0.9 -0.6 -0.2 -1.0 -1.0 -0.9 -0.8 -0.8 -1.2 -0.9 -1.0 -1.2 -0.9 8.9 9.1 9.3 9.4 9.3 9.4 9.5 9.6 9.7 9.9 9.8 9.9 10.1 10.2 10.5 10.3 9.9 10.5 10.6 10.5 11.6 10.1 10.3 10.7 10.9 17.4 17.7 17.6 18.4 18.6 17.4 18.4 17.6 16.7 18.2 18.5 19.1 19.0 19.0 19.8 19.8 20.0 20.9 19.6 19.5 18.4 19.3 19.3 19.9 20.0 Jan. 2020 Jan. 2020 49 7.3 10.9 10.8 11.1 11.2 11.6 50 51 52 53 54 55 56 57 58 59 60 61 62 63 7.4 7.5 7.0 6.8 7.4 7.3 7.3 7.2 7.5 7.5 7.5 6.4 6.5 6.5 6.2 6.8 6.9 6.5 6.9 7.0 6.8 6.8 6.5 -0.9 -0.9 -1.0 -0.8 0.0 -0.5 -0.8 -0.4 -0.2 -0.7 -0.7 -1.0 11.5 11.6 11.9 11.8 11.9 12.0 11.9 20.0 20.1 20.1 20.2 21.1 20.6 20.7 21.3 21.4 21.5 21.8 21.5 Jan. 2021 Feb. 2021 Mar. 2021 Forecasting Case Analysis MGT 3332 Spring 2021 8- Use regression to perform trend analysis on the de-seasonalized demand values. Is trend analysis suitable for this data? Find MAD and explain the Excel Regression output (trend equation. 1.1-squared, goodness of model). 9- Find the seasonally adjusted trend forecasts for January through March 2021 Important Notice: All team members must do all parts of the project and compare results. DO NOT divide up parts of the project as many parts are related. Teams must submit a copy of Word file and Excel file of their work through Blackboard Dropbox. Do not submit any file until all team members agree on the deliverables. Only one team member will submit the files. There is no second chance on submittal. 10- Perform simple linear regression analysis with ADV as the independent variable to predict demand. Write the complete equation, find MAD and explain the Excel Regression output. Make sure to use the de-seasonalized demand data for this model and all future models. The Fresh Detergent Case 11- Repeat part (11) with DIFF as the independent variable. 12- Construct multiple linear regression model with Period. AIP, DIFF, and ADV as independent variables. Formulate the equation, find MAD, and explain the output. Rank variables based on their degree of contribution to the model. Observe significant F, R- squared, and p-values and explain. Enterprise Industries produces Fresh, a brand of liquid detergent. In order to more effectively manage its inventory, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 48 sales periods. Each sales period is defined as one month. The variables are as follows: Demand = Y = demand for a large size bottle of Fresh (in 100,000) Price = the price of Fresh as offered by Ent. Industries AIP = the average industry price ADV = Ent. Industries Advertising Expenditure (in $100,000) to Promote Fresh in the sales period. DIFF = AIP - Price = the "price difference" in the sales period 1- Download the data from Course Blackboard site into Excel spreadsheet. 13- Perform multiple linear regression analysis with Period, DIFF, and ADV as independent variables. Formulate the equation and find MAD. Which variable is the most significant predictor of demand? Rank the independent variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain. 14- Use the model obtained in parts 14 and make forecasts for the following months. Make sure to seasonalize final forecasts. 2- Make time series scatter plots of all five variables (five graphs). Insert trend line, equation, and R-squared. Observe graphs and provide interpretation of results. Period Jan. Feb. Mar Price $10.00 $9.35 $9.10 2021 2021 2021 AIP $8.95 $8.60 $9.05 ADV $11.1 $11.3 $12.2 3- Construct scatter plots of Demand vs. DIFF and Demand vs. ADV. Demand vs. AIP, and Demand vs. Price. Insert fitted line, equation, and R-squared. Observe graphs and provide interpretation. Note that Demand is always on the Y axis. 15- Provide a case conclusion based on above analysis. 4- Obtain the correlation matrix for all six variables and list the variables that have strong correlation with Demand. High correlation is r> 0.70. Explain your findings in plain language. Grading Criteria Completeness/Correctness Quality of Interpretations/Analysis General Quality of Report graphs/writing 70% 20% 10% 5. Use 3-month and 6-month moving averages to predict the demand for January 2021. Find MAD for both forecasts and identify the preferred one based on each calculation. Is the moving average suitable method for forecasting for this data set? Explain your reasoning. 6- Use Exponential smoothing forecasts with alpha of 0.1, 0.2.0.5.0 0.9 to predict January 2021 demand. Identify the value of alpha that results in the lowest MAD. 7- Find the monthly seasonal indices for the demand values using Simple Average (SA) method. Find the de-seasonalized demand values dividing monthly demand by seasonal indices