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, r, r-squared, goodness of model) 9- Find the seasonally adjusted trend forecasts for March through May 2019 10-Perform simple linear regression analysis with ADV as the independent variable. 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 11-Repeat part (10) 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. 13-Perform multiple linear regression analysis with Period, DIFF, and ADV as independent variable is the most significant variables. Formulate the equation and find MAD 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 13 and make forecasts for the following months. Make sure to seasonalize final forecasts Period Year March 2019 Apr 2019 May 2019 Price $6.10 $6.30 $6.50 AIP S6.50 $6.60 $7.10 ADV S10.3 S10.7 S11.1 Month/Yr June 2016 6.15.8.35.3 5.75 5.7 6.3 0.6 7.25 5.75.7 5.6 5.850.25 7.2 5.6 5.8 0.2 6.5 5.6 5.750.156.75 4.4 15.3 16.5 16.1 7.3 15.5 15.2 Jan. 2017 13.3 13.12 13.8 14.8 15.3 16.3 17.5 10 6.2 5.9 6.1 0.2 6.5 5.9 6 0.25.5 0.1 6.25 145.756.2 0.456.9 5.75 6.1 0.356.8 5.8 6.1 0.3 6.8 5.7 6.2 0.5 7.1 16 17.1 16.8 16.5 16 15.2 15.3 5.9 16.2 17.5 18.4 9.4 5.7 6.1 0.46.8 5.8 Jan. 2018 20 23 24 5.55 5.650. 7.5 25 26 5.65 6.250.6 8.3 27 28 5.755.750 29 30 5.7 5.90.2 7.3 5.6 6.0.5 8.1 9.2 8.4 5.3 6.250.958.8 5.4 6.3 0.99.5 5.7 6.40.7 9.3 5.9 6.5 0.6 9.1 18.7 18.2 8.4 17.5 17.1 Jan. 2019 Feb. 2019 Har-19 Apr-19 May-19 32 34 35 36 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, r, r-squared, goodness of model) 9- Find the seasonally adjusted trend forecasts for March through May 2019 10-Perform simple linear regression analysis with ADV as the independent variable. 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 11-Repeat part (10) 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. 13-Perform multiple linear regression analysis with Period, DIFF, and ADV as independent variable is the most significant variables. Formulate the equation and find MAD 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 13 and make forecasts for the following months. Make sure to seasonalize final forecasts Period Year March 2019 Apr 2019 May 2019 Price $6.10 $6.30 $6.50 AIP S6.50 $6.60 $7.10 ADV S10.3 S10.7 S11.1 Month/Yr June 2016 6.15.8.35.3 5.75 5.7 6.3 0.6 7.25 5.75.7 5.6 5.850.25 7.2 5.6 5.8 0.2 6.5 5.6 5.750.156.75 4.4 15.3 16.5 16.1 7.3 15.5 15.2 Jan. 2017 13.3 13.12 13.8 14.8 15.3 16.3 17.5 10 6.2 5.9 6.1 0.2 6.5 5.9 6 0.25.5 0.1 6.25 145.756.2 0.456.9 5.75 6.1 0.356.8 5.8 6.1 0.3 6.8 5.7 6.2 0.5 7.1 16 17.1 16.8 16.5 16 15.2 15.3 5.9 16.2 17.5 18.4 9.4 5.7 6.1 0.46.8 5.8 Jan. 2018 20 23 24 5.55 5.650. 7.5 25 26 5.65 6.250.6 8.3 27 28 5.755.750 29 30 5.7 5.90.2 7.3 5.6 6.0.5 8.1 9.2 8.4 5.3 6.250.958.8 5.4 6.3 0.99.5 5.7 6.40.7 9.3 5.9 6.5 0.6 9.1 18.7 18.2 8.4 17.5 17.1 Jan. 2019 Feb. 2019 Har-19 Apr-19 May-19 32 34 35 36