The Fresh Detergent Case

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 33 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

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.

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: Demand is always on the Y-axis.

Obtain the correlation matrix for all six variables and list the variables that have strong correlation with Demand. High correlation is r > 0.50. Explain your findings in plain language.

Use 3-month and 6-month moving averages to predict demand for October 2019. Find MAD for both forecasts and identify the preferred forecast based on each calculation. Is the moving average suitable method for forecasting for this data set? Explain your reasoning.

Use Exponential smoothing forecasts with alpha of 0.1, 0.2,..., 0.9 to predict October 2019 demand. Identify the value of alpha that results in the lowest MAD.

Find the monthly seasonal indices for the demand values using Simple Average (SA) method. Find the de-seasonalized demand values by dividing monthly demand by seasonal indices.

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).

Find the seasonally adjusted trend forecasts for October through December 2019.

Find the best simple linear regression model among the five variables: Period, Price, Adv., AIP, and DIFF. 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.

Find the best two variables multiple linear regression model among the five variables: Period, Price, Adv., AIP, and DIFF. Write the complete equation, find MAD and explain the Excel Regression output. Rank independent variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.

Find the best three variables multiple linear regression model among the five variables: Period, Price, Adv., and DIFF. Write the complete equation, find MAD and explain the Excel Regression output. Rank independent variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.

Find the best four variables multiple linear regression model among the five variables: Period, Price, Adv., AIP, and DIFF. Write the complete equation, find MAD and explain the Excel Regression output. Rank independent variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.

Use the model obtained in parts 13 and make forecasts for the following months. Make sure to seasonalize final forecasts.

Period Price AIP ADV

Oct. 2019 $7.30 $7.65 $10.1

Nov. 2019 $7.45 $7.70 $10.3

Dec. 2019 $7.80 $7.95 $10.5

Data Set to use is below

Month/Year.	Period	Price	AIP	DIFF	ADV	Demand
Jan.2017	1	6.1	5.8	-.3	5.3	12.4
	2	5.75	6	.25	6.75	13.3
	3	5.7	6.3	.6	7.25	14.5
	4	5.7	5.7	0	7.3	14.1
	5	5.6	5.85	.25	7.2	14
	6	5.6	5.8	.2	6.5	13.5
	7	5.6	5.75	.15	6.75	13.2
	8	6.3	5.85	-.45	6.89	11.9
	9	6.4	5.65	-.75	5.8	11.3
	10	6.2	6	-.2	5.5	11.12
	11	5.9	6.1	.2	6.5	11.8
	12	5.9	6	.1	6.25	12.8
Jan 2018	13	5.7	6.1	.4	7	13.3
	14	5.75	6.2	.45	6.9	14.3
	15	5.75	6.1	.35	6.8	15.5
	16	5.8	6.1	.3	6.8	15.4
	17	5.7	6.2	.5	7.1	15.1
	18	5.8	6.3	.5	7	14.8
	19	5.7	6.1	.4	7.2	14.5
	20	5.8	5.75	-.05	7.5	14
	21	5.8	5.75	-.05	7.8	13.8
	22	5.75	5.65	-.1	8.2	13.8
	23	5.7	5.9	.2	8.3	14.1
	24	5.55	5.65	.1	8.4	15.4
	25	5.6	6.1	.5	8.9	15.8
	26	5.65	6.25	.6	9.1	16.5
	27	5.7	5.65	-.05	9.3	17.2
	28	5.75	5.75	0	9.4	17.6
	29	5.8	5.85	.05	9.3	17.3
	30	5.3	6.25	.95	9.4	17.1
	31	5.4	6.3	.9	9.5	16.8
	32	5.7	6.4	.7	9.6	16
Sep 2019	33	5.9	6.5	.6	9.7	15.9
Oct 2019	34
Nov 2019	35
Dec 2018	36

Attach Excel file showing work.