Can I get a step by step process on how to answer questions $8-15$ through in excel? Forecasting Case Analysis

MGT $3332$

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. Post names of all contributing team members

on top of Word file.

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 conceming demand for Fresh over the last $33$

sales periods. Each sales period is defined as one month. The variables are as follows:

Period $=$ Time period in month

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$=$ Enterprise Industries Advertising Expenditure $($in $$100,000)$ to Promote Fresh in the

sales period.

DIFF$=$ AIP $-$ Price $=$ the "price difference" in the sales period

Download the data from Course Blackboard site into Excel spreadsheet.

Make time series scatter plots of all five variables $($five graphs$).$ Insert trend line, equation, and

R$-$squared. Observe graphs and provide interpretation.

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.

Obtain the correlation matrix for all six variables and rank the variables that have strong

correlation with Demand $($must use absolute value.$)$ Explain your findings in plain language.

Use $3-$month and $6-$month moving averages to predict the demand for October $2022.$ 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.

Use Exponential smoothing forecasts with alpha of $0\mathrm{.}1,0\mathrm{.}2,$dots,$0\mathrm{.}9$ to predict October $2022$

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 respective 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 $2022.$

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.

Repeat part $(10)$ with DIFF as the independent variable.

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.

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.

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

seasonalize final forecasts.

Provide a case conclusion based on above analysis.

Grading Criteria

Completeness$/$Correctness $70\%$

Quality of Interpretations$/$Analysis $20\%$

General Quality of Report$/$graphs$/$writing $10\%$