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Please use attached data in CIDR excel sheet to answer below questions. I’ve added each tab for each part to be answered(CIDR-A,CIDR-B…)

1. The United

States Census Bureau releases monthly

and annual retail trade reports

that reflect retail economic activities in the United

States. More than thirty years of

time-series data can be downloaded at http://www.census.gov/retail/. Monthly retail sales in Food Services and

Drinking Places (in millions of

dollars), from January 1992 to December 2021, are downloaded and are available

in Exam1Data.xlsx. You are now trying to develop forecasts

for the Year 2022. For parts a and

b, do not deseasonalize the data

yet.

a. Use the single exponential smoothing1 forecasting method to forecast

the retail sales. Start your

exponential smoothing with 15,700 (millions of dollars) for January, 1992. Calculate

the mean absolute

percentage error (MAPE)

to determine the best alpha.

(You are not required to use Excel Solver, but you

may be able to find the best alpha by setting the objective to “minimize” MAPE/MAD/MSE and changing variable(s) to

the alpha value.) What is your

forecast for July (Month 7), 2022? Finally, report MAD and MSE.

b. You may have realized that some months have high demand while others have low demand. Calculate the seasonal index for

each month, deseasonalize the retail sales data, and insert a new column that shows the deseasonalized sales. Plot

the deseasonalized sales to see if there is an increasing or

decreasing trend.

c. Now suppose you consider using linear regression for your forecast. What would be good independent variable(s) for the dependent variable, retail

sales (second column)? Check out the values of Significance of F,

p-value, and adjusted R Square, and then provide their implications for your regression model. Provide MAD, MAPE, and

MSE and compare them with those you find in part a. Finally, calculate your forecast for July,

2022.

d. Find your best forecasting method and provide forecasts for the months of Year

2022 (January to December). You may want to follow “deseasonalize –

apply forecasting method – re-seasonalize”

technique and/or try a different method that takes into account of seasonality and trend in the data. Everyone will compete to find the best forecasting model although what you get in part c may turn out to be

the best. Hence, you need to report

the MAD, MAPE and MSE of your best

method for the periods from January 1992 to December 2021. Make sure

you briefly describe the method

and steps you followed.

e. When you tried causal forecasting methods such as part a, you may have realized challenges in getting an accurate forecast

during the recession of 2007-2009 and during the latest coronavirus pandemic. Discuss one or two ways to improve

your forecasts if you decide not to

use time-series forecasting methods.