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Forecast and Regression Analysis

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1. The saki motorcycle dealer in Minneapolis wants to make an accurate forecast of demand for the Saki Super TXII motorcycle during the next month.
Because the manufacturer is in Japan, it is difficult to send motorycles back or reorder if the proper number is not ordered a month ahead.
From sales records, the dealer has accumulated the following data for the past year:

Month Motorcyle Sales
January 9
February 7
March 10
April 8
May 7
June 12
July 10
August 11
September 12
October 10
November 14
December 16

a) Compute a 3-month moving average forecast of demand for April thru January of the next year
b) Compute a 5-month moving average forecast for June thru January
c)Compare the two forecasts computed in (a) and (b) using MAD-which should be used next year January?

2. The chairperson of the department of management at State University wants to forecast the number of students
who will enroll in production and operations mgmt next semester-- in order to determine how many sections to schedule.
The chair has acumulated the following enrollment data for the past 8 semesters:

Semester Students enrolled
1 400
2 450
3 350
4 420
5 500
6 575
7 490
8 650

a) Compute a three semester moving average forecast for semesters 4 thru 9
b)compute the exponentially smoothed forecast for the enrollment data α = 0.20
c) compare the two forecasts using MAD-which is more accurate?

3. Whistle Stop Café is well known for its homemade ice cream, made in a small plant in back of café. People drive
all the way from Atlanta and Macon to buy the ice cream. The two women who own the cafe want to develop a forecasting model
so they can plan their ice cream production operation and determine the number of employees they need to sell ice cream in the café.
They have accumulated the following sales records for their ice cream for the past 12 quarters:

Year Quarter Ice Cream sales(gallons)
2003 1 350
2 510
3 750
4 420
2004 5 370
6 480
7 860
8 500
2005 9 450
10 550
11 820
12 570

Develop an adjusted exponential smoothing model with α = 0.50 and β = 0.50 to forecast demand and assess
its accuracy using cumulative error(e) and average error. Does there appear to be any BIAS in the forecast?

4. Aztec Industries has developed a forecasting model that was used to forecast during a 10-month period.
The forecasts and actual demand were as follows:

Month Actual Demand Forecast Demand
1 160 170
2 150 165
3 175 157
4 200 166
5 190 183
6 220 186
7 205 203
8 210 204
9 200 207
10 220 203

Measure the accuracy of the forecast by using MAD, MAPD, and cumulative error.
Does forecasting method appear to be accurate?

5. The manager of Ramona Hotel believes that how well the local Blue Sox professional baseball team is has an impact on the occupancy rate at the hotel during summer months.
Following are the number of victories for the Blue Sox( in a 162-game schedule) for the past 8 years and the hotel occupancy rates:

Year Blue Sox Wins Occupancy Rate in %
1 75 83
2 70 78
3 85 86
4 91 85
5 87 89
6 90 93
7 87 92
8 67 91

Develop a linear regression model for these data and forecast the occupancy rate for the next year if Blue Sox wins 88 games.

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Solution Summary

This solution is comprised of detailed step-by-step calculations and explanation of various Forecast Techniques like Moving Average Forecast, Exponentially Smoothed Forecast, Adjusted Exponentially Smoothed Forecast etc. and various Forecast Accuracy Measures like Forecast Error, Absolute Error, MAD, MAPD, Cumulative Error etc. in EXCEL. Comparison of these Forecast Techniques based on these Forecast Accuracy measures has also been shown. The solution also provides a detailed Regression Analysis performed in EXCEL.

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Forecasting - regression analysis

Please help. I need to clarify my answers. Thanks.

John Howard, a Mobile, Alabama, real estate developer, has devised a regression model to help determine residential housing prices in South Alabama. The model was developed using recent sales in a particular neighborhood. The price (Y) of the house is based on the size (square footage =X) of the house. The model is:
Y = 13,473 + 38.50X

The coefficient of correlation for the model is 0.65.

Using the above model, the selling price of a house that is 1,880 square feet = $_____(enter a whole number)
A 1,880-square-foot house recently sold for $98,000, which is different than the predicted value. This is a) possible or b) not possible as the forecast represents a) average or b) actual value
To make this model more realistic, additional quantitative variables that could be included in a multiple regression model are (select the choice that has all the factors that are quantifiable):
a) The age of the house, the number of bedrooms, and the size of the lot
b) The size of the lot, the number of bedrooms, and the layout of the rooms
c) The age of the house, the location of the house, and the size of the garage
For the given model, the value of the coefficient of determination = ____ (round response to 3 decimal places)

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