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    Forecasting using Moving Average and Linear Regression

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    2. The manger of the Carpet City outlet needs to make an accurate forecast of the demand of Soft Shag carpet (its biggest seller). If the manger does not order enough carpet from the carpet mill, customer will their carpet from one of Carpet City's many competitors. The manager has collected the following demand data for the past 8 months:

    Demand for Soft Shag
    Month Carpet (1,000 yd.)
    1 8
    2 12
    3 7
    4 9
    5 15
    6 11
    7 10
    8 12

    a. Compute a 3- month moving average forecast for months 4 through 9.
    b. Compute a weighed 3- month moving average forecast for months 4 through 9. Assign weights of .55, .33, and .12 to the months in sequence, starting with the most recent month.
    c. Compare the two forecasts by using MAD. Which forecast appears to be more accurate?

    6. The manager of the Petroco Service station wants to forecast the demand for unleaded gasoline next month so that the proper number of gallons can be ordered from the distributor. The owner has accumulated the following data on demand for unleaded gasoline from sales during the past 10 months:
    Month Gasoline Demand (gal)
    October 800
    November 725
    December 630
    January 500
    February 645
    March 690
    April 730
    May 810
    June 1200
    July 980

    a. Compute and exponentially smoothed forecast, using on α value of .30.
    b. Compute an adjusted exponentially smoothed forecast (with α =.30 and β=.20).
    c. Compare the two forecast by using MAPD and indicate which seems to be more accurate.

    15. The Cat Creek Mining and ship coal. It has experienced the following demand for coal during the past 8 years:

    Year Coal Sales (tons) 1 4260
    2 4510
    3 4050
    4 3720
    5 3900
    6 3470
    7 2890
    8 3100

    Develop a forecasting model that you believe would provide the company with relatively accurate forecast for the next and indicate the forecasted shipping space
    required for the next 3 months.

    23. The manager of the Ramona Inn Hotel near Cloverleaf Stadium that how well the local Blue Sox professional baseball team is playing has impact on the occupancy rate at the hotel during the summers. 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 (%)
    1 75 83
    2 70 78
    3 85 86
    4 91 85
    5 87 89
    6 90 93
    7 87 92
    8 67 61
    Develop a linear regression model for these data and forecast the occupancy rate for next year if the Blue Sox win 88 games.

    25. The manager of Gilley's Ice Cream Parlor needs an accurate forecast of the demand for ice cream. The store order ice cream from a distributor a week ahead; if the store orders too little, it loses business, and if it orders too much, the extra must be thrown away. The manager believes that a major determinant of ice cream sales is temperature (i.e., the hotter the weather, the more ice cream people buy). Using an almanac, the manger has determined the average daytime temperature for 10 weeks, selected at random, and from store records he has determined the ice cream consumption for the same 10 weeks. The data are summarized as follows:
    Week Average Temperature Ice Cream Sold
    (Degrees) (gal.)
    1 73 110
    2 65 95
    3 81 135
    4 90 160
    5 75 97
    6 77 105
    7 82 120
    8 93 175
    9 86 140
    10 79 121
    a. Develop linear regression model for these data and forecast the ice cream consumption if the average weekly daytime temperature is expected to be 85 degrees.

    b. Determine the strength of the linear relationship between temperature and ice cream consumption by using correlation.

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    Solution Summary

    This posting provides solution to forecasting problem using moving average, weighted moving average and linear regression techniques for problems of Carpet City, Ramona Inn Hotel, Blue Sox Professional Baseball team and Gilley's Ice Cream Parlor.