Business Management: Index Conversion and Forecasting
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1. Convert the 48 monthly data points from the data into an index. Use the 1st month in the data set as your base amount.
2. Identify which month was used as the base month.
3. Use the time series data from the index to forecast inventory for the 49th month. This forecast should incorporate linear regression methodology to arrive at the forecasted value.
4. Briefly discuss why you believe this would or would not be an accurate forecast for this data.
Format discussion consistent with APA guidelines.
DATA:
Typical Seasonal Demand for Summer Highs
Actual Demands (in units)
Month Year 1 Year 2 Year 3 Year 4 Forecast
1 18,000 45,100 59,800 35,500
2 19,800 46,530 30,740 51,250
3 15,700 22,100 47,800 34,400
4 53,600 41,350 73,890 68,000
5 83,200 46,000 60,200 68,100
6 72,900 41,800 55,200 61,100
7 55,200 39,800 32,180 62,300
8 57,350 64,100 38,600 66,500
9 15,400 47,600 25,020 31,400
10 27,700 43,050 51,300 36,500
11 21,400 39,300 31,790 16,800
12 17,100 10,300 31,100 18,900
Avg.
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Solution Summary
This solution provides a detailed a computation of the given business problem.
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** Please see the attached file for the complete solution response **
Requirement 1 Requirement 2
Data Index Month 1 of Year 1 is the base month
Y1, M1 18,000
Y1, M2 19,800 1.10 Requirement 3
Y1, M3 15,700 0.87
Y1, M4 53,600 2.98
Y1, M5 83,200 4.62
Y1, M6 72,900 4.05
Y1, M7 55,200 3.07
Y1, M8 57,350 3.19
Y1, M9 15,400 0.86
Y1, M10 27,700 1.54
Y1, M11 21,400 1.19
Y1, ...
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