Quantitative Methods Case Study
Assist with quantitative methods case study. website to assist your answer as you read case study details.
https://www.bls.gov/bdm/entrepreneurship/entrepreneurship.htm
>Summary
ing
: Submodel = 11; Problem size @ 5 by 3
Analysis
Forecast Error 2 2 4 .5
3.5 %
39 6 6 1.5 2.5 Average Bias MAD MAPE Forecasting Time Value Enter the past demands in the data area Num pds 3 Period Demand Forecast Error Absolute Squared Abs Pct Err 2.6666666667 5.6666666667 Bias MAD MSE MAPE Period 7 44 Average after forecast period 6 Forecasting Enter the past demands in the data area Forecasts and Error Analysis Period Demand Forecast Error Absolute Squared Abs Pct Err 2.4 2.32 6.376 Bias MAD MSE MAPE 9.4632 Period 7 44 Average after forecast period 6 Forecasting Enter alpha (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast. Alpha 0.3 Forecasts and Error Analysis Error Absolute Squared Abs Pct Err 1.98 3.4498 5.473798 06.26% Bias MAD MSE MAPE Forecasting Enter alpha and beta (between 0 and 1), enter the past demands in the shaded column then enter a starting forecast. If the starting forecast is not in the first period then delete the error analysis for all rows above the starting forecast. Forecasting Case Study: New Business Planning
Important Note: Students must access the “Entrepreneurship and the U.S. Economy” page of the Bureau of Labor Statistics website in order to complete this assignment.
Scenario
The generation of new business start-up is vital to the growth of the economy as it builds new jobs and creates new opportunities for the community. The Bureau of Labor Statistics tracks new business development and jobs created on the website for the United States Department of Labor. You have been tasked with forecasting economic growth and decline patterns for new businesses in the United States.
Forecasting
Access the “Entrepreneurship and the U.S. Economy” page of the Bureau of Labor Statistics website. Under the “Business establishment age” heading, the first chart reviews new businesses less than 1 year old during the March 1994 to March 2 015 period. Click on the [Chart data] link below the chart: Once the chart data window opens, you will see the number of establishments that are less than 1 year old for each year during this period: Using the five most recent years and the “Forecasting Template” spreadsheet provided, complete the forecasts for the next two periods and provide updated Totals and Average Bias, median absolute deviation (MAD), mean squared error (MSE), and mean absolute percentage error (MAPE) for all four charts. Provide a Summary Page in Excel with a 500-750 word report on the analysis completed by the forecasting models. Include review of error, recommendations on the best forecasting model to use, and analysis of the business trend data for new business startup in the United States. 2
2
2 period moving average
Forecast
Moving averages – 2 period moving average
Num pds
3
Data
Elissa Torres:
Forecasting
Forecasts and
Error
Period
Demand
Absolute
Squared
Abs Pct Err
Period 1
38
Period 2
4
0
Period 3
41
39
04.88%
Period 4
37
40
–
3.5
12.25
09.4
6
Period 5
45
36
13.33%
Total
4.5
1
1.5
52.25
27.67%
Average
3.8333333333
17.4166666667
09.22%
before forecast
Bias
MAD
M
SE
MAPE
Period 6
50
47.5
2.5
6.25
0
5.00%
Period 7
44
after forecast period 6
MSE
Demand 38 40 41 37 45 Forecast 39 40.5 39 3 period moving average
Forecasting
Moving averages – 3 period moving average
Data
Elissa Torres: Forecasting: Submodel = 11; Problem size @ 5 by 3
Forecasts and Error Analysis
Period 1 38
Period 2 40 Period 3 41
Period 4 37
39.6666666667
–
2.6666666667
7.1111111111
07.21%
Period 5 45
39.3333333333
5.6666666667
32.1111111111
12.59%
Total 3
8.3333333333
39.2222222222
19.80%
Average 1.5
4.1666666667
19.6111111111
09.90%
Period 6 50 44 6 6 36
12.00%
Bias MAD MSE MAPE
Demand 38 40 41 37 45 Forecast 39.666666666666664 39.333333333333336 Time
ValueExponential Smoothing
Forecasting
Exponential smoothing
Alpha
0.3
Data
Elissa Torres: Forecasting: Submodel = 13; Problem size @ 5 by 1
Period 1 38 38 0 0 0
0.00%
Period 2 40 38 2 2 4 5.00%
Period 3 41
38.6
2.4
5.76
5.85%
Period 4 37
39.32
–
2.32
5.3824
6.27%
Period 5 45
38.624
6.376
40.653376
14.17%
Total
8.456
13.096
55.795776
31.29%
Average
1.6912
2.6192
11.1591552
06.26%
Before forecast
SE
4.3126084914
Period 6 50
40.5368
9.4632
89.55215424
18.93%
Bias MAD MSE MAPE
38 40 41 37 45 38 38 38.6 39.32 38.624000000000002 Time
ValueTrend Adj Exp Smoothing
Forecasting
Trend adjusted exponential smoothing
Beta
0.7
Data
Elissa Torres: Forecasting: Submodel = 14; Problem size @ 5 by 1
Period Demand
Smoothed Forecast, Ft
Smoothed Trend, Tt
Forecast Including Trend, FITt
Period 1 38 38 38 0 0 0
00.00%
Period 2 40 38 0 38 2 2 4
05.00%
Period 3 41 38.6
0.42
39.02
1.98
3.9204
04.83%
Period 4 37
39.614
0.8358
40.4498
–
3.4498
11.90112004
09.32%
Period 5 45
39.41486
0.111342
39.526202
5.473798
29.9624645448
0.1216399556
Next period
41.1683414
1.26083958
42.42918098
Total
6.003998
12.903598
49.7839845848
31.32%
41.1683414 Average
1.2007996
2.5807196
9.956796917
Bias MAD MSE MAPE
SE
4.0736545666
Next period
42.050929106
0.6178113942
42.6687405002
Total
After forecast
Average
SE 0
Demand 38 40 41 37 45 Smoothed Forecast, Ft 38 38 38.599999999999994 39.61399999999999 39.41485999999999 Time
Value