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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

2 period moving average

ForecastingMoving averages – 2 period moving average
Num pds3
Data
Elissa Torres: Forecasting: Submodel = 11; Problem size @ 5 by 3
Forecasts and Error Analysis
PeriodDemandForecastErrorAbsoluteSquaredAbs Pct Err
Period 138
Period 240
Period 3413922404.88%
Period 43740.5-3.53.512.2509.46%
Period 54539663613.33%
Total4.511.552.2527.67%
Average1.53.833333333317.416666666709.22%before forecast
BiasMADMSEMAPE
Period 65047.52.52.56.2505.00%
Period 744Averageafter forecast period 6
BiasMADMSEMAPE

ForecastingDemand 38 40 41 37 45 Forecast 39 40.5 39

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Time

Value

ForecastingDemand 38 40 41 37 45 Forecast 39 40.5 39

Time

Value

Enter the past demands in the data area

ForecastingDemand 38 40 41 37 45 Forecast 39 40.5 39

Time

Value

3 period moving average

ForecastingMoving averages – 3 period moving average
Num pds3
Data
Elissa Torres: Forecasting: Submodel = 11; Problem size @ 5 by 3
Forecasts and Error Analysis
PeriodDemandForecastErrorAbsoluteSquaredAbs Pct Err
Period 138
Period 240
Period 341
Period 43739.6666666667-2.66666666672.66666666677.111111111107.21%
Period 54539.33333333335.66666666675.666666666732.111111111112.59%
Total38.333333333339.222222222219.80%
Average1.54.166666666719.611111111109.90%
BiasMADMSEMAPE
Period 65044663612.00%
Period 744Averageafter forecast period 6
BiasMADMSEMAPE

ForecastingDemand 38 40 41 37 45 Forecast 39.666666666666664 39.333333333333336

Time

Value

ForecastingDemand 38 40 41 37 45 Forecast 39.666666666666664 39.333333333333336

Time

Value

Enter the past demands in the data area

ForecastingDemand 38 40 41 37 45 Forecast 39.666666666666664 39.333333333333336

Time

Value

Exponential Smoothing

ForecastingExponential smoothing
Alpha0.3
Data
Elissa Torres: Forecasting: Submodel = 13; Problem size @ 5 by 1
Forecasts and Error Analysis
PeriodDemandForecastErrorAbsoluteSquaredAbs Pct Err
Period 138380000.00%
Period 240382245.00%
Period 34138.62.42.45.765.85%
Period 43739.32-2.322.325.38246.27%
Period 54538.6246.3766.37640.65337614.17%
Total8.45613.09655.79577631.29%
Average1.69122.619211.159155206.26%Before forecast
BiasMADMSEMAPE
SE4.3126084914
Period 65040.53689.46329.463289.5521542418.93%
Period 744Averageafter forecast period 6
BiasMADMSEMAPE

Forecasting38 40 41 37 45 38 38 38.6 39.32 38.624000000000002

Time

Value

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.

Forecasting38 40 41 37 45 38 38 38.6 39.32 38.624000000000002

Time

Value

Trend Adj Exp Smoothing

ForecastingTrend adjusted exponential smoothing
Alpha0.3
Beta0.7
Data
Elissa Torres: Forecasting: Submodel = 14; Problem size @ 5 by 1
Forecasts and Error Analysis
PeriodDemandSmoothed Forecast, FtSmoothed Trend, TtForecast Including Trend, FITtErrorAbsoluteSquaredAbs Pct Err
Period 138383800000.00%
Period 2403803822405.00%
Period 34138.60.4239.021.981.983.920404.83%
Period 43739.6140.835840.4498-3.44983.449811.9011200409.32%
Period 54539.414860.11134239.5262025.4737985.47379829.96246454480.1216399556
Next period41.16834141.2608395842.42918098
Total6.00399812.90359849.783984584831.32%
41.1683414Average1.20079962.58071969.95679691706.26%
BiasMADMSEMAPE
SE4.0736545666
Next period42.0509291060.617811394242.6687405002
TotalAfter forecast
Average
BiasMADMSEMAPE
SE0

ForecastingDemand 38 40 41 37 45 Smoothed Forecast, Ft 38 38 38.599999999999994 39.61399999999999 39.41485999999999

Time

Value

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.

ForecastingDemand 38 40 41 37 45 Smoothed Forecast, Ft 38 38 38.599999999999994 39.61399999999999 39.41485999999999

Time

Value

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 2015 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

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