Lab: Trend Projection is a time-series forecasting model that examines historical data. The quantitative goal is to fit the data trend line to a series of historical data points. The qualitative goal is to use the data as a means to project medium to long-term forecast plans. In Industry, this is the method to reduce the SSE, the Sum of SquaresErrors.

Given the following data, create a Trend Projection with the least Squares Analysis. Compute the slope and intercept of the line. Use the Slope and Intercept data to project demand for 2018. Hint:You need to change the Data to reflect the more recent years:

Boston Data Servers Inc.: DemandForecast

Forecast Year	Data Server Demand
2011	91
2012	101
2013	85
2014	99
2015	147
2016	180
2017	137

1. What is the equation of the line: Slope and intercept?

2. Project the demand for 2018, using x=8 (since we are forecasting for the next period, period 8)

3. Boston Data Servers received a call, confirming that Amazon is moving to Boston. They need to revise the new demand for 2017 to 300 units. Now, adjust the demand for 2017 in your Excel sheet. Observe the changes.

4. What is the new equation of the line: slope and intercept? In other words, what is the demand for the next period?

5. To evaluate the model, we compare the historical demand and the trend line. Please comment on your personal findings.

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A B 1 CHAPTER 4: Example 8 2 3 4 5 6 7 8 Date 9 Period PHANH978987 10 11 12 13 14 15 16 17 18 Intercept 19 Slope 20 Forecasting Regression/Trend analysis If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y. 23 24 25 26 27 28 29 30 31 32 33 2001 2002 2003 2004 2005 2006 2007 21 Next period 22 Demand (y) 74 79 80 90 105 142 122 56.71428571 10.53571429 141 Period (x) 1 2 3 4 5 6 7 E 8 F Forecasts and Error Analysis Forecast Error Total Average Bias G Absolute 6.75 6.75 Squared 67.25 45.5625 77.7857143 1.21428571 1.21428571 1.4744898 88.3214286 -8.3214286 8.32142857 69.2461735 98.8571429 -8.8571429 8.85714286 78.4489796 109.392857 -4.3928571 4.39285714 19.2971939 119.928571 22.0714286 22.0714286 487.147959 130.464286 -8.4642857 8.46428571 71.6441327 -4.263E-14 60.0714286 772.821429 -6.09E-15 8.58163265 110.403061 MAD SE Correlation H MSE 150 100 50 0 0 Abs Pct Err 09.12% 01.54% 10.40% 09.84% 04.18% 15.54% 06.94% 57.57% 08.22% MAPE 1 2 J Regression Series1 K 3 4 5 Linear (Series1) L 6 7 M 8 N A B 1 CHAPTER 4: Example 8 2 3 4 5 6 7 8 Date 9 Period PHANH978987 10 11 12 13 14 15 16 17 18 Intercept 19 Slope 20 Forecasting Regression/Trend analysis If this is trend analysis then simply enter the past demands in the demand column. If this is causal regression then enter the y,x pairs with y first and enter a new value of x at the bottom in order to forecast y. 23 24 25 26 27 28 29 30 31 32 33 2001 2002 2003 2004 2005 2006 2007 21 Next period 22 Demand (y) 74 79 80 90 105 142 122 56.71428571 10.53571429 141 Period (x) 1 2 3 4 5 6 7 E 8 F Forecasts and Error Analysis Forecast Error Total Average Bias G Absolute 6.75 6.75 Squared 67.25 45.5625 77.7857143 1.21428571 1.21428571 1.4744898 88.3214286 -8.3214286 8.32142857 69.2461735 98.8571429 -8.8571429 8.85714286 78.4489796 109.392857 -4.3928571 4.39285714 19.2971939 119.928571 22.0714286 22.0714286 487.147959 130.464286 -8.4642857 8.46428571 71.6441327 -4.263E-14 60.0714286 772.821429 -6.09E-15 8.58163265 110.403061 MAD SE Correlation H MSE 150 100 50 0 0 Abs Pct Err 09.12% 01.54% 10.40% 09.84% 04.18% 15.54% 06.94% 57.57% 08.22% MAPE 1 2 J Regression Series1 K 3 4 5 Linear (Series1) L 6 7 M 8 N

Answer rating: 100% (QA)

1 What is the equation of the line Slope and intercept The equation of the line is y 128571x 644286 ... View the full answer