I have most of this, but the two that I’m stuck on are below. I left out the par

Question

I have most of this, but the two that I’m stuck on are below. I left out the parts that are finished, but I don’t think any of it is needed for these two parts:

The OFLT Trucking Company needs to determine whether they should increase their fleet size or not. Since their ultimate objective is to satisfy the certain percentage of nationwide transportation demand, they obtained the following data representing total transportation demand in billions of dollars

Use regression method to forecast the demand for 2017-2020.

Determine the most accurate forecast method and estimate the demand for 2017.

Table 1. Demand for transportation Year Total transporta 1980 1985 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 ation demand 332.6 486.4 598.3 595.8 638.0 682.7 742.0 772.1 828.1 896.4 931.1 1,009.4 1,059.9 1,052.6 1.075.4 1,097.3 1,160.5 1.248.2 1,312.4 .391.1 .359.7 1,175.6 1.320.2 1,467.1 560.5 1,608.0 .661.1 1,630.6 1690.5

Explanation / Answer

Use Data AnalysisToolPak in excel and regress with y values as Demand and x values as year.

R square is 0.97

The equation is Demand = -79571 + 40.29*Year

Therefore input the year values in the equation. to get the forecast demand for 2017-2020

The other forecast methods would be not to use year variable but

a) generally the growth every year and using it to forecast.

b) another method is using smoothing technique over the growth. This can be done by exponential smoothing forecasting.

C) another method is to use the moving average method.

Demonstrated the 2 months, 3 months, 5 months moving average method. As you can see the average Mean absolute percentage error is low for the 2 months moving average method. For example In 2 months moving average method we average the previous 2 months demand to get the forecast for this month.

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -79571 2689.924 -29.5811         0.000 -85090.2 -74051.7 -85090.2 -74051.7 Year         40.29           1.34         29.98         0.000         37.54         43.05         37.54         43.05

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