Alexander Enterprises manufactures plastic parts for the automotive industry Its sales (in thousands of dollars) for 2012Q1 through 2016Q4 are as follows Period Sales Mar 12 3,816 5 Jun 12 3,916 7 Sep 12 3,978 8 Dec 12 4,046 6 Mar 13 4,119 1 Jun 13 4,169 4 Sep 13 4,193 0 Dec 13 4,216 4 Mar 14 4,238 1 Jun 14 4,270 5 Sep 14 4,321 8 Dec 14 4,349 5 Mar 15 4,406 4 Jun 15 4,394 6 Sep 15 4,422 3 Dec 15 4,430 8 Mar 16 4,463 9 Jun 16 4,517 8 Sep 16 4,563 6 Dec 16 4,633 0 Mar 17 Jun 17 Sep 17 Dec 17 Begin by preparing a time series plot of the data set Does it appear from this plot that a linear trend model will be appropriate Explain Use a bivariate linear regression trend model to estimate this trend equation SALES a b(TIME) Is the sign of b what you would expect Is b significantly different from zero What is the coefficient of determination for this model Explain your answers Based on this model, make a trend forecast of sales (SALESFT) for the four quarters of 2017 The actual sales (SALESA) for the four quarters are provided in the table 2017, Q1 4667 1 2017, Q2 4710 3 2017, Q3 4738 7 2017, Q4 4789 0 Calculate the MAPE for this forecast model in the historical period (2012, Q1 2016, Q4) as well as for the forecast horizon (2017, Q1 2017, Q4) Which of these measures accuracy and which measures fit

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Alexander Enterprises manufactures plastic parts for the automotive industry. Its sales (in thousands of dollars) for 2012Q1 through 2016Q4 are as follows: Period Sales Mar-12

Alexander Enterprises manufactures plastic parts for the automotive industry. Its sales (in thousands of dollars) for 2012Q1 through 2016Q4 are as follows:

Period	Sales
Mar-12	3,816.5
Jun-12	3,916.7
Sep-12	3,978.8
Dec-12	4,046.6
Mar-13	4,119.1
Jun-13	4,169.4
Sep-13	4,193.0
Dec-13	4,216.4
Mar-14	4,238.1
Jun-14	4,270.5
Sep-14	4,321.8
Dec-14	4,349.5
Mar-15	4,406.4
Jun-15	4,394.6
Sep-15	4,422.3
Dec-15	4,430.8
Mar-16	4,463.9
Jun-16	4,517.8
Sep-16	4,563.6
Dec-16	4,633.0
Mar-17
Jun-17
Sep-17
Dec-17

Begin by preparing a time series plot of the data set. Does it appear from this plot that a linear trend model will be appropriate? Explain.
Use a bivariate linear regression trend model to estimate this trend equation: SALES = a + b(TIME) Is the sign of b what you would expect? Is b significantly different from zero? What is the coefficient of determination for this model? Explain your answers.
Based on this model, make a trend forecast of sales (SALESFT) for the four quarters of 2017.
The actual sales (SALESA) for the four quarters are provided in the table.

2017, Q1	4667.1
2017, Q2	4710.3
2017, Q3	4738.7
2017, Q4	4789.0

Calculate the MAPE for this forecast model in the historical period (2012, Q1-2016, Q4) as well as for the forecast horizon (2017, Q1-2017, Q4). Which of these measures accuracy and which measures fit?