Assignment 4: Forecasting
Introduction
This assignment assesses your understanding of the simple linear regression model and provides you with an opportunity to apply this method to forecast data.
Instructions
Submit your answers to all questions in a Word document. Download the data sets and use Excel to solve the problems.
Question 1. Carolina Wood Products, Inc., a major manufacturer of household furniture, is interested in predicting expenditures on furniture (FURN) for the entire United States. You are going to help the company with this prediction. Download the data Download Download the databy quarter for 2007 through 2016.
Estimate the bivariate linear trend model for the data where TIME=1 for 2007, Q1 through TIME=40 for 2016, Q4.FURN = a + b(TIME)
Evaluate this model in one paragraph, with particular emphasis on its usefulness in forecasting.
Prepare a time-trend forecast of furniture and household equipment expenditures for 2017 based on the model in (b). PeriodTimeTrend Forecast2017 Q1412017 Q2422017 Q3432017 Q444
Question 2. Alexander Enterprises manufactures plastic parts for the automotive industry. Download data Download Download dataon its sales (in thousands of dollars) for 2012, Q1 through 2016, Q4. You are to forecast sales for 2017, Q1 through 2017, Q4.
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. (Attach your graph to your answer.)
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, Q14447.12017, Q24501.62017, Q34543.22017, Q44603.1
Calculate RMSE 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?