Chapter 12 - Homework Exercises-1
.doc
keyboard_arrow_up
School
George Mason University *
*We aren’t endorsed by this school
Course
515
Subject
Industrial Engineering
Date
Dec 6, 2023
Type
doc
Pages
2
Uploaded by moodymind1
Chapter 12 - Forecasting - Homework Exercises
1.
USE FILE FoodTruck. Food trucks have become a common sight on American campuses. They serve
scores of hungry students strolling through campus and looking for trendy food served fast. The owner
of a food truck collects data on the number of students he serves on weekdays on a small campus in
California.
a.
Use the 3-period moving average to make a forecast for Weekday 41.
b.
Use the 5-period moving average to make a forecast for Weekday 41.
c.
Which is the preferred technique for making the forecast based on MSE, MAD, and MAPE?
2.
USE FILE Downtown_Cafe. The manager of a trendy downtown café in Columbus, Ohio, collects
weekly data on the number of customers it serves.
a.
Use the simple exponential smoothing technique with α = 0.2 to make a forecast for Week 53.
b.
Use the simple exponential smoothing technique with α = 0.4 to make a forecast for Week 53.
c.
Which is the preferred technique for making the forecast based on MSE, MAD, and MAPE?
3.
USE FILE Vacation Vacation destinations often run on a seasonal basis, depending on the primary
activities in that location. Amanda Wang is the owner of a travel agency in Cincinnati, Ohio. She has
built a database of the number of vacation packages (Vacation) that she has sold over the last twelve
years.
a.
Estimate the linear regression models using seasonal dummy variables with and without the
trend term.
b.
Determine the preferred model and use it to forecast the quarterly number of vacation packages
sold in the first two quarters of 2020.
4.
USE FILE Weekly_Earnings1. Data on weekly earnings are collected as part of the Current Population
Survey, a nationwide sample survey of households in which respondents are asked how much each
worker usually earns.
a.
Estimate the linear and the quadratic trend models with seasonal dummy variables.
b.
Determine the preferred model and use it to forecast earnings for the first two quarters of 2018.
5.
USE FILE Tax_Revenue. The accompanying data file contains 57 months of tax revenue from medical
and retail marijuana tax and fee collections. For cross-validation, let the training and the validation sets
comprise the first 45 months and the last 12 months, respectively.
a.
Use the training set to estimate the linear, the quadratic, and the cubic trend models and
compute the resulting MSE, MAD, and MAPE for the validation set.
b.
Determine the preferred model and reestimate it with the entire data set to forecast tax revenue
for the 58th month.
6.
USE FILE Weekly_Earnings2. The accompanying data file contains quarterly data on weekly earnings
(Earnings, adjusted for inflation) in the U.S. For cross-validation, let the training and the validation sets
comprise the periods from 2010:01 to 2015:04 and 2016:01 to 2017:04, respectively.
a.
Use the training set to estimate the linear and the quadratic trend models with seasonal dummy
variables and compute the resulting MSE, MAD, and MAPE for the validation set.
b.
Determine the preferred model and reestimate it with the entire data set to forecast earnings for
the first quarter of 2018.
7.
USE FILE Vehicle_Miles. The accompanying data file lists monthly data on vehicle miles traveled in
the U.S. (in millions). For cross-validation, let the training and the validation sets comprise the periods
from Jan-12 to Dec-16 and Jan-17 to Sep-18, respectively.
a.
Use the training set to implement the Holt-Winters exponential smoothing method with additive
and multiplicative seasonality and compute the resulting MSE, MAD, and MAPE for the
validation set.
b.
Determine the preferred model and reimplement it with the entire data set to forecast vehicle
miles for the last three months of 2018.
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help