Practical Management Science, Loose-leaf Version
Practical Management Science, Loose-leaf Version
5th Edition
ISBN: 9781305631540
Author: WINSTON, Wayne L.; Albright, S. Christian
Publisher: Cengage Learning
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Chapter 14.7, Problem 28P

a)

Summary Introduction

To characterize: The seasonality pattern if present.

Introduction: Forecasting is a technique of predicting future events based on historical data and projecting them into the future with a mathematical model. Forecasting may be an intuitive or subjective prediction.

b)

Summary Introduction

To forecast: The series to track the seasonal pattern.

Introduction: Forecasting is a technique of predicting future events based on historical data and projecting them into the future with a mathematical model. Forecasting may be an intuitive or subjective prediction.

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Consider the following set of time series sales data for a growing company over the past 8 months:   Month Sales 1 15 2 13 3 18 4 22 5 20 6 23 7 22 8 21 Construct a time series plot.  What type of pattern exists?                        Develop a forecast for the next month using the averaging method.     Develop a forecast for the next month using the naïve last-value method.         Develop a forecast for the next month using a four-month moving average method. Use the Excel Functions SLOPE and INTERCEPT to write the linear regression prediction equation with Months as the independent variable and sales as the dependent variable. Use the prediction equation to estimate the number of sales in month 9.
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