- Submitted By: ihgonzalez
- Date Submitted: 05/05/2016 7:39 PM
- Category: Business
- Words: 2859
- Page: 12

Chapter 3

Forecasting II

3-1

Techniques for Trend

• Linear trend equation

– Linear Regression

• Non-linear trends

– Parabolic trend equation

– Exponential trend equation

– Growth curve trend equation

3-2

Simple Linear Regression

• Regression - a technique for fitting a line to a

set of data points

– Simple linear regression - the simplest form of

regression that involves a linear relationship between

two variables

• The object of simple linear regression is to obtain an

equation of a straight line that minimizes the sum of

squared vertical deviations from the line (i.e., the least

squares criterion)

3-3

Least Squares Line

y c a bx

where

y c Predicted (dependent) variable

x Predicted (independent) variable

b Slope of the line

a Value of y c when x 0 (i.e., the height of the line at the y intercept)

and

n xy

x y

b

n x x

y b x

a

or y bx

2

2

n

where

n Number of paired observations

3-4

Simple Linear Regression Assumptions

1. Variations around the line are random

2. Devaiations around the average value (the

line) should be normally distributed

3. Predictions are made only within the range of

observed values

3-5

The implication of the regression models in

forecasting is called the The Time-series

Approach

3-6

Time Series Forecasting Methods

• Static methods

– Assumes that the estimates of level, trend, and

seasonality within systematic components do not vary

as new demand is observed

– Estimate the value of parameters and use these value

for future forecasts

• Adaptive forecasting

– Estimates of level, trend, and seasonality are updated

after each demand observation

– Moving average, exponential smoothing

3-7

The Time Series Approach: Static

Methods

• A simple data plot can reveal the existence and

nature of a trend

Simply replace x by t

• Linear trend equation

Ft a bt

where

Ft...