Predicting Future
Two Approaches to Predition
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Extrapolation: Use past experiences for
predicting future. One looks for patterns over
time.
Predictive models: Observed relationship
between dependent and independent factors.
Goodness of fit: estimated by analysis of
residuals.
Commonly Used Methods

Bivariate Regression/simple regression.
 Y=a+bx
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Multiple Regression.
 Y=a+b1x1+b2x2+b3x3+…+dnxn

Time Series Analysis
 y=a+bt
Bivariate Regression
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y=a+bx
A ‘least square’ criteria produces the lowest
residual.
It is a test of linear association and not a test of
causal relationship.
Should not be used to prediction outside the
bounds of the data used.
Multiple Regression
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Y=a+b1x1+b2x2+b3x3+…+dnxn
Special Cases:
Use of Dummy Variable (0 and 1 option as in nominal scale)
Use of standardized betas to compare the
importance of independent variables.
Using Multiple Regression as a screening device.
Stepwise Regression.
Time Series Analysis

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y=a+bt
Simple trend.
Exponential Smoothing.
 F(t+1)

= GXt+(1-G)Ft
Moving Averages.
 F(t+1)
= {Xt - X(t-N) }/N + Ft
Cycle and Seasonality
Where:
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F= forecast for the period.
X= actual value at a time.
N= number of values included in average, and
G=exponential smoothing parameter (gamma) and 0<=G<=1