1. Predicting Future

2. Two Approaches to Predition n Extrapolation: Use past experiences for predicting future. One looks for patterns over time. n Predictive models: Observed relationship between dependent and independent factors. n Goodness of fit: estimated by analysis of residuals.

3. Commonly Used Methods n Bivariate Regression/simple regression. u Y=a+bx n Multiple Regression. u Y=a+b 1 x 1 +b 2 x 2 +b 3 x 3 +…+d n x n n Time Series Analysis u y=a+bt

4. Bivariate Regression n y=a+bx n A ‘least square’ criteria produces the lowest residual. n It is a test of linear association and not a test of causal relationship. n Should not be used to prediction outside the bounds of the data used.

5. Multiple Regression n Y=a+b 1 x 1 +b 2 x 2 +b 3 x 3 +…+d n x n n Special Cases: n Use of Dummy Variable (0 and 1 option as in nominal scale) n Use of standardized betas to compare the importance of independent variables. n Using Multiple Regression as a screening device. n Stepwise Regression.

6. Time Series Analysis n y=a+bt n Simple trend. n Exponential Smoothing.  F (t+1) =  X t +(1-  )F t n Moving Averages. u F (t+1) = {X t - X (t-N) }/N + F t n Cycle and Seasonality Where: u F= forecast for the period. u X= actual value at a time. u N= number of values included in average, and   =exponential smoothing parameter (gamma) and 0<=  <=1