1 / 12

ISQA 451 Business Forecasting

ISQA 451 Business Forecasting. Dr. Alan Raedels C.P.M. Summer 2010 Week 3 Multivariate Regression Models. Linear Regression . Assumptions Underlying relation is linear Errors are independent Errors have constant variation F test of the beginning and ending variances

matia
Download Presentation

ISQA 451 Business Forecasting

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ISQA 451 Business Forecasting Dr. Alan Raedels C.P.M. Summer 2010 Week 3 Multivariate Regression Models

  2. Linear Regression • Assumptions • Underlying relation is linear • Errors are independent • Errors have constant variation • F test of the beginning and ending variances • Errors are normally distributed • Plot a histogram of the residuals

  3. Simple Linear Regression • Ft = β0 + β1t • β1 • β0

  4. Multiple Regression • Multicollinearity • Exists when two or more independent variables are highly correlated with each other. • Could be causal or just randomness • Use the variable which makes the most sense. • In seasonality, you need to have the base case equal one of the seasons • Timeliness of data • Evaluation of MR models • Are the signs on the coefficients reasonable? • Are the coefficients significant? • Is R2 significant?

  5. Residual Analysis • Plot a histogram of the residuals • Plot the residuals against the fitted values • Plot the residuals against the explanatory variable • Plot the residuals over time if the data are chronological

  6. Variable Transformations • 1/X • Log(X) • X1/2 • X2

  7. Standard Error of the Estimate • The Standard Error of the Estimate (SEE) measures the amount by which the actual A values differ from the estimated values F, • For large samples, we would expect about 67% of the differences (A-F) to be within 1 SEE of 0 and about 95% of these differences to be within 2 SEE of 0.

  8. Durbin – Watson Interpretation

  9. Multiple Regression • Auto- or Serial correlation • Durbin-Watson • Tests for randomness of residuals • Calculation • If DW is > upper bound there is no positive autocorrelation. • If DW is < lower bound, there is positive autocorrelation. • If DW> lower bound and less then upper bound, the results are inconclusive.

  10. Durbin – Watson Interpretation

  11. Multiple Regression Overfitting There should be at least 10 observations for each independent variable. Large F ratios With a significance level of 0.05 the F ratio should be at least 4 times the critical value.

  12. Multiple Regression • Use of dummy variables • Always use one less independent variable than the number of states you are modeling. • For example, if my data has quarterly data then I would have three dummy variables for quarters 2, 3, and 4

More Related