1 / 51

HRP 223 - 2008

HRP 223 - 2008. Topic 9 - Regression. Height and Resting Pulse. The spreadsheet RESTING.xls has height and pulse measures on 50 people. On average, does pulse go up or down with height?. Look before you leap!. Root MSE = Estimated standard deviation of the error in the model (eta)

axel
Download Presentation

HRP 223 - 2008

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. HRP 223 - 2008 Topic 9 - Regression

  2. Height and Resting Pulse The spreadsheet RESTING.xls has height and pulse measures on 50 people. On average, does pulse go up or down with height?

  3. Look before you leap!

  4. Root MSE = Estimated standard deviation of the error in the model (eta) Dependent Mean = Mean of the outcome CV = ratio of above * 100 In general r2 is interpreted as: .1 small effect, 3. medium effect, .5 large effect Adjusted R-square =1- ( (1- rsquare) * ((n-1)/n-m-1)) ) n=subjects m=variables It penalizes you for putting extra terms in the model. R-squared is typically reported if you have a single predictor variable. Adjusted R-square is typically reported if you have several predictors.

  5. Oxygen The next set of data looks at the relationship between oxygen inhaled and exhaled. You would hope that there would be close to a perfect relationship between the two factors.

  6. Add the library to a new flowchart. Add the SAS data set to the project.

  7. Look at the Data This is bad news…. At least it is symmetric.

  8. Simple correlation is questionable.

  9. Are the residuals about normal?

  10. Leave yourself a note on how to interpret the output. Right click on the flowchart and choose New > Note. Leave yourself some notes. Right click on the Note icon > Link Note to > Quadratic

  11. Ice cream! In this example you will predict ice cream sales based on factors like price and temperature. Start by making a library (or copy and paste the existing one) in a new flowchart. The data is in a text file. Import the data.

  12. Load the Data

  13. Add Celsius Celsius is ( (5/9) * (Fahr-32) ) 2 1

  14. Celsius is ( (5/9) * (Fahr-32) )

  15. Some people say VIF > 10 is a problem but that is arbitrary. If VIF is > 1/(1 - R-squared) then the factors are more related to other predictors than outcome.

  16. Severely Dehydrated Children

  17. A Look • Do univariate descriptive statistics. • Things look reasonable. • Do bivariate correlations. • Age and weight are correlated • Do univariate modeling. • There is a weak but statistically significant association. • Build a model with all 3 predictors and check variance inflation.

  18. A Simpler Model It explains a fair amount of the variability (45%). How can I check to make sure the model is working well and is not being driven by outliers?

  19. Outliers Images from: Statistics I: Introduction to ANOVA, Regression, and Logistic Regression Course Notes (2005) and Categorical Data Analysis Using Logistic Regression Course Notes (2005), SAS Press.

  20. First Check Residuals

  21. What is influential? • Freund and Littell SAS System for Regression 3rd edition, page 70; • Variance inflation: • vifcheck = 1 /(1 – r2) • Leverage greater than this value: • leverageCheck = 2 * (predictors + 1) / records • Covariance more extreme than: • cov1Check = 1 + 3 * (predictors+1) / records • cov1Check = 1 - 3 * (predictors+1) / records • Dfits values with absolute value bigger than: • dffitsCheck = 2 * ((predictors + 1)/records) ** .5

  22. Influence Code

More Related