Session 2: Issues in linear regression

June 5 2008

Materials

• attachment:lexdecRT.R

Reading

Notes on the readings

Additional terminology

Feel free to add terms you want clarified in class:

Questions

• Q: Determining the significance of a coefficient: one-tailed or two-tailed t test?
• A: It's a two-tailed test because we cannot a-priori assume which direction the coefficient will go. I guess if one had a really strong theoretical reason

to assume one direction, you could do a one-tailed test (which is less conservative).

Suggested topics

If you have any material that you would like to cover that isn't included in the list below, please make note of it here.

Anchor(assignments)

Assignments

Upload your solutions to this page by 10pm.

In addition to the book problems, we will distribute a data set from the ongoing ngrams project.

AttachList

Anchor(Topics)

Topics

• More on outliers
  • detect outliers

    • boxplot(), scatterplots plot(), identify()BR

  • dealing with outliers

    • exclusion subset()BR robust regression (based on t-distribution): tlm() (in package hatt)BR

• Overly influential cases (can be, but don't have to be outliers)

  • lm.influence(), also library(Rcmdr)BR

• Collinearity:

  • tests: vif(), kappa(), summary of correlations between fixed effects in lmer() BR

  • countermeasures:

    • centering and/or standardizing scale()} BR

    • use of residuals resid(lm(x1 ~ x2, data)) BR

    • principal component analysis (PCA) princomp() BR

• Model evaluation: Where is the model off?

  • case-by-case: residuals(), predict()BR

  • based on predictor: residuals() against predictors, calibrate()BR

  • overall: validate()BR

• Corrections:

  • correcting for clusters (violation of assumption of independence): bootcov()BR