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Lecture slides stats1.13.l20.air

  1. 1. Statistics One Lecture 20 Binary Logistic Regression 1
  2. 2. Two segments •  Overview •  Example 2
  3. 3. Lecture 20 ~ Segment 1 Binary Logistic Regression Overview 3
  4. 4. Binary logistic regression •  Appropriate when predicting a binary categorical outcome variable from a set of predictor variables that may be continuous and/or categorical –  Same logic as multiple regression but outcome variable is categorical and binary
  5. 5. Binary logistic regression •  When outcome has two levels –  Binary logistic regression •  When outcome has multiple levels –  Multinomial regression
  6. 6. Multiple regression •  Ŷ= B0 + Σ(BkXk) Ŷ = predicted value on the outcome variable Y B0 = predicted value on Y when all X = 0 Xk = predictor variables Bk = unstandardized regression coefficients (Y – Ŷ) = residual (prediction error) k = the number of predictor variables 6
  7. 7. Binary logistic regression •  ln(Ŷ / (1 - Ŷ)) = B0 + Σ(BkXk) Ŷ = predicted value on the outcome variable Y B0 = predicted value on Y when all X = 0 Xk = predictor variables Bk = unstandardized regression coefficients (Y – Ŷ) = residual (prediction error) k = the number of predictor variables 7
  8. 8. Binary logistic regression •  Why ln(Ŷ / (1 – Ŷ))? •  Predicted score must fall between 0 and 1 8
  9. 9. Binary logistic regression
  10. 10. Binary logistic regression •  Why not P(outcome) = B0 + Σ(BkXk) ??? •  There is no guarantee that the linear combination of predictors will produce a score between 0 and 1 •  A transformation is therefore applied 10
  11. 11. Binary logistic regression •  Odds = P(outcome) / (1 – P(outcome)) •  For example, what are the odds a flipped coin will land heads? Odds = .5 / .5 = 1 •  Then take the natural log of the odds, which is called the log-odds or logit •  Logit = ln(P(outcome) / (1 – P(outcome)) •  Logit = ln(Ŷ / (1 – Ŷ)) 11
  12. 12. Binary logistic regression •  Logit = ln(Ŷ / (1 – Ŷ)) •  Ŷ = P(outcome)
  13. 13. Binary logistic regression •  P(outcome) = odds / (1 + odds) •  Odds = P(outcome) / P(~outcome) •  For example, •  If P = .50 then Odds = 1 and Logit = 0
  14. 14. Binary logistic regression •  Example •  Outcome variable = Faculty Promotion to tenure •  Predictor variable = Publications (Pubs) •  Logit(Promotion) = B0 + B1(Pubs) •  Logit(Promotion) = 0.00 + .39(Pubs) •  For every one unit increase in Pubs, the Logit increases .39
  15. 15. Binary logistic regression •  Logit = ln(P(outcome) / (1 – P(outcome)) •  Odds = P(outcome) / (1 – P(outcome)) •  Logit = .39 translates to an odds ratio of 1.48 –  This means that the odds of promotion are multiplied by 1.48 for each increment in Pubs
  16. 16. Binary logistic regression •  Thus, if the odds of Promotion with 16 publications is 1.27 then the Odds of Promotion with 17 publications is 1.27*1.48 = 1.88 •  This can also be presented in terms of probability •  Pubs = 17 means P(Promotion) = .65 because P(Promotion) = Odds / (1 + Odds) = 1.88/2.88 = .65
  17. 17. Binary logistic regression •  Hypothesis tests •  Is an individual predictor variable significant? •  Is the overall model significant? •  Is Model A significantly better than Model B?
  18. 18. Binary logistic regression •  To test each predictor variable •  Regression coefficient •  Odds ratio •  Wald test •  Tests the model vs. the model without the predictor
  19. 19. Binary logistic regression •  To test the overall model •  Compare the chi-square for the model to the chi-square of a model with no predictors (the null model) •  And/or compare multiple models •  Also, does the model classify cases correctly?
  20. 20. Segment summary •  Binary logistic regression is appropriate when predicting a binary categorical outcome variable from a set of predictor variables that may be continuous and/or categorical
  21. 21. Segment summary •  Main components of the output are –  Regression coefficients –  Odds ratios –  Wald tests –  Model chi-square –  Classification success
  22. 22. END SEGMENT 22
  23. 23. Lecture 20 ~ Segment 2 Binary Logistic Regression Example 23
  24. 24. Binary logistic regression •  This example is based on “mock jury” research by Diamond & Casper (1992) –  People (mock jurors) watched a video of the sentencing phase of a murder trial in which the defendant had already been found guilty –  The issue for the jurors to decide was whether the defendant deserved the death penalty
  25. 25. Binary logistic regression •  This example is based on “mock jury” research by Diamond & Casper (1992) –  Assume the data were collected “pre-deliberation”, which means that each juror was asked to provide his or her vote on the death penalty verdict before the jurors met as a group to decide the overall jury verdict
  26. 26. Binary logistic regression •  Outcome variable (Y) •  Verdict •  1 = Voted for the death penalty •  0 = Voted against the death penalty •  Predictors (Xs) •  •  •  •  •  •  Danger Rehab Punish Gendet Specdet Incap •  All measured on a scale of 0 – 10
  27. 27. Binary logistic regression •  Danger (Dangerousness) •  Individual’s beliefs as to the future dangerousness of the defendant •  Rehab (Rehabilitation) •  Individual’s beliefs as to the importance of rehabilitation as a goal of criminal sentencing •  Punish (Punishment) •  Individual’s beliefs as to the importance of punishment as a goal of criminal sentencing
  28. 28. Binary logistic regression •  Gendet (General deterrence) •  Individual’s beliefs as to the importance of general deterrence as a goal of criminal sentencing (sentencing should deter the general public) •  Specdet (Specific deterrence) •  Individual’s beliefs as to the importance of specific deterrence as a goal of criminal sentencing (sentencing should deter the specific defendant) •  Incap (Incapacitation) •  Individual’s beliefs as to the importance of punishment as a goal of criminal sentencing
  29. 29. Binary logistic regression •  The General Linear Model will not guarantee a predicted outcome score between 0 and 1 •  The Logit transformation is a feature of an even more “general” mathematical framework in regression •  The Generalized Linear Model •  Allows for non-linear relationships between predictors and the outcome variable (see Lecture 23)
  30. 30. Binary logistic regression
  31. 31. Binary logistic regression
  32. 32. Binary logistic regression
  33. 33. Binary logistic regression
  34. 34. Binary logistic regression
  35. 35. Binary logistic regression
  36. 36. Binary logistic regression
  37. 37. Binary logistic regression •  Evaluation of individual predictors –  Odds ratios •  For a one unit increase in X, the predicted change in odds •  Can also report confidence intervals for odds –  Wald test •  A function of the regression coefficient. A Wald tests is calculated for each predictor variable and compares the fit of the model to the fit of the model without the predictor.
  38. 38. Binary logistic regression •  Evaluation of the model –  Model chi-square –  Compares the fit of the model to the fit of the null model –  Classification success •  Percentage of cases classified correctly
  39. 39. Binary logistic regression •  More than 2 categories on the outcome –  Multinomial logistic regression •  A-1 logistic regression equations are formed –  Where A = # of groups –  One group serves as reference group
  40. 40. END SEGMENT 40
  41. 41. END LECTURE 20 41

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