Logistic regression
This presentation is the property of its rightful owner.
Sponsored Links
1 / 10

Logistic Regression PowerPoint PPT Presentation


  • 198 Views
  • Uploaded on
  • Presentation posted in: General

Logistic Regression. Now with multinomial support!. An Introduction. Logistic regression is a method for analyzing relative probabilities between discrete outcomes (binary or categorical dependent variables) Binary outcome: standard logistic regression ie . Dead (1) or NonDead (0)

Download Presentation

Logistic Regression

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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Logistic regression

Logistic Regression

Now with multinomial support!


An introduction

An Introduction

  • Logistic regression is a method for analyzing relative probabilities between discrete outcomes (binary or categorical dependent variables)

    • Binary outcome: standard logistic regression

      • ie. Dead (1) or NonDead (0)

    • Categorical outcome: multinomial logistic regression

      • ie. Zombie (1) or Vampire (2) or Mummy (3) or Rasputin (4)


How it all works

How It All Works

  • The logistic equation is written as a function of z, where z is a measure of the total contribution of each variable x used to predict the outcome

  • Coefficients determined by maximum likelihood estimation (MLE), so larger sample sizes are needed than for OLS


Graph of the logistic function

Graph of the Logistic Function


Coefficient interpretation

Coefficient Interpretation

  • Standard coefficients (untransformed) report the change in the log odds of one outcome relative to another for a one-unit increase of the independent variable (positive, negative)

  • Exponentiating the coefficients reports the change in the odds-ratio (greater than, less than one)

  • By evaluating all other values at particular levels (ie. their means) it is possible to obtain predicted probability estimates


Logistic regression

SPSS

  • Standard Logistic Regression:

    • logistic regression [dep. var] with [ind. vars]

  • Multinomial Logistic Regression:

    • nomreg [dep. var] with [ind. vars]


Stata

STATA

  • Standard Logistic Regression:

    • logit [dep. var] [ind. vars]

  • Multinomial Logistic Regression:

    • mlogit [dep. var] [ind. vars]

  • Odds-Ratio Coefficients

    • [regression], or

  • Predicted Probability Estimates (new to Stata 11)

    • margins [ind. var to analyze], at[value of other ind. vars]


Other methods

Other Methods?

  • Probit

    • Very similar to logit

    • Easier to interpret coefficients (predicted probabilities)

    • Probabilities aren’t bounded between 0 and 1


Examples

Examples

  • Stata:

    • use http://www.ats.ucla.edu/stat/stata/dae/binary.dta

    • logit admit gre gpa i.rank

    • logit, or

      • odds-ratio (instead of log odds-ratio) interpretation of the coefficients

    • margins rank, atmeans

      • predicted probability of rank with gre and gpa at their means

    • margins, at(gre=(200(100)800))

      • start with gre=200, increase by steps of 100, end at 800


Examples1

Examples

  • SPSS

    • Download binary.sav from http://www.ats.ucla.edu/stat/spss/dae/logit.htm

    • After opening the file:

      • logistic regression admit with gregpa rank

        /categorical = rank.


  • Login