Logistic Regression

Logistic regression is a statistical tool used to assess the effect of several explanatory variables on a categorical response variable, that is a variable that can only take on a limited set of values. This situation is encountered often in several fields of applications. Here are a few examples of questions that can be adressed using logistic regression:

Whenever the response variable is binary, that is, it can only take on two values, logistic regression is used. Whenever the response variable is ordinal, for instance the severity of a disease, ordinal logistic regression is used. Finally, polytomous regression may also be used to study the relationship betwen a variable that can take on several unordered values and a set of explanatory variables.

Upon completion of this course, participants will be able to:

Understand how polytomous regression works

This is primarily ilt training Computer Lab Work This class may involve group study This class may be available at a classroom in Montreal, QC, Contact Creascience for more information

Course Level: introductory Duration: 1 days Training Presented in: English

• Introduction to Logistic Regression
  • Goal: To Study the Relationship between a Categorical Variable and a Set of Explanatory Variables
  • Why Does Ordinary Multiple Linear Regression Fail for the Analysis of a Categorical Response Variable?
• Refresher on Multiple Linear Regression
  • Estimation of the Model
  • Interpretation of the Coefficients of the Model Parameters
  • Goodness-of-Fit and Validation Techniques
• Classical Case: a Binary Response Variable
  • Basic Principle: Modeling the probability of observing a given value of the reponse variable
  • Example
  • Interpretation of Statistical Software Output: Coefficients and Mathematical Transformations, Odds Ratios, Statistical Testing of Model Coefficients
  • Comparison of Logistic Regression Software Output with Multiple Linear Regression
  • Goodness-of-Fit Measures: Nested Models, Cross-Validation Techniques
  • Parallel with Discriminant Analysis
  • Using the Model for Prediction
  • Principle of Variable Selection
• Case of an Ordinal Response Variable: Ordinal Logistic Regression
• Case of a Nominal Response Variable: Polytomous Regression
• Practical Considerations
  • Procedures Available in Statistical Software
  • Implementation and Interpretation
• Summary

• Cluster Analysis and its Applications
• cole d' t sur les analyses multidimensionnelles
• Fundamental Tools in Statistics
• Introduction aux tudes de dur e de vie et de stabilit
• Introduction to the Design of Experiments
• L'analyse de classification et ses applications
• L'analyse en composantes principales et ses applications
• Les outils essentiels de la statistiques
• Life, Reliability and Survival Models
• Logistic Regression
• Multivariate Data Analysis Summer School
• Nonlinear Regression
• Principal Component Analysis and its Applications
• Regression on Principal Components and PLS Regression
• Simple and Multiple Linear Regression Techniques
• Workshop on Regression Techniques