Logistic Regression (Graphical)

Logistic Regression:

Logistic regression is used with binary data when you want to model the probability that a specified outcome will occur. Specifically, it is aimed at estimating parameters a and b in the following model:

where p_i is the probability of a success for given value x_i of the explanatory variable X.

Use of the log of the odds p/(1-p) (the logit) guarantees that the predicted value of p will always be between 0 and 1.

Courses Using This Term

Categorical Data Analysis

This course will teach you the analysis of contingency table data. Topics include tests for independence, comparing proportions as well as chi-square, exact methods, and treatment of ordered data. Both 2-way and 3-way tables are covered.

Generalized Linear Models

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.

Predictive Analytics 1 – Machine Learning Tools

This course introduces to the basic concepts in predictive analytics to visualize and explore data to understand the two core paradigms that account for most business applications of predictive modeling: classification and prediction.

Courses

Certificates

Degrees

Skillsets

Logistic Regression (Graphical)

Courses Using This Term

About Statistics.com

Our Links

Social Networks

Contact