Course details

Linear and Logical Regression

Linear and Logical Regression


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
Regression modeling investigates relationships between dependent and independent variables and is heavily relied upon for predictive analytics and data mining applications. Explore both the linear and logistic regression models.

Target Audience
All individuals who are new to predictive analytics and wish to use it to optimize their business performance; business leaders; analysts; marketing, sales, software, and IT professionals who want to add predictive analytics to their skill set; and decision makers of any kind

Prerequisites
None

Expected Duration (hours)
0.8

Lesson Objectives

Linear and Logical Regression

  • start the course
  • recognize characteristics of linear regression
  • calculate sum of squared errors
  • determine the OLS parameters
  • make regression inferences
  • list key features of logistic regression
  • recognize the logit transformation and likelihood functions
  • interpret logistic regression results
  • calculate the odds ratio
  • recognize key considerations for logistic regression
  • determine and interpret the statistical significance of individual variables and of the overall model
  • Course Number:
    df_prma_a12_it_enus

    Expertise Level
    Intermediate