• Online, Self-Paced

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.

Learning Objectives

Linear Regression

  • start the course
  • recognize characteristics of linear regression
  • calculate sum of squared errors
  • determine the OLS parameters
  • make regression inferences

Logistic Regression

  • 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

Practice: Linear Regression Inference

  • determine and interpret the statistical significance of individual variables and of the overall model

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • All-Source Analysis
  • Data Administration
  • Systems Analysis

Specialty Areas have been removed from the NICE Framework. With the recent release of the new NICE Framework data, updates to courses are underway. Until this course can be updated, this historical information is provided to give better context as to how it can help you with your cybersecurity goals.

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.