Regression Analysis


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


Overview/Description
Regression analysis is a predictive modeling technique for investigating the relationships among variables. In this course, you'll learn about various Microsoft R regression analysis models, including linear, nonlinear, and logistic.

Target Audience
All individuals who wish to understand key concepts in big data analysis and Microsoft R features including scientists, analysts, and statisticians

Prerequisites
None

Expected Duration (hours)
1.0

Lesson Objectives

Regression Analysis

  • start the cours
  • describe fundamentals of linear regression and its use cases
  • recognize important metrics for measuring the accuracy of linear regression models
  • describe Microsoft R's important functions and arguments for modeling linear regressions
  • recall how to interpret linear regression results
  • describe nonlinear regression analysis and identify key differences between linear and nonlinear regression analysis
  • identify important functions for constructing and evaluating linear regression models
  • describe fundamentals of logistic regression and its use cases
  • identify important metrics for measuring the accuracy of logistic regression models
  • recognize how to interpret logistic regression results
  • recall Microsoft R's important functions and arguments for modeling logistic regressions
  • recognize important functions for constructing and evaluating logistic regression models

    • Course Number:
    df_abdr_a07_it_enus

    Expertise Level
    Intermediate