Statistical Analysis and Modeling in R: Performing Regression Analysis

R Programming 4.0+
  • 9 Videos | 1h 3m 53s
  • Includes Assessment
  • Earns a Badge
Regression models are used to predict continuous values and are some of the most commonly used machine learning models. Use this course to grasp what exactly machine learning (ML) algorithms are and how you can use ML models to predict outcomes based on input data. Learn how regression models work, train them, and evaluate regression results using metrics such as R2 and RMSE. Perform regression analysis in R using the ordinary least squares regression. Build models using simple and multiple regression and train a regression model using cross-validation. Upon completing this course, you'll be able to perform regression to predict continuous values and evaluate these models using metrics such as the R2 and adjusted R2.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recall the basic characteristics of machine learning models
    examine how to fit a straight line on data to build a regression model and evaluate the model
    identify and visualize the relationships in data
    perform simple linear regression with a single predictor
  • perform multiple regression using multiple predictors
    apply the regression model to get predictions for test data
    build a regression model using cross-validation
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 1s
    UP NEXT
  • Playable
    2. 
    The Basic Characteristics of Machine Learning Models
    9m 11s
  • Locked
    3. 
    Building and Evaluating Regression Models Using R
    10m 42s
  • Locked
    4. 
    Visualizing Data Relationships Using R
    10m 1s
  • Locked
    5. 
    Performing Simple Linear Regression in R
    8m 43s
  • Locked
    6. 
    Performing Multiple Regression in R
    10m 7s
  • Locked
    7. 
    Deriving Predictions Using Regression Models in R
    3m 12s
  • Locked
    8. 
    Building Regression Models Using Cross-validation
    4m 22s
  • Locked
    9. 
    Course Summary
    2m 5s

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