Regression Models
Explore linear and logistic regression for predictive modeling and interpret model outputs.
Regression Models is a free R Academy lesson on CoddyKit. This is lesson 1 of 3. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 3 lessons in total.
Introduction to Regression Models
Regression analysis is a statistical technique used to model relationships between variables. It helps predict outcomes and uncover data patterns.
Understanding Linear Regression
Linear regression models the relationship between a dependent and independent variable using a straight line.
model <- lm(mpg ~ hp, data=mtcars)
summary(model)Visualizing Regression Lines
Use abline() to add a regression line to a scatter plot.
plot(mtcars$hp, mtcars$mpg)
abline(model, col='red')Multiple Linear Regression
Multiple regression includes more than one independent variable to explain variation in the dependent variable.
model_multi <- lm(mpg ~ hp + wt, data=mtcars)
summary(model_multi)Checking Model Fit
Use summary() to evaluate regression model quality, including R-squared and p-values.
summary(model_multi)Performing Logistic Regression
Logistic regression models binary categorical outcomes (e.g., success vs. failure).
data(mtcars)
mtcars$am <- as.factor(mtcars$am)
model_logit <- glm(am ~ hp + wt, data=mtcars, family='binomial')
summary(model_logit)Predicting New Values
Use predict() to generate predictions from a trained regression model.
predict(model_multi, newdata=data.frame(hp=120, wt=3))Evaluating Model Assumptions
Regression models assume normality, homoscedasticity, and independence of errors. Check residual plots to assess validity.
plot(model_multi$residuals)Summary
In this lesson, you learned:
- How to perform linear and multiple regression.
- How to evaluate regression model performance.
- How to predict new values using regression models.
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Frequently Asked Questions
Is the “Regression Models” lesson free?
Yes — any 3 lessons of the R Academy learning path, including “Regression Models”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 3 lessons in total.
What will I learn in “Regression Models”?
Explore linear and logistic regression for predictive modeling and interpret model outputs. You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start R Academy?
No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 1 of 3.
How long does the “Regression Models” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this R Academy lesson?
Yes. Every R Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Regression Models
- Time Series Analysis
- Machine Learning Fundamentals