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R lessons (32/41)

R — Linear Regression

Linear regression

model <- lm(y ~ x1 + x2, data = df)
summary(model)

Predict

predictions <- predict(model, newdata = test_df)

Logistic regression

model <- glm(y ~ x, data = df, family = "binomial")

Mini Practice

  1. Fit linear model
  2. View summary
  3. Make predictions
  4. Try logistic regression

Up Next

Continue with Clustering - Cluster analysis.

Related Topics

RegressionMultiple RegressionMachine LearningData Visualization

Frequently Asked Questions about Linear Regression

What is Linear Regression in R?

Linear Regression is a fundamental concept in R. This lesson explains it step by step with clear examples, making it easy for beginners to understand.

How do I learn Linear Regression?

Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Linear Regression.

Why is Linear Regression important in R?

Linear Regression is essential for R development. Understanding this concept will help you write better code and solve real-world problems more effectively.