Data

Regression

Master regression analysis — the cornerstone of predictive modeling. From simple linear regression to logistic regression, learn to model relationships in data.

1

Correlation

Measuring linear relationships

Lesson 1: Scatter Plots & Correlation Coming Soon
Lesson 2: Pearson's r Coming Soon
Lesson 3: Correlation vs Causation Coming Soon
2

Simple Linear Regression

Fitting a line to data

Lesson 4: The Regression Equation Coming Soon
Lesson 5: Slope & Intercept Coming Soon
Lesson 6: Making Predictions Coming Soon
3

Least Squares

Minimizing prediction errors

Lesson 7: The Least Squares Method Coming Soon
Lesson 8: Sum of Squared Errors Coming Soon
Lesson 9: Line of Best Fit Coming Soon
4

Residuals

Analyzing prediction errors

Lesson 10: What are Residuals? Coming Soon
Lesson 11: Residual Plots Coming Soon
Lesson 12: Checking Assumptions Coming Soon
5

Multiple Regression

Using multiple predictors

Lesson 13: Multiple Regression Model Coming Soon
Lesson 14: Interpreting Coefficients Coming Soon
Lesson 15: Adjusted R-Squared Coming Soon
6

Polynomial Regression

Fitting curves to data

Lesson 16: Quadratic Regression Coming Soon
Lesson 17: Higher-Degree Polynomials Coming Soon
Lesson 18: Overfitting Coming Soon
7

Logistic Regression

Predicting binary outcomes

Lesson 19: The Logistic Function Coming Soon
Lesson 20: Odds & Log-Odds Coming Soon
Lesson 21: Classification with Logistic Regression Coming Soon
8

Model Selection

Choosing the best model

Lesson 22: AIC & BIC Coming Soon
Lesson 23: Cross-Validation Coming Soon
Lesson 24: Bias-Variance Tradeoff Coming Soon
9

Prediction & Forecasting

Using models to predict the future

Lesson 25: Confidence vs Prediction Intervals Coming Soon
Lesson 26: Extrapolation Risks Coming Soon
Lesson 27: Real-World Forecasting Coming Soon