15 interactive notebooks ready to launch.
Investigate sampling bias and estimate population parameters.
Use bootstrap resampling to construct confidence intervals.
Explore the Student’s t distribution for small-sample inference.
Learn hypothesis testing concepts with real examples.
Use permutation tests for nonparametric A/B testing.
Understand power and error types in hypothesis testing.
Perform t-tests and interpret results for comparing means.
Use analysis of variance to compare multiple group means.
Apply chi-square tests to categorical data analysis.
Fit linear models and interpret slope and intercept.
Extend regression to multiple predictors and interpret coefficients.
Measure model fit and learn to identify overfitting.
Model binary outcomes with logistic regression and odds ratios.
Train and test a logistic regression classifier on sample data.
Evaluate classifier performance with confusion matrices.