Statistics 3
Why?
Statistical modeling and regression analysis are fundamental tools in the data scientist's toolkit. These techniques allow you to explore relationships between variables, make predictions, and understand the underlying structure of your data. Statistics 3 builds on previous statistical knowledge to equip you with the analytical skills needed for more advanced machine learning concepts and data-driven decision making.
What?
This course focuses on regression analysis and modeling techniques. You will learn how to build, evaluate, and interpret linear regression models, extend to multiple regression, understand correlation measures, and diagnose model performance. The course emphasizes both the mathematical foundations and practical application of these statistical methods for data analysis. Linear regression is considered the simplest machine learning model, we learn it in this course from a statistical point of view.
Curriculum:
Linear Regression Analysis
Introduction to regression analysis, its purpose and applications in data science. Overview of the linear model, least squares estimation, and interpretation of regression parameters.
Simple Linear Regression
Detailed exploration of the simple linear regression model with one predictor variable. Topics include parameter estimation, hypothesis testing, confidence intervals, and prediction.
Multiple Linear Regression
Extending regression to multiple predictor variables, partial regression coefficients, model building strategies, interpretation of results, and handling categorical variables through dummy coding.
Correlation
Understanding correlation measures, their relationship to regression, interpretation of correlation coefficients, limitations, and testing for significant relationships between variables.
Model Diagnostics
Techniques for assessing model assumptions, identifying influential observations, detecting multicollinearity, evaluating residuals, and improving model fit through transformations and remedial measures.
Notes
Linear Regression is 1st machine learning model usually learned. The course focuses on the statistical point of view, but there is more to it. If you wanna have a really solid base (especially if you aim to continue in research) in machine learning, you might want to try implement it yourself, or dive more into the proof and intuition of least squares method (check maximum likelihood estimation), or its generalized form (General Linear Models).
