Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. - A basic understanding of statistics and regression models. - At least a little familiarity with proof based mathematics. - Basic knowledge of the R programming language. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.
Residuals distributional results
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Skills You'll Learn
Applied Mathematics, Regression Analysis, Mathematical Modeling, Probability Distribution, Probability & Statistics, Statistical Modeling, Statistical Analysis, Integral Calculus, Linear Algebra, R Programming
Reviews
4.6 (101 ratings)
- 5 stars69.30%
- 4 stars21.78%
- 3 stars4.95%
- 2 stars2.97%
- 1 star0.99%
RL
Jan 14, 2023
Great !!! Learning time and I enjoy the math side of it...
ML
Jan 31, 2017
Good course on applied linear statistical modeling.
From the lesson
Residuals
In this module we will revisit residuals and consider their distributional results. We also consider the so-called PRESS residuals and show how they can be calculated without re-fitting the model.
Taught By
Brian Caffo, PhD
Professor, Biostatistics