Download free course Regression Models for Data Science in R, pdf file on 144 pages by Brian Caffo.
The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.
The student should have a basic understanding of statistical inference such as contained in "Statistical inference for data science". The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective.
After reading the book and watching the associated videos, students will be able to perform multivariable regression models and understand their interpretations.
Table of contents
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Introduction
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Notation
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Ordinary least squares
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Regression to the mean
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Statistical linear regression models
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Residuals
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Regression inference
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Multivariable regression analysis
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Multivariable examples and tricks
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Adjustment
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Residuals, variation, diagnostics
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Multiple variables and model selection
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Generalized Linear Models
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Binary GLMs
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Count data
| Pages : | 144 |
| Size : | 4.3 MB |
| Downloads: | 50 |
| Created: | 2022-02-03 |
| License: | CC BY-NC-SA |
| Author(s): | Brian Caffo |