Statistical Modeling Theory- Variable Types & Design Matrices
pdf | 7.18 MB | English | Isbn: B08NHZ3XGD | Author: Christopher Panaretos | Year: 2020
Description:
This book briefly explains how statistical models are applied to data sets.
The first part of the book lists 16 types of variables that appear in statistics, and these types can apply to predictor or response variables.
The second part works through example data sets using the R programming language, covering different combinations of predictor and response types.
In the final part of the book, various predictor types are used to illustrate how their information is encoded as a matrix. Seeing this design matrix is helpful for understanding how computers fit statistical models to data.
Part 1 - Statistical Modeling Theory
Part 2 - Model Fitting Examples
Part 3 - Summary of Statistical Modeling
Part 4 - Design Matrices
The first part of the book lists 16 types of variables that appear in statistics, and these types can apply to predictor or response variables.
The second part works through example data sets using the R programming language, covering different combinations of predictor and response types.
In the final part of the book, various predictor types are used to illustrate how their information is encoded as a matrix. Seeing this design matrix is helpful for understanding how computers fit statistical models to data.
Part 1 - Statistical Modeling Theory
Part 2 - Model Fitting Examples
Part 3 - Summary of Statistical Modeling
Part 4 - Design Matrices
Category:Science Methodology & Statistics, Science Education Research, Probability & Statistics