MESSuR
Preface
1
Introduction to Simple Models
2
Sampling Distributions and Statistical Inference
3
Simple Regression
4
Model Evaluation
5
Models with Multiple Categorical Predictors
6
Moderation and Nonlinear Models
7
Models with Single Categorical Predictor
8
Models with Multiple Categorical Predictors
9
Models with Continuous and Categorical Predictors
10
Models with Nonindependent Errors
11
Incorporating Predictors with Nonindependent Data
12
Logistic Models for Categorical Dependent Variables
References
Appendices
A
Appendix A: R as a Statistical Programming Language
A.1
Overview
A.2
Elements of Statistical Programming
A.2.1
Basic Elements of a Good SPL
A.3
Expressions
A.4
Primitive Expressions
A.5
Primitive Data Types:
A.6
Primitive Functions
A.6.1
Operators
A.7
Programming Languages are Not Forgiving
A.7.1
Syntactically valid expressions
A.7.2
Semantically valid expressions
A.8
Assignment
A.9
Combining Expressions
A.10
Complex Data Types
A.11
Grouping Homogeneous Data Types
A.12
Complex Functions
A.13
Abstraction
A.14
Abstraction
A.15
Data Abstraction
A.16
Functional Abstraction
A.17
Anatomy of a Function
A.18
Teaching With A Statistical Programming Language
A.18.1
An Example
A.19
myMean
A.20
Basic Elements of a Good SPL
Published with bookdown
Modeling in Education, and Social Sciences Using R
Modeling in Education, and Social Sciences Using R
William M. Murrah
2021-08-16
Preface