![]() Although there are many useful books for purchase and subscription training sites available, it’s also quite possible to learn R using only free resources. Not only is the software free, so are many of the resources available for learning R. One of the most appealing aspects of R is that it is a free, open source software program. R is free! (and available for Windows, Macs, and Linux machines) It is there that you will see the full expanse of arguments and options that you can incorporate into your code. This typically involves using resources like this e-book or doing a web search for documentation and/or websites that detail a particular package or function. Beyond that, knowing R means knowing how to marshal available resources, bringing them to bear on the task at hand. Instead, think of knowing R as understanding its basic logic, structure, and grammatical conventions. Even then, you will only be able to hold a limited number of functions in your head. Unless you work with R daily, you should not expect to memorize the language. It is also helpful to keep in mind what it is to learn or know R. In the end, though, they are akin to different accents or dialects of the same language. These variations can add to the difficulty in learning R. Different packages (user-written functions) may have slightly different command structures. There may be more than one way to accomplish a task. That said, as with many languages, you will find there to be some flexibility within that structure. Although these rules offer needed structure, they can be inflexible, leading to errors when coding. As with any language, R has a vocabulary and operates according to certain grammatical rules. R is a programming language, one particularly well suited to statistical analysis and graphing. I offer step-by-step instructions for getting started at the end of the document. ![]() I recommend reading the entire document before taking any actions. This document is intended to provide needed background for getting started in R. 11.11 Consolidated Code for Multiple Logistic Regression.11.9 Interactions (modeling and graphing) for Multiple Logistic Regression.11.8 Graphing Margins (Predicted Probabilities) for Multiple Logistic Regression.11.7 Graphing Coefficients and CIs for Multiple Logistic Regression.11.6 Producing Formatted Tables of Multiple Logistic Regression Results.11.5 Diagnostics for Multiple Logistic Regression.11.4 Model Fit Statistics for Multiple Logistic Regression.11.3 Basic Multiple Logistic Regression Commands.11.2 Data Prep for Multiple Logistic Regression.11.1 Packages Needed for Multiple Logistic Regression.10.10 Consolidated Code for Multiple OLS Regression.10.9 Modeling and Graphing Interactions for Multiple OLS Regression.10.8 Graphing Margins (Predicted Values) for Multiple OLS Regression.10.7 Graphing Coefficients and CIs for Multiple OLS Regression.10.6 Producing Formatted Tables of Multiple OLS Regression Results.10.5 Outliers - Identifying and Excluding.10.4 Diagnostic Plots for Multiple OLS Regression.10.2 Data Prep for Multiple OLS Regression.10.1 Packages Needed for Multiple OLS Regression.9.5 Consolidated Code for Correlation and Simple OLS Regression.9.4.3 Scatterplot Matrix and Correlation Matrix.9.4.2 Correlogram for Correlation Matrix.9.1 Packages Needed for Correlation and Simple OLS Regression.9 Correlation and Simple OLS Regression.8.3 Cross-Tabulation, Chi-Square Test of Independence, and Effect Size.7.4 The Analysis of Variance and Effect Size.7.3.2 Checking Equal Variances for ANOVA.7.3 Checking Data for Violations of Assumptions for ANOVA.6.5 Wilcoxon/Mann-Whitney Rank Sum Test.6.4 T-Test Command (two sample test of group means) and Effect Size.6.3.2 Checking for Equal Variances for T-Test.6.3 Checking Data for Violations of Assumptions for T-Test.5.4 Generating a Summary Statistics Table. ![]()
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