5.3 Simulations – using fake data as an intuition pump.5.1 Standard errors are used to compute p-values and confidence intervals.5 Variability and Uncertainty (Standard Deviations, Standard Errors, and Confidence Intervals).Part III: Some Fundamentals of Statistical Modeling.4.2.9 How to add the interaction effect to response and effects plots.4.2.8 How to combine the response and effects plots.4.2.6 How to generate a Response Plot with a grid of treatments using ggplot2.4.2.5 How to generate a Response Plot using ggpubr.4.2.4 How to use the Plot the Model functions.4.2.3 Be sure ggplot_the_model is in your R folder.4.1.3 Combining Effects and Modeled mean and CI plots – an Effects and response plot.4.1.2 Pretty good plot component 2: Modeled mean and CI plot.4.1.1 Pretty good plot component 1: Modeled effects plot.4.1 Pretty good plots show the model and the data.3.5.2 Reshaping data – Transpose (turning the columns into rows).3.2 Use the here function to construct the file path.3.1 Long data format – the way data should be.3 Data – Reading, Wrangling, and Writing.2.11 Let’s play around with an R Markdown file.2.10 Create and setup an R Markdown document (Rmd).2.9 Working on a project, in a nutshell.2.8 Create an R Studio Project for this textbook.2.4 If you didn’t modify the workspace preferences from the previous section, go back and do it.

2.3 Open R Studio and modify the workspace preference.2.2 Download and install R and R studio.2 Getting Started – R Projects and R Markdown.This, raises the question, what is “an effect”? 1.1 This text is about using linear models to estimate treatment effects and the uncertainty in our estimates.1 Analyzing experimental data with a linear model.including mapping between linear models and classical tests.Why bother with linear models – aren’t t-tests and ANOVA good enough?.

For mac download Draw.io 21.6.5