5.7.1 95% of 95% CIs of the difference include the true difference.5.7 Confidence limits of a difference between means.5.6.1 The standard error of a difference between means is the standard deviation of the sampling distribution of the difference.5.6 Standard error of a difference between means.5.5.4 A plot of a “parametric” CI vs. bootstrap CI of the means.5.5.2 Interpretation of a confidence interval.5.4.1 An example of bootstrapped standard errors.5.3.2 Using R to generate fake data to explore the standard error.5.3.1 Using Google Sheets to generate fake data to explore the standard error.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?.

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