Introduction to Simplistics (Self-Guided)

Are you ready to be a stats ninja? This course is your beginning, your “white belt” training, if you will. By the end of this course, you will know what it takes to understand your data, to understand your model, to make inferences, and to estimate uncertainty.

We’ll begin with quick (but important) discussions of data ethics and measurement before tackling the foundation of all statistics: models. First, we’ll first speak of univariate models, including how to visualize them and how to compute estimates, such as means, medians, standard deviations, etc. As we begin with univariate models, you’ll learn to speak (well, type, technically) the language of flexplot, which will enable you to visualize both the data and the model, and allow you to judge how well the data fit your model.

You’ll then learn about bivariate models, both the old names (t-tests, ANOVA, regression) and the new (linear model, linear model, and….um…linear model). We’ll begin by visualizing these models with scatterplots and beeswarm plots, give you insights in what problems to look out for, and teach you how to interpret estimates from these models (e.g., Cohen’s d, correlation coefficients, mean differences, slopes, intercepts).

Before moving on to multivariate models, we’ll take a quick statistical siesta and learn how to evaluate models. How do we know whether they are good models? You’ll learn about linear models assumptions (linearity, normality, etc.), how to evaluate them visually (e.g., with SL plots or residual dependence plots), and why they matter. Then we get to the fun of multivariate models, where you’ll learn both the old names (ANCOVA, factorial ANOVA, multiple regression) and the new names (linear models, linear models, and ….. do I really have to say it?).

Finally, we’ll save the most complicated for last: probability. We’ll learn the basics of significance testing, Bayesian inference, and (my personal favorite) model comparisons. We’ll use each of these to make decisions about data and to estimate uncertainty.

Course Structure

This class is broken down into twelve “units”:
• Ethics
• Measurement
• Univariate Models
• Bivariate Models
• Diagnostics
• Linear Models
• Multivariate Linear Models
• Conditioning
• Interaction Effects
• Probability 1
• Probability 2
• Model Comparisons

Within each unit, you’ll see a unit introduction that itemizes the learning objectives, followed by a series of videos (mostly from my YouTube channel), textbook readings, optional scholarly articles, and your assignments. For each unit you’ll have at least two assignments:

1. A unit discussion board. Here you will find questions students have asked in the past, and have the opportunity to posts questions yourself.
2. A weekly quiz. These quizzes test your understanding of the learning objectives. You are welcome (and encouraged) to take these quizzes as many times as you like, both to perfect your score (which really doesn’t matter, except for your own gratification) and to receive more questions. (I aim to have a pool that is much larger than the actual quizzes you take so you’ll have different quizzes every time).

For some of the weeks, you’ll also have a “practice quiz,” which is much more R-focused, allowing you to analyze actual datasets and answer questions about those datasets.

There is also a midterm and a final for this course. Again, there’s no penalty for scoring poorly. It’s simply an opportunity to self-evaluate your understanding of course content.