Below, I’ve embedded a Python Jupyter Notebook hosted on Kyso, which is a hub where you, “can publish Jupyter notebooks, charts, code, datasets and write articles in our custom markdown editor.” This is a work in progress while I teach myself Python (here is the original Kyso post, nmmichalak/python_selfteach).
Introduction In this post, I’ll introduce the logistic regression model in a semi-formal, fancy way. Then, I’ll generate data from some simple models:
1 quantitative predictor
1 categorical predictor
2 quantitative predictors
1 quantitative predictor with a quadratic term I’ll model data from each example using linear and logistic regression. Throughout the post, I’ll explain equations, terms, output, and plots. Here are some key takeaways:
OSF page storing materials used for workshops conducted at the University of Michigan
Multidimensional Scaling, the precursor to Principal Components Analysis, Common Factor Analysis, and related techniques Multidimensional scaling is an exploratory technique that uses distances or disimilarities between objects to create a multidimensional representation of those objects in metric space. In other words, multidimensional scaling uses data about the distance (e.g., miles between cities) or disimilarity (e.g., how (dis)similar are apples and tomatoes?) among a set of objects to “search” for some metric space that represents those objects and their relations to each other.
Introduction I wrote this for psychologists who want to learn how to use R in their research right now. What does a psychologist need to know to use R to import, wrangle, plot, and model their data today? Here we go.
Foundations: People and their resources that inspired me. Dan Robinson [.html] convinced me that beginneRs should learn tidyverse first, not Base R. This tutorial uses tidyverse.
What is a correlation? A correlation quantifies the linear association between two variables. From one perspective, a correlation has two parts: one part quantifies the association, and the other part sets the scale of that association.
The first part—the covariance, also the correlation numerator—equates to a sort of “average sum of squares” of two variables:
\(cov_{(X, Y)} = \frac{\sum(X - \bar X)(Y - \bar Y)}{N - 1}\) It could be easier to interpret the covariance as an “average of the X-Y matches”: Deviations of X scores above the X mean multipled by deviations of Y scores below the Y mean will be negative, and deviations of X scores above the X mean multipled by deviations of Y scores above the Y mean will be positive.
R Shiny Application and GitHub repository for 2020 Democratic Presidential Primary Polling Averages