Data Cleaning is a critically important part of any data analysis. Without properly prepared data, the analysis will yield inaccurate results. Correcting errors later in the analysis adds to the time, effort, and cost of the project.
Membership Webinars
Member Training: Data Cleaning
June 1st, 2020 by guest contributer
Member Training: Seven Fundamental Tests for Categorical Data
May 1st, 2020 by TAF Support
In the world of statistical analyses, there are many tests and methods that for categorical data. Many become extremely complex, especially as the number of variables increases. But sometimes we need an analysis for only one or two categorical variables at a time. When that is the case, one of these seven fundamental tests may come in handy.
These tests apply to nominal data (categories with no order to them) and a few can apply to other types of data as well. They allow us to test for goodness of fit, independence, or homogeneity—and yes, we will discuss the difference! Whether these tests are new to you, or you need a good refresher, this training will help you understand how they work and when each is appropriate to use.
Member Training: Practical Suggestions for Improving Your Scatterplots
April 1st, 2020 by guest contributer
The scatterplot is a simple display of the relationship between two, or sometimes three, variables. You have a wide range of options for displaying a scatterplot. In particular, you can control the location, size, shape, and color of the points in your scatterplot.
Member Training: Confusing Statistical Terms
February 28th, 2020 by guest contributer
Learning statistics is difficult enough; throw in some especially confusing terminology and it can feel impossible! There are many ways that statistical language can be confusing.
Some terms mean one thing in the English language, but have another (usually more specific) meaning in statistics. (more…)
Member Training: A Gentle Introduction to Multilevel Models
January 31st, 2020 by guest contributer
In this Stat’s Amore Training, Marc Diener will help you make sense of the strange terms and symbols that you find in studies that use multilevel modeling (MLM). You’ll learn about the basic ideas behind MLM, different MLM models, and a close look at one particular model, known as the random intercept model. A running example will be used to clarify the ideas and the meaning of the MLM results.
Member Training: The Anatomy of an ANOVA Table
December 31st, 2019 by Jeff Meyer
Our analysis of linear regression focuses on parameter estimates, z-scores, p-values and confidence levels. Rarely in regression do we see a discussion of the estimates and F statistics given in the ANOVA table above the coefficients and p-values.
And yet, they tell you a lot about your model and your data. Understanding the parts of the table and what they tell you is important for anyone running any regression or ANOVA model.