Member Training: Explaining Logistic Regression Results to Non-Researchers

August 1st, 2020 by

Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?


Member Training: Practical Suggestions for Improving Your Scatterplots

April 1st, 2020 by

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: How to Avoid Common Graphical Mistakes

December 1st, 2019 by

Good graphs are extremely powerful tools for communicating quantitative information clearly and accurately.

Unfortunately, many of the graphs we see today confuse, mislead, or deceive the reader.

These poor graphs result from two key limitations. One is a graph designer who isn’t familiar with the principles of effective graphs. The other is software with a poor choice of default settings.


R Graphics: Multiple Graphs and par(mfrow=(A,B))

December 16th, 2014 by

by David Lillis, Ph.D.

Today we see how to set up multiple graphs on the same page. We use the syntax  par(mfrow=(A,B)) (more…)

Member Training: Using Excel to Graph Predicted Values from Regression Models

May 1st, 2013 by

Graphing predicted values from a regression model or means from an ANOVA makes interpretation of results much easier.

Every statistical software will graph predicted values for you. But the more complicated your model, the harder it can be to get the graph you want in the format you want.

Excel isn’t all that useful for estimating the statistics, but it has some very nice features that are useful for doing data analysis, one of which is graphing.

In this webinar, I will demonstrate how to calculate predicted means from a linear and a logistic regression model, then graph them. It will be particularly useful to you if you don’t have a very clear sense of where those predicted values come from.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

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About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

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