# logistic regression

### SPSS Procedures for Logistic Regression

May 15th, 2014 by

Need to run a logistic regression in SPSS? Turns out, SPSS has a number of procedures for running different types of logistic regression.

Some types of logistic regression can be run in more than one procedure.  For some unknown reason, some procedures produce output others don’t.  So it’s helpful to be able to use more than one.

### Logistic Regression

Logistic Regression can be used only for binary dependent (more…)

### Member Training: Discrete Time Event History Analysis

February 1st, 2014 by

What is the relationship between predictors and whether and when an event will occur?

This is what event history (a.k.a., survival) analysis tests.

There are many flavors of Event History Analysis, though, depending on how time is measured, whether events can repeat, etc.

In this webinar, we discussed many of the issues involved in measuring time, including censoring, and introduce one specific type of event history model: the logistic model for discrete time events.

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.

### How to Interpret Odd Ratios when a Categorical Predictor Variable has More than Two Levels

July 1st, 2013 by

One great thing about logistic regression, at least for those of us who are trying to learn how to use it, is that the predictor variables work exactly the same way as they do in linear regression.

Dummy coding, interactions, quadratic terms–they all work the same way.

### Dummy Coding

In pretty much every regression procedure in every stat software, the default way to code categorical variables is with dummy coding.

All dummy coding means is recoding the original categorical variable into a set of binary variables that have values of one and zero.  You may find it helpful to (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.

Not a Member? Join!

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.

Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.

### Member Training: Hierarchical Regressions

April 1st, 2013 by

Hierarchical regression is a very common approach to model building that allows you to see the incremental contribution to a model of sets of predictor variables.

Popular for linear regression in many fields, the approach can be used in any type of regression model — logistic regression, linear mixed models, or even ANOVA.

In this webinar, we’ll go over the concepts and steps, and we’ll look at how it can be useful in different contexts.

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.
Not a Member? Join!

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.

Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.

### Member Training: Types of Regression Models and When to Use Them

February 1st, 2013 by

Linear, Logistic, Tobit, Cox, Poisson, Zero Inflated… The list of regression models goes on and on before you even get to things like ANCOVA or Linear Mixed Models.

In this webinar, we will explore types of regression models, how they differ, how they’re the same, and most importantly, when to use each one.

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.

Not a Member? Join!