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.
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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Have you ever been told you need to run a mixed (aka: multilevel) model and been thrown off by all the new vocabulary?
It happened to me when I first started my statistical consulting job, oh so many years ago. I had learned mixed models in an ANOVA class, so I had a pretty good grasp on many of the concepts.
But when I started my job, SAS had just recently come out with Proc Mixed, and it was the first time I had to actually implement a true multilevel model. I was out of school, so I had to figure it out on the job.
And even with my background, I had a pretty steep learning curve to get to a point where it made sense. Sure, I was able to figure out the steps, but there are some pretty tricky situations and complicated designs out there.
To implement it well, you need a good understanding of the big picture, and how the small parts fit into it. (more…)
Have you starting using R?
One secret to using any statistical software well and without frustration is learning the little “tricks” that make it easy to do the things you need to do.
This is especially true in R, which is constantly being updated.
In this webinar, R expert David Lillis will show you 10 tips for getting the most of R.
David Lillis has taught R to many researchers and statisticians. His company, Sigma Statistics and Research Limited, provides both on-line instruction and face-to-face workshops on R, and coding services in R. David holds a doctorate in applied statistics and is a frequent contributor to The Analysis Factor.
This webinar has already taken place. You can gain free access to a video recording of the webinar by completing the form below.
We also offer some online R workshops taught by David.
All statistical modeling–whether ANOVA, Multiple Regression, Poisson Regression, Multilevel Model–is about understanding the relationship between independent and dependent variables. The content differs, but as a data analyst, you need to follow the same 13 steps to complete your modeling.
This webinar will give you an overview of these 13 steps:
- what they are
- why each one is important
- the general order in which to do them
- on which steps the different types of modeling differ and where they’re the same
Having a road map for the steps to take will make your modeling more efficient and keep you on track.
This webinar has already taken place. You can gain free access to a video recording of the webinar by completing the form below.
Two methods for dealing with missing data, vast improvements over traditional approaches, have become available in mainstream statistical software in the last few years.
Both of the methods discussed here require that the data are missing at random–not related to the missing values. If this assumption holds, resulting estimates (i.e., regression coefficients and standard errors) will be unbiased with no loss of power.
The first method is Multiple Imputation (MI). Just like the old-fashioned imputation (more…)
If you’ve ever done any sort of repeated measures analysis or mixed models, you’ve probably heard of the unstructured covariance matrix. They can be extremely useful, but they can also blow up a model if not used appropriately. In this article I will investigate some situations when they work well and some when they don’t work at all.
The Unstructured Covariance Matrix
The easiest to understand, but most complex to estimate, type of covariance matrix is called an unstructured matrix. Unstructured means you’re not imposing any constraints on the values. For example, if we had a good theoretical justification that all variances were equal, we could impose that constraint and have to only estimate one variance value for every variance in the table.
But in an unstructured covariance matrix there are no constraints. Each (more…)
