Blog Archives

Concepts you Need to Understand to Run a Mixed or Multilevel Model

January 23, 2013
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Concepts you Need to Understand to Run a Mixed or Multilevel Model

Have you ever been told you need to run a 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 [...]

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Ten Data Analysis Tips in R with David Lillis

December 4, 2012
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Ten Data Analysis Tips in R with David Lillis

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 [...]

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The 13 Steps to Running Any Statistical Model

October 3, 2012
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The 13 Steps to Running Any Statistical Model

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 [...]

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Overview of Nonparametric Techniques with Elaine Eisenbeisz

August 1, 2012
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Overview of Nonparametric Techniques with Elaine Eisenbeisz

A distribution of data which is not normal does not mean it is abnormal.  There are many data analysis techniques which do not require the assumption of normality. This webinar will provide information on when it is best to use nonparametric alternatives and provides information on suggested tests to use in lieu of: Independent samples [...]

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The Unstructured Covariance Matrix: When It Does and Doesn’t Work

June 22, 2012
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The Unstructured Covariance Matrix: When It Does and Doesn’t Work

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 [...]

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Confusing Statistical Term #6: Factor

April 27, 2012
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Confusing Statistical Term #6: Factor

Factor is tricky much in the same way as hierarchical and beta, because it too has different meanings in different contexts. Factor might be a little worse, though, because its meanings are related. In both meanings, a factor is a variable. But a factor has a completely different meaning and implications for use in two different contexts. Factor analysis In factor analysis, a factor is an unmeasured, latent variable, that…

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Data Mining Webinar with Peter Bruce, President, Statistics.com

April 4, 2012
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Data Mining Webinar with Peter Bruce, President, Statistics.com

Data Mining methods lie at the center of the constellation of techniques under the umbrella of “business analytics.”  These techniques deal with analysis of large existing datasets (as opposed to controlled experiments, or sample surveys). This webinar will give an overview of data mining techniques, which include: In predictive modeling, we build a model to [...]

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When To Fight For Your Analysis and When To Jump Through Hoops

February 14, 2012
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When To Fight For Your Analysis and When To Jump Through Hoops

In the world of data analysis, there’s not always one clearly appropriate analysis for every research question. There are so many factors to take into account, including the research question to be answered, the measurement of the variables, the study design, data limitations and issues, the audience, practical constraints like software availability, and the purpose [...]

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Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood

March 2, 2009
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Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood

Two methods for dealing with missing data,vast improvements over traditional approaches, have become available in mainstream statistical software in the last few years.

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