One of the most confusing things about statistical analysis is the different vocabulary used for the same, or nearly-but-not-quite-the-same, concepts.

Sometimes this happens just because the same analysis was developed separately within different fields and named twice.

So people in different fields use different terms for the same statistical concept.  Try to collaborate with a colleague in a different field and you may find yourself awed by the crazy statistics they’re insisting on.

Other times, there is a level of detail that is implied by one term that isn’t true of the wider, more generic term.  This level of detail is often about how the role of variables or effects affects the interpretation of output. [click to continue…]

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Anatomy of a Normal Probability Plot

Across the bottom are the observed data values, sorted lowest to highest. You can see that just like on the histogram, the values range from about -2.2 to 2.2. (Note, these are standardized residuals, so they already have a mean of 0 and a standard deviation of 1. If they didn’t, the plot would standardize them before plotting).

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R Is Not So Hard! A Tutorial, Part 14: Pie Charts

In Part 14, let’s see how to create pie charts in R. Let’s create a simple pie chart using the pie() command. As always, we set up a vector of numbers and then we plot them.

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R Programming Video: 15 Tips for The Beginner

One of our instructors–David Lillis–recently gave a talk in front of the Wellington R Users Group highlighting 15 Tips for using the R statistical programming language aimed at the beginner. Below is a video recording of his presentation… Send to KindleRelated PostsSPSS, SAS, R, Stata, JMP? Choosing a Statistical Software Package or Two. The 3 […]

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Analysis of Complex Sample Surveys Made Simple

Complex Surveys use a sampling technique other than a simple random sample. Terms you may have heard in this area include cluster sampling, stratified sampling, oversampling, two-stage sampling, and primary sampling unit. Complex Samples require statistical methods that take the exact sampling design into account to ensure accurate results. This webinar, by guest presenter Dr. […]

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R Is Not So Hard! A Tutorial, Part 13: Box Plots

In Part 13, let’s see how to create box plotsin R. Let’s create a simple box plot using the boxplot() command, which is easy to use. First, we set up a vector of numbers and then we plot them.

Boxplots can be created for individual variables or for variables by group.

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R is Not So Hard!, Part 12: Creating Histograms & Setting Bin Widths

In Part 12, let’s see how to create histograms in R. Let’s create a simple histogram using the hist() command, which is easy to use, but actually quite sophisticated.

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When Listwise Deletion works for Missing Data

You may have never heard of listwise deletion for missing data, but you’ve probably used it.

Listwise deletion means that any individual in a data set is deleted from an analysis if they’re missing data on any variable in the analysis.

Although the simplicity of it is a major advantage, it causes big problems in many missing data situations.

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Five Common Relationships Among Three Variables in a Statistical Model

Including Z in the model often leads to the relationship between X and Y becoming more significant because Z has explained some of the otherwise unexplained variance in Y.

An example of this kind of covariate is when an experimental manipulation (X) on response time (Y) only becomes significant when we control for finger dexterity levels (Z).

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Ten Ways Learning a Statistical Software Package is Like Learning a New Language

Someone recently asked me if they need to learn R. In responding, it struck me that this is another way that learning a stat package is like learning a new language.

The metaphor is extremely helpful for deciding when and how to learn a new stat package, and to keep you going when the going gets rough.

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