Why We Needed a Random Sample of 6 numbers between 1 and 10000

As you may have read in one of our recent newsletters, this month The Analysis Factor hit two milestones:

  1. 10,000 subscribers to our mailing list
  2. 6 years in business.

We’re quite happy about both, and seriously grateful to all members of our community.

So to celebrate and to say thanks, we decided to do a giveaway to 6 randomly-chosen  newsletter subscribers.

I just sent emails to the 6 winners this morning.

How We Randomly Generated 6 Equally Likely Values out of 10000 Using R Commander [click to continue…]

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Spotlight Analysis for Interpreting Interactions

Not too long ago, a client asked for help with using Spotlight Analysis to interpret an interaction in a regression model.

Spotlight Analysis? I had never heard of it.

As it turns out, it’s a (snazzy) new name for an old way of interpreting an interaction between a continuous and a categorical grouping variable in a regression model…

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When a Variable’s Level of Measurement Isn’t Obvious

A central concept in statistics is level of measurement of variables. It’s so important to everything you do with data that it’s usually taught within the first week in every intro stats class. But even something so fundamental can be tricky once you start working with real data…

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Classification and Regression Trees Webinar

Cluster analysis classifies individuals into two or more unknown groups based on a set of numerical variables.

It is related to, but distinct from, a few other multivariate techniques including discriminant Function Analysis..

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Generalized Linear Models in R, Part 3: Plotting Predicted Probabilities

We continue with the same glm on the mtcars data set (regressing the vs variable on the weight and engine displacement).

Now we want to plot our model, along with the observed data.

Although we ran a model with multiple predictors, it can help interpretation to plot the predicted probability that vs=1 against each predictor separately. So first we fit

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Generalized Linear Models in R, Part 2: Understanding Model Fit in Logistic Regression Output

by David Lillis, Ph.D. In the last article, we saw how to create a simple Generalized Linear Model on binary data using the glm() command. We continue with the same glm on the mtcars data set Send to KindleRelated PostsMixed Models for Logistic Regression in SPSS Chi-square test vs. Logistic Regression: Is a fancier test […]

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Generalized Linear Models in R, Part 1: Calculating Predicted Probability in Binary Logistic Regression

Ordinary Least Squares regression provides linear models of continuous variables. However, much data of interest to statisticians and researchers are not continuous and so other methods must be used to create useful predictive models.

The glm() command is designed to perform generalized linear models (regressions) on binary outcome data, count data, probability data, proportion data and many other data types.

In this blog post, we explore the use of R’s glm() command on one such data type. Let’s take a look at a simple example where we model binary data.

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SPSS Procedures for Logistic Regression

Logistic Regression can be used only for binary dependent variables. It can be invoked using the menu choices at right or through the LOGISTIC REGRESSION syntax command.

The dependent variable must have only two values. If you specify a variable with more than two, you’ll get an error.

One big advantage of this procedure is it allows you to build successive models by entering a group of predictors at a time.

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This Month’s Membership Webinar: Cluster Analysis–Hierarchical and KMeans

Cluster analysis classifies individuals into two or more unknown groups based on a set of numerical variables.

It is related to, but distinct from, a few other multivariate techniques including discriminant Function Analysis..

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What’s in a Name? Moderation and Interaction, Independent and Predictor Variables

When we talk about moderation, though, there is a specific role to X and Z. One is assigned as the Independent Variable and the other as the Moderator.

The Independent Variable is an independent variable based on the third implication listed above: its effect is of primary interest.

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