This free, one-hour webinar is part of our regular Craft of Statistical Analysis series. In it, we will introduce and demonstrate two of the core concepts of mixed modeling—the random intercept and the random slope.

Most scientific fields now recognize the extraordinary usefulness of mixed models, but they’re a tough nut to crack for someone who didn’t receive training in their methodology.

But it turns out that mixed models are actually an extension of linear models. If you have a good foundation in linear models, the extension to mixed models is more of a step than a leap. (Okay, a large step, but still).

You’ll learn what random intercepts and slopes mean, what they do, and how to decide if one or both are needed. It’s the first step in understanding mixed modeling.

Date: Friday, August 21, 2015
12pm EDT (New York time)

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Using the Collapse Command in Stata

Have you ever worked with a data set that had so many observations and/or variables that you couldn’t see the forest for the trees? You would like to extract some simple information but you can’t quite figure out how to do it. Get to know Stata’s collapse command…

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Five Tips and Tricks: How to Make Stata Easier to Use

Stata allows you to describe, graph, manipulate and analyze your data in countless ways. But at times (many times) it can be very frustrating trying to create even the simplest results. Join us and learn how to reduce your future frustrations.This one hour demonstration is for new and intermediate users of Stata. If you’re a […]

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Using Stored Calculations in Stata to Center Predictors: an Example

One of Stata’s incredibly useful abilities is to temporarily store calculations from commands. Why is this so useful?

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July 2015 Membership Webinar: An Overview of Effect Size Statistics and Why They are So Important

In the webinar we will travel beyond “statistical significance” to “practical significance”, “how big of a difference” rather than “is there a difference”.

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Measures of Predictive Models: Sensitivity and Specificity

A perfectly accurate test would put every transaction into boxes a and d. Thieves are stopped but customers are not.

A test that is so bad it’s worthless would have a lot of b’s (angry customers without groceries) and c’s (happy thieves with groceries) and possibly both.

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June 2015 Membership Webinar: A Gentle Introduction to Bayesian Data Analysis

In this webinar, we will review the interpretation of p-values and see an alternative approach based on Bayesian data analysis.

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Effect Size Statistics in Logistic Regression

Many of the common effect size statistics, like eta-squared and Cohen’s d, can’t be calculated in a logistic regression model. So now what do you use?

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What is a Logit Function and Why Use Logistic Regression?

One of the big assumptions of linear models is that the residuals are normally distributed. This doesn’t mean that Y, the response variable, has to also be normally distributed, but it does have to be continuous, unbounded and measured on an interval or ratio scale.

Unfortunately, categorical response variables are none of these.

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May 2015 Membership Webinar: Transformations & Nonlinear Effects in Linear Models

Why is it we can model non-linear effects in linear regression? What the heck does it mean for a model to be “linear in the parameters?”

In this webinar we will explore a number of ways of using a linear regression to model a non-linear effect between X and Y.

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