with guest instructor Jeff Meyer

Whenever we run an analysis of variance or run a regression one of the first things we do is look at the p-value of our predictor variables to determine whether they are statistically significant. When the variable is statistically significant, did you ever stop and ask yourself how significant it is? [click to continue…]

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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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Linear Models in R: Improving Our Regression Model

Last time we created two variables and used the lm() command to perform a least squares regression on them, and diagnosing our regression using the plot() command..

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Linear Models in R: Diagnosing Our Regression Model

Last time we created two variables and used the lm() command to perform a least squares regression on them, treating one of them as the dependent variable and the other as the independent variable. Here they are again..

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Linear Models in R: Plotting Regression Lines

Today let’s re-create two variables and see how to plot them and include a regression line. We take height to be a variable that describes the heights (in cm) of ten people.

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Models for Repeated Measures Continuous, Categorical, and Count Data

Lately, I’ve gotten a lot of questions about learning how to run models for repeated measures data that isn’t continuous. Mostly categorical. But once in a while discrete counts. A typical study is in linguistics or psychology where..

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