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Classes and Seminars Learning Modules Frequently Asked Questions Code Fragments (Advanced) Statistical Analyses Data Analysis Examples Textbook Examples (see also Stat Books for Loan on R) Downloadable Books on R Important Links How can I get R? Where can I run R? Installing, Customizing, Updating R Documentation for R packages organized by topical domains Have you seen? Class Notes: Introduction
Multinomial Logistic Regression | R Data Analysis Examples
Multinomial logistic regression is used to model nominal outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables. This page uses the following packages. Make sure that you can load them before trying to run the examples on this page. If you do not have a package installed, run: install.packages("packagename"), or if you see the versio
Choosing the Correct Statistical Test in SAS, Stata, SPSS and R
The following table shows general guidelines for choosing a statistical analysis. We emphasize that these are general guidelines and should not be construed as hard and fast rules. Usually your data could be analyzed in multiple ways, each of which could yield legitimate answers. The table below covers a number of common analyses and helps you choose among them based on the number of dependent var
Ordinal Logistic Regression | R Data Analysis Examples
Introduction The following page discusses how to use R’s polr package to perform an ordinal logistic regression. For a more mathematical treatment of the interpretation of results refer to: How do I interpret the coefficients in an ordinal logistic regression in R? Preparation Make sure that you can load the following packages before trying to run the examples on this page. If you do not have a pa
Latent Class Analysis | Mplus Data Analysis Examples
Hypothetical Scenarios Example 1 You are interested in studying drinking behavior among adults. Rather than conceptualizing drinking behavior as a continuous variable, you conceptualize it as forming distinct categories or typologies. For example, you think that people fall into one of three different types: abstainers, social drinkers and alcoholics. Since you cannot directly measure what categor
Accuracy in Parameter Estimation | Stata Data Analysis Examples
Introduction Not all sample size issues are directly related to power. Accuracy in parameter estimation (AIPE) is also a function of sample size, that is, the larger the sample size the smaller the confidence interval for a parameter estimate. Accuracy in parameter estimation allows you to specify the size of confidence interval you want to achieve and, in return, gives you the sample size needed
Logit Regression | R Data Analysis Examples
Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. This page uses the following packages. Make sure that you can load them before trying to run the examples on this page. If you do not have a package installed, run: install.packages("packagename
FAQ: What are pseudo R-squareds?
FAQ: What are pseudo R-squareds? As a starting point, recall that a non-pseudo R-squared is a statistic generated in ordinary least squares (OLS) regression that is often used as a goodness-of-fit measure. In OLS, where N is the number of observations in the model, y is the dependent variable, y-bar is the mean of the y values, and y-hat is the value predicted by the model. The numerator of the r
FAQ What is complete or quasi-complete separation in logistic/probit regression and how do we deal with them?
Occasionally when running a logistic/probit regression we run into the problem of so-called complete separation or quasi-complete separation. On this page, we will discuss what complete or quasi-complete separation is and how to deal with the problem when it occurs. Notice that the example data set used for this page is extremely small. It is for the purpose of illustration only. What is complete
OARC Stats – Statistical Consulting Web Resources
Statistical Consulting Web Resources
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