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Friday, 31 July 2015

Data Analysis Examples










The pages below contain examples (often hypothetical) illustrating the application of different statistical analysis techniques using different statistical packages. Each page provides a handful of examples of when the analysis might be used along with sample data, an example analysis and an explanation of the output, followed by references for more information. These pages merely introduce the essence of the technique and do not provide a comprehensive description of how to use it.
The combination of topics and packages reflect questions that are often asked in our statistical consulting. As such, this heavily reflects the demand from our clients at walk in consulting, not demand of readers from around the world. Many worthy topics will not be covered because they are not reflected in questions by our clients. Also, not all analysis techniques will be covered in all packages, again largely determined by client demand. If an analysis is not shown in a particular package,this does not imply that the package cannot do the analysis, it may simply mean that the analysis is not commonly done in that package by our clients.

StataSASSPSSMplusR
Regression Models
Robust RegressionStataSASR
Models for Binary and Categorical Outcomes
Logistic RegressionStataSASSPSSMplusR
Exact Logistic RegressionStataSASR
Multinomial Logistic RegressionStataSASSPSSMplusR
Ordinal Logistic RegressionStataSASSPSSMplusR
Probit RegressionStataSASSPSSMplusR
Count Models
Poisson RegressionStataSASSPSSMplusR
Negative Binomial RegressionStataSASSPSSMplusR
Zero-inflated Poisson RegressionStataSASMplusR
Zero-inflated Negative Binomial RegressionStataSASMplusR
Zero-truncated PoissonStataSASR
Zero-truncated Negative BinomialStataSASMplusR
Censored and Truncated Regression
Tobit RegressionStataSASMplusR
Truncated RegressionStataSASR
Interval RegressionStataSASR
Multivariate Analysis
One-way MANOVAStataSASSPSS
Discriminant Function AnalysisStataSASSPSS
Canonical Correlation AnalysisStataSASSPSSR
Multivariate Multiple RegressionStataSASMplus
Mixed Effects Models
Generalized Linear Mixed ModelsIntroduction to GLMMs
Mixed Effects Logistic RegressionStataR
Other
Latent Class AnalysisMplus

See more:  http://www.ats.ucla.edu/stat/dae/