Stata is a complete, integrated statistical package that provides everything you need for data analysis, data management, and graphics. Stata is not sold in modules, which means you get everything you need in one package. And, you can choose a perpetual license, with nothing more to buy ever. Annual licenses are also available.Stata 12 adds many new features such as structural equation modeling (SEM), contrasts, ARFIMA, business calendars, chained equations for multiple imputation, contour plots, automatic memory management, importing and exporting of Excel files, and more. (Source:

References in zbMATH (referenced in 290 articles , 1 standard article )

Showing results 1 to 20 of 290.
Sorted by year (citations)

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  1. Anne Petersen; Claus Ekstrøm: dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R (2019) not zbMATH
  2. Ann-Kristin Kreutzmann; Sören Pannier; Natalia Rojas-Perilla; Timo Schmid; Matthias Templ; Nikos Tzavidis: The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators (2019) not zbMATH
  3. Araar, Abdelkrim (ed.); Verme, Paolo (ed.): Prices and welfare. An introduction to the measurement of well-being when prices change (2019)
  4. Cleff, Thomas: Applied statistics and multivariate data analysis for business and economics. A modern approach using SPSS, Stata, and Excel (2019)
  5. Daniel Sabanés Bové, Wai Yin Yeung, Giuseppe Palermo, Thomas Jaki: Model-Based Dose Escalation Designs in R with crmPack (2019) not zbMATH
  6. Das, Panchanan: Econometrics in theory and practice. Analysis of cross section, time series and panel data with Stata 15.1 (2019)
  7. Drton, Mathias; Fox, Christopher; Wang, Y. Samuel: Computation of maximum likelihood estimates in cyclic structural equation models (2019)
  8. Gerhard Kurz; Igor Gilitschenski; Florian Pfaff; Lukas Drude; Uwe Hanebeck; Reinhold Haeb-Umbach; Roland Siegwart: Directional Statistics and Filtering Using libDirectional (2019) not zbMATH
  9. Kaufman, Robert L.: Interaction effects in linear and generalized linear models. Examples and applications using Stata (2019)
  10. Mihai Tivadar: OasisR: An R Package to Bring Some Order to the World of Segregation Measurement (2019) not zbMATH
  11. Sebastian Calonico; Matias Cattaneo; Max Farrell: nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference (2019) not zbMATH
  12. Shieh, Gwowen: Effect size, statistical power, and sample size for assessing interactions between categorical and continuous variables (2019)
  13. Stockemer, Daniel: Quantitative methods for the social sciences. A practical introduction with examples in SPSS and Stata (2019)
  14. Thomas Jaki; Philip Pallmann; Dominic Magirr: The R Package MAMS for Designing Multi-Arm Multi-Stage Clinical Trials (2019) not zbMATH
  15. Twisk, Jos W. R.: Applied mixed model analysis. A practical guide (2019)
  16. Yiyun Shou and Michael Smithson: cdfquantreg: An R Package for CDF-Quantile Regression (2019) not zbMATH
  17. Agresti, Alan: An introduction to categorical data analysis (2018)
  18. Alberto Garcia-Hernandez; Dimitris Rizopoulos: %JM: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses (2018) not zbMATH
  19. Daniel Heck and Morten Moshagen: RRreg: An R Package for Correlation and Regression Analyses of Randomized Response Data (2018) not zbMATH
  20. Delia Voronca; Mulugeta Gebregziabher; Valerie Durkalski-Mauldin; Lei Liu; Leonard Egede: MTPmle: A SAS Macro and Stata Programs for Marginalized Inference in Semi-Continuous Data (2018) not zbMATH

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