- Stata Trial Version Mac Software
- Stata Free Version
- Download Stata Free Trial
- Stata Trial For Mac
- Free Stata Trial
- Stata 12 Trial Version
Order by request
Stata is a complete, integrated statistical software package that provides everything you need for data analysis, data management, and graphics. Stata is not sold in pieces, which means you get everything you need in one package. May 27, 2018 Download Stata 11 for free. Stata 11 - Stata is a suite of applications used for data analysis, data management, and graphics. Download Stata 11 15.0 from our website for free. Our antivirus check shows that this download is clean. Stata trial version free download. Stata version 11 free download. Stata 11.zip file. Stata for Mac requires macOS 10.11 (El Capitan) or newer for Macs with Intel processors and macOS 11.0 (Big Sur) or newer for Macs with Apple Silicon. Can I trade datasets with my friends who use Stata for Windows? Stata for Mac requires macOS 10.11 (El Capitan) or newer for Macs with Intel processors and macOS 11.0 (Big Sur) or newer for Macs with Apple Silicon. Can I trade datasets with my friends who use Stata.
Ab einer bestimmten Bestellmenge stellen wir Ihnen ein individuelles Angebot zusammen.
Dieser Artikel wird Ihrem Warenkorb beigelegt, wird jedoch nicht in der Berechnung berücksichtigt
Stata's expertise lies in the analysis of time based data. Stata provides not only the basic time series models like ARIMA but even the multivariate equivalents (VAR/VEC-Models) as well. Further you are able to model volatility using GARCH-models in Stata. Kaplan-Meier-curves are the way to analyse survival times, while mixed models help to analyse panel data. A mighty scripting language completes the package.
Stata produces all kinds of classical statistics. You can use it for descriptive statistics, hypothesis testing and visualization of data. Typically Stata is used in research and development. The large amount of different statistical methods helps scientists in all fields of applications (Social science, econometrics, epidimiology, medical research).
No matter if you are a student or a senior researcher, there is always the right version of Stata available: Stata/IC, Stata/SE and Stata/MP
Arguments for Stata:
- Used in research and development
- Wide range of statistical and graphical methods
- Comprehensive statistical software
- Flexible and especially powerful for analysis of time series
- Easy to learn but mighty scripting language
Recommended products
Stata SE
NLOGIT (includes Limdep)
Limdep
Stata/MP
Stata statistical software is a complete, integrated statistical software 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.
Easy to learn yet fully programmable for the most demanding data management and statistical requirements.
With Stata's menus and dialogs, you can easily point and click or drag and drop your way to all of Stata's statistical, graphical, and data management features. You can completely reshape your data, create group-level variables for panel or longitudinal data, graph a receiver operating characteristics (ROC) curve or impulse-response function (IRF), perform a case-control analysis, estimate a random-effects count-data model or a Cox proportional hazards model, or compute marginal effects from a nonlinear estimator. You can even access the dialog boxes for each command directly from the online help system. T his is a great way to explore all of the capabilities of Stata.
Stata Software is available in 3 different flavours
Whether you're a student or a seasoned research professional, we have a package designed to suit your needs:
- Stata/MP: The fastest version of Stata (for quad-core, dual-core, and multicore/multiprocessor computers) that can analyze the most data
- Stata/SE: Stata for large datasets
- Stata/IC: Stata for mid-sized datasets
- Numerics by Stata: Stata for embedded and web applications
Stata/MP is the fastest and largest version of Stata. Virtually any current computer can take advantage of the advanced multiprocessing of Stata/MP. This includes the Intel i3, i5, i7, i9, Xeon, and Celeron, and AMD multi-core chips. On dual-core chips, Stata/MP runs 40% faster overall and 72% faster where it matters, on the time-consuming estimation commands. With more than two cores or processors, Stata/MP is even faster. Find out more about Stata/MP.
Stata/MP, Stata/SE, and Stata/IC all run on any machine, but Stata/MP runs faster. You can purchase a Stata/MP license for up to the number of cores on your machine (maximum is 64). For example, if your machine has eight cores, you can purchase a Stata/MP license for eight cores, four cores, or two cores.
Stata/MP can also analyze more data than any other flavor of Stata. Stata/MP can analyze 10 to 20 billion observations given the current largest computers, and is ready to analyze up to 1 trillion observations once computer hardware catches up.
Stata/SE and Stata/IC differ only in the dataset size that each can analyze. Stata/SE and Stata/MP can fit models with more independent variables than Stata/IC (up to 10,998). Stata/SE can analyze up to 2 billion observations.
Stata/IC allows datasets with as many as 2,048 variables and 2 billion observations. Stata/IC can have at most 798 independent variables in a model.
Numerics by Stata can support any of the data sizes listed above in an embedded environment.
All the above flavors have the same complete set of features and include PDF documentation.
| Product features | Stata/IC | Stata/SE | Stata/MP |
| Maximum number of variables | 2,048 | 32,767 | 120 |
| Maximum number of observations | 2.14 billion | 2.14 billion | Up to 20 billion |
| Maximum number of independent variables | 798 | 10,998 | 10,998 |
| Multicore support (Time to run logistic regression with 5 million obs and 10 covariates ) | 1-core/ 10.0 sec | 1-core/ 10.0 sec | 2- core (5.0 sec), 4-core (2,6 sec), 4+ core (even faster) |
| Complete suite of statistical features | Yes! | Yes! | Yes! |
| Publication-quality graphics | Yes! | Yes! | Yes! |
| Matrix programming language | Yes! | Yes! | Yes! |
| Complete PDF documentation | Yes! | Yes! | Yes! |
| Exceptional technical support | Yes! | Yes! | Yes! |
| Includes within-release updates | Yes! | Yes! | Yes! |
| 64-bit version available | Yes! | Yes! | Yes! |
| Windows, macOS, and Linux | Yes! | Yes! | Yes! |
| Memory requirements | 1 GB | 2 GB | 4 GB |
| Disk space requirements | 1 GB | 1 GB | 1 GB |
* The maximum number of observations is limited only by the amount of available RAM on your system.
Stata scripting language
Stata's scripting language is easy to learn and helps you to get the most out of your data. It allows not only to use and modify the existing routines to generate standard reports, but can easily be extended with newly created statistical functions.
Efficient Datamanagent with Stata
Datamanagement with Stata is easy and efficient. Joining datasets, creating new variables or producing summary tables is done in no time.
Professional Graphics with Stata
Stata provides professional graphics that can directly be used for documents and publications. This includes not only pre-defined standard graphs but although highly customizeable graphics.
Further Information:
Trialversion of Stata
The producer provides a free 30-day trialversion on their website. The trialversion contains all the features of Stata. You can register for this license simply by visiting the following link: http://www.stata.com/customer-service/evaluate-stata/
| Windows | Mac | Linux | |
| Further Requirements | Stata for Unix requires a video card that can display thousands of colors or more (16-bit or 24-bit color) | ||
| Operating System | Windows XP, Vista, 7, 8, 10, Windows Server 2003, 2008, 2012 (32-/64-Bit) | Mac OS X 10.7 or higher (64-Bit) | Any 64-bit (x86-64 or compatible) or 32-bit (x86 or compatible) running Linux |
| Min. CPU | |||
| Min. RAM | 512 MB | ||
| Disk Space | 900 MB |
Latent class analysis (LCA)Discover and understand the unobserved groupings in your data. Use LCA's model-based classification to find out
|
bayes: logistic .. |
Markdown & dynamic documentsType this,
|
Linearized DSGEsWrite your model in simple algebraic form. Stata does the rest: solve model, estimate parameters, estimate policy and transition matrices (with CIs), estimate and graph IRFs, and perform forecasts. |
Finite mixture models (FMMs)
|
Spatial autoregressive models |
Interval-censored survival modelsFit any of Stata's six parametric survival models to interval-censored data. All the usual survival features are supported: stratified estimation, robust and clustered SEs, survey data, graphs, and more. |
Nonlinear multilevel |
Mixed logit models: Advanced choice modelingDo you walk to work, ride a bus, or drive your car? Which of three insurance plans do you buy? Which political party do you vote for? We make dozens of choices every day. Researchers have access to gaggles of data about those choices. Mixed logit introduces random effects into choice modeling and thereby relaxes the IIA assumption and increases model flexibility. |
Nonparametric regressionWhen you know something matters. But have no idea how. |
Create Word documents from Stata
|
Bayesian multilevel modelsSmall number of groups? Consider Bayesian multilevel modeling. |
Threshold regressionYour time-series regression may change parameters at some point in time or at multiple points in time. The activity of foraging animals might follow a completely different pattern at temperatures above some threshold. You may not know the value of that threshold. Finding such thresholds and estimating the parameters within the regimes is what threshold regression does. |
Panel-data tobit with random coefficientsStata has long had estimators for random effects (random intercepts) in panel data. |
Search, browse, and import FRED dataThe St. Louis Federal Reserve makes available over 470,000 U.S. and international economic and financial time series. You can now easily search, browse, and import these data. |
Multilevel regression for interval-measured outcomesIncomes are sometimes recorded in groupings, as are people's weights, insect counts, grade-point averages, and hundreds of other measures. Often we have repeated measurements for individuals, or schools, or orchards, etc. So .. we need multilevel regression for interval-measured (interval-censored) outcomes. |
Multilevel tobit regression for censored outcomes
|
Panel-data cointegration tests
|
Tests for multiple breaks in time series
|
Multiple-group generalized SEMGeneralized SEM now supports multiple-group analysis. Easily specify groups and test parameter invariance across groups. GSEM models include
|
ICD-10-CM/PCS
|
Power for cluster randomized designsPower analysis for comparing
when you randomize clusters instead of individuals |
Power for linear regression models
|
Heteroskedastic linear regression
|
Poisson models with sample selectionCounts are common. How many: Fish did you catch? Accidents occurred? Patents does a firm generate? Outcomes are not always seen. Folks evade the game warden. Accidents are not always reported. Some firms prefer trade secrets to patents. So you need Poisson models with sample selection. |
More in panel dataNonlinear models with random effects, including random coefficients Bayesian panel-data models Interval regression with random intercepts and random coefficients |
More in graphicsTransparency in graphsSVG export |
More in statisticsBayesian survival models Zero-inflated ordered probit Add your own power and sample-size methods Bayesian sample-selection modelsAnd yet more |
More in the interfaceStata in SwedishStata in ChineseImprovements to the Do-file Editor |
And, even more
The whole feature list you will find under the following link:
Stata Features
Data management
data transformations, match-merge, ODBC, XML, by-group processing, append files, sort, row–column transposition, labeling, saving results
Basic statistics
summaries, cross-tabulations, correlations, t tests, equality-of-variance tests, tests of proportions, confidence intervals, factor variables
Linear models
regression; bootstrap, jackknife, and robust Huber/White/sandwich variance estimates; instrumental variables; three-stage least squares; constraints; quantile regression; GLS
Multilevel mixed-effects models
generalized linear models;continuous, binary, and count outcomes; two-, three-, and higher-level models; random-intercepts; random-slopes; crossed random effects; BLUPs of effects and fitted values; hierarchical models; residual error structures; support for survey data in linear models
Binary, count, and discrete outcomes
logistic, probit, tobit; Poisson and negative binomial; conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic; multinomial probit; zero-inflated and left-truncated count models; selection models; marginal effects
Longitudinal data/panel data
random and fixed effects with robust standard errors; linear mixed models, random-effects probit, GEE, random- and fixed-effects Poisson, dynamic panel-data models, and instrumental-variables regression; panel unit-root tests; AR(1) disturbances
Generalized linear models (GLMs)
ten link functions, user-defined links, seven distributions, ML and IRLS estimation, nine variance estimators, seven residuals
Nonparametric methods
Wilcoxon-Mann-Whitney, Wilcoxon signed ranks and Kruskal-Wallis tests; Spearman and Kendall correlations; Kolmogorov-Smirnov tests; exact binomial CIs; survival data; ROC analysis; smoothing; bootstrapping
Exact statistics
exact logistic and Poisson regression, exact case-control statistics, binomial tests, Fisher's exact test for r × c tables
ANOVA/MANOVA
balanced and unbalanced designs; factorial, nested, and mixed designs; repeated measures; marginal means; contrasts
Multivariate methods
factor analysis, principal components, discriminant analysis, rotation, multidimensional scaling, Procrustean analysis, correspondence analysis, biplots, dendrograms, user-extensible analyses
Cluster analysis
hierarchical clustering; kmeans and kmedian nonhierarchical clustering; dendrograms; stopping rules; user-extensible analyses Gta sa samp money hack cs 1.6.
Resampling and simulation methods
bootstrapping, jackknife and Monte Carlo simulation; permutation tests
Tests, predictions, and effects
Wald tests; LR tests; linear and nonlinear combinations, predictions and generalized predictions, marginal means, least-squares means, adjusted means; marginal and partial effects; forecast models; Hausman tests
Graphics
line charts, scatterplots, bar charts, pie charts, hi-lo charts, regression diagnostic graphs, survival plots, nonparametric smoothers, distribution Q-Q plots
Survey methods
multistage designs; bootstrap, BRR, jackknife, linearized, and SDR variance estimation; poststratification; DEFF; predictive margins; means, proportions, ratios, totals; summary tables; regression, instrumental variables, probit, Cox regression
Survival analysis
Kaplan-Meier and Nelson-Aalen estimators,; Cox regression (frailty); parametric models (frailty); competing risks; hazards; time-varying covariates; left- and right-censoring, Weibull, exponential, and Gompertz analysis
Epidemiology
standardization of rates, case–control, cohort, matched case-control, Mantel-Haenszel, pharmacokinetics, ROC analysis, ICD-9-CM
Time series
ARIMA; ARFIMA; ARCH/GARCH; VAR; VECM; multivariate GARCH; unobserved components model; dynamic factors; state-space models; business calendars; correlograms; periodograms; forecasts; impulse-response functions; unit-root tests; filters and smoothers; rolling and recursive estimation
Panel-data cointegration tests
|
Tests for multiple breaks in time series
|
Multiple-group generalized SEMGeneralized SEM now supports multiple-group analysis. Easily specify groups and test parameter invariance across groups. GSEM models include
|
ICD-10-CM/PCS
|
Power for cluster randomized designsPower analysis for comparing
when you randomize clusters instead of individuals |
Power for linear regression models
|
Heteroskedastic linear regression
|
Poisson models with sample selectionCounts are common. How many: Fish did you catch? Accidents occurred? Patents does a firm generate? Outcomes are not always seen. Folks evade the game warden. Accidents are not always reported. Some firms prefer trade secrets to patents. So you need Poisson models with sample selection. |
More in panel dataNonlinear models with random effects, including random coefficients Bayesian panel-data models Interval regression with random intercepts and random coefficients |
More in graphicsTransparency in graphsSVG export |
More in statisticsBayesian survival models Zero-inflated ordered probit Add your own power and sample-size methods Bayesian sample-selection modelsAnd yet more |
More in the interfaceStata in SwedishStata in ChineseImprovements to the Do-file Editor |
And, even more
The whole feature list you will find under the following link:
Stata Features
Data management
data transformations, match-merge, ODBC, XML, by-group processing, append files, sort, row–column transposition, labeling, saving results
Basic statistics
summaries, cross-tabulations, correlations, t tests, equality-of-variance tests, tests of proportions, confidence intervals, factor variables
Linear models
regression; bootstrap, jackknife, and robust Huber/White/sandwich variance estimates; instrumental variables; three-stage least squares; constraints; quantile regression; GLS
Multilevel mixed-effects models
generalized linear models;continuous, binary, and count outcomes; two-, three-, and higher-level models; random-intercepts; random-slopes; crossed random effects; BLUPs of effects and fitted values; hierarchical models; residual error structures; support for survey data in linear models
Binary, count, and discrete outcomes
logistic, probit, tobit; Poisson and negative binomial; conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic; multinomial probit; zero-inflated and left-truncated count models; selection models; marginal effects
Longitudinal data/panel data
random and fixed effects with robust standard errors; linear mixed models, random-effects probit, GEE, random- and fixed-effects Poisson, dynamic panel-data models, and instrumental-variables regression; panel unit-root tests; AR(1) disturbances
Generalized linear models (GLMs)
ten link functions, user-defined links, seven distributions, ML and IRLS estimation, nine variance estimators, seven residuals
Nonparametric methods
Wilcoxon-Mann-Whitney, Wilcoxon signed ranks and Kruskal-Wallis tests; Spearman and Kendall correlations; Kolmogorov-Smirnov tests; exact binomial CIs; survival data; ROC analysis; smoothing; bootstrapping
Exact statistics
exact logistic and Poisson regression, exact case-control statistics, binomial tests, Fisher's exact test for r × c tables
ANOVA/MANOVA
balanced and unbalanced designs; factorial, nested, and mixed designs; repeated measures; marginal means; contrasts
Multivariate methods
factor analysis, principal components, discriminant analysis, rotation, multidimensional scaling, Procrustean analysis, correspondence analysis, biplots, dendrograms, user-extensible analyses
Cluster analysis
hierarchical clustering; kmeans and kmedian nonhierarchical clustering; dendrograms; stopping rules; user-extensible analyses Gta sa samp money hack cs 1.6.
Resampling and simulation methods
bootstrapping, jackknife and Monte Carlo simulation; permutation tests
Tests, predictions, and effects
Wald tests; LR tests; linear and nonlinear combinations, predictions and generalized predictions, marginal means, least-squares means, adjusted means; marginal and partial effects; forecast models; Hausman tests
Graphics
line charts, scatterplots, bar charts, pie charts, hi-lo charts, regression diagnostic graphs, survival plots, nonparametric smoothers, distribution Q-Q plots
Survey methods
multistage designs; bootstrap, BRR, jackknife, linearized, and SDR variance estimation; poststratification; DEFF; predictive margins; means, proportions, ratios, totals; summary tables; regression, instrumental variables, probit, Cox regression
Survival analysis
Kaplan-Meier and Nelson-Aalen estimators,; Cox regression (frailty); parametric models (frailty); competing risks; hazards; time-varying covariates; left- and right-censoring, Weibull, exponential, and Gompertz analysis
Epidemiology
standardization of rates, case–control, cohort, matched case-control, Mantel-Haenszel, pharmacokinetics, ROC analysis, ICD-9-CM
Time series
ARIMA; ARFIMA; ARCH/GARCH; VAR; VECM; multivariate GARCH; unobserved components model; dynamic factors; state-space models; business calendars; correlograms; periodograms; forecasts; impulse-response functions; unit-root tests; filters and smoothers; rolling and recursive estimation
Multiple imputation
nine univariate imputation methods; multivariate normal imputation; chained equations; explore pattern of missingness; manage imputed datasets; fit model and pool results; transform parameters; joint tests of parameter estimates; predictions
Simple maximum likelihood
specify likelihood using simple expressions; no programming required; survey data; standard, robust, bootstrap, and jackknife SEs; matrix estimators
Programmable maximum likelihood
user-specified functions; NR, DFP, BFGS, BHHH; OIM, OPG, robust, bootstrap, and jackknife SEs; Wald tests; survey data; numeric or analytic derivatives
Other statistical methods
kappa measure of interrater agreement; Cronbach's alpha; stepwise regression; tests of normality
Programming features
Stata Trial Version Mac Software
adding new commands; command scripting; object-oriented programming; menu and dialog-box programming; Project Manager; plugins
Matrix programming-Mata
interactive sessions, large-scale development projects, optimization, matrix inversions, decompositions, eigenvalues and eigenvectors, LAPACK engine, real and complex numbers, string matrices, interface to Stata datasets and matrices, numerical derivatives, object-oriented programming
Internet capabilities
ability to install new commands, web updating, web file sharing, latest Stata news
Accessibility
Section 508 compliance, accessibility for persons with disabilities
Sample session
A sample session of Stata for Mac, Unix, or Windows.
User-written commands
User-written commands for meta-analysis, data management, survival, econometrics
Graphical user interface
menus and dialogs for all features; Data Editor; Variables Manager; Graph Editor; Project Manager; Do-file Editor; Clipboard Preview Tool; multiple preference sets
Graphics
line charts; scatterplots; bar charts; pie charts; hi-lo charts; contour plots; GUI Editor; regression diagnostic graphs; survival plots; nonparametric smoothers; distribution Q-Q plots
Stata Free Version
Documentation
20 manuals20 manuals; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references
Power and sample size
power; sample size; effect size; minimum detectable effect; means; proportions; variances; correlations; case-control studies; cohort studies; survival analysis; balanced or unbalanced designs; results in tables or graphs
Treatment effects
inverse probability weight (IPW); doubly robust methods; propensity score matching; regression adjustment; covariate matching; multilevel treatments; average treatment effects (ATEs); average treatment effects on the treated (ATETs); potential-outcome means (POMs)
SEM (Structural equation modeling)
graphical path diagram builder; standardized and unstandardized estimates; modification indices; direct and indirect effects; continuous, binary, count, and ordinal outcomes (GLM); multilevel models; random slopes and intercepts; factors scores, empirical Bayes, and other predictions; groups and tests of invariance; goodness of fit; handles MAR data by FIML; correlated data
Functions
statistical; random-number; mathematical; string; date and time
Embedded statistical computations
Numerics by Stata
Contrasts, pairwise comparisons, and margins
compare means, intercepts, or slopes; compare to reference category, adjacent category, grand mean, etc.; orthogonal polynomials; multiple comparison adjustments; graph estimated means and contrasts; interaction plots
Download Stata Free Trial
GMM an nonlinear regression
generalized method of moments (GMM); nonlinear regression
Stata Trial For Mac
This page provides instructions on how to install IBM SPSS Statistics on a computer running Mac OS X 10.10 (Yosemite), Mac OS X 10.11 (El Capitan), or Mac OS X 10.12 (Sierra).
Installation Instructions
Free Stata Trial
- Install the IBM SPSS Statistics file you downloaded from software.usc.edu/spss.
- Double-click the SPSS Statistics Installer icon on your desktop.
- You may need to authorize the installer. Enter your computer's login name and password and then click Install Helper to authorize the installer.
- On the IBM SPSS Statistics screen, click OK.
- On the IBM SPSS Statistics – Introduction screen, click Next.
- On the IBM SPSS Statistics – Software License Agreement screen, select I accept the terms in the license agreement and click Next.
- On the IBM SPSS Statistics – Install Python Essentials screen, under Install IBM SPSS Statistics – Essentials for Python, select Yes. Click Next to continue to the next screen.
- The following license agreement screen will appear. Select I accept these terms of the License Agreement and click Next to continue with the installation.
- The IBM SPSS Statistics – License Agreement for Essentials for Python screen will appear , select I accept the terms in the license agreement and click Next to continue the installation.
- On the IBM SPSS Statistics – Choose Install Folder screen, choose the location where you would like to install SPSS. Click Next.
- On the IBM SPSS Statistics – Pre-Installation Summary screen, click Install.
- SPSS will now install. This process takes approximately 2-5 minutes.
- The IBM SPSS Statistics – Install Complete screen will appear, click Done to finish the installation process and launch IBM SPSS Statistics.
- When you launch IBM SPSS Statistics for the first time, you will see the following screen. Click License Product to enter the license key you received from ITS and authorize your copy of IBM SPSS Statistics. NOTE: You must be connected to the Internet to successfully complete the authorization process.
- The License Status screen will appear. Click Next.
- On the Product Authorization screen, choose Authorized user license (I purchased a single copy of the product). and then click Next.
- On the Enter Codes screen, enter your authorization code. When purchased your software from ITS, you should have received an email with this authorization code.
- Once the authorization process is complete on the Internet Authorization Status screen, click Next.
- On the Licensing Completed screen, click Finish to complete the installation.
Stata 12 Trial Version
NOTE: ITS suggests you open IBM SPSS Statistics once the installation and authorization has finished to ensure that the software has installed correctly.
