Stata software statistik dan analisis di ciptakan pada tahun 1985 oleh Stata Corporation. Sebagian besar penggunanya bekerja dalam penelitian, terutama di bidang Ekonomi, Sosiologi, Ilmu Politik, Biomedis dan Epidemiologi. Kemampuan kinerja Stata mencakup manajemen data, analisis statistik, grafik, simulasi, regresi, dan pemograman custom. Untuk informasi lebih lengkap silahkan anda Click disini Anda akan diarahkan pada situs Resmi Stata Indonesia 

Stata is a complete, integrated statistical software package that provides everything you need for data analysis, data management, and graphics. With this application you can explore, visualize and model your project easily. It will allow you to collect your result into the reproducible reports. Stata has got a point and click interface along with an intuitive command syntax. You can reproduce and document all the analyses for the publication and review. The version control ensures that the scripts for the analyses continue to produce the same result regardless of the fact when they were written. The data management feature will give you more control over all sorts of data.

You can combine as well as reshape the datasets and can also manage the variables. You can point and click for creating a custom graph plus you can also write the scripts in order to produce loads of graphs in reproducible manner. The graphs can be exported to EPS or TIFF for publication to SVG or PNG for the web or PDF files for viewing purpose. To see more complete information Click here You will be directed to the Official Site of the Stata Corporation to see more complete information. 












STATA FEATURES

LINEAR MODELS
• GLS.
• Regression.
• Constraints.
• Quantile regression.
• Censored outcomes.
• Instrumental variables.
• Endogenous regressors.
• Three stage least squares.
• Bootstrap, jackknife, and robust and cluster robust variance.

LONGITUDINAL DATA - PANEL DATA
• GEE.
• Panel unit root tests.
• Linear mixed models.
• Instrumental variables.
• Random effects probit.
• Dynamic panel data models.
• Random and fixed effects Poisson.
• Random and fixed effects with robust standard errors.

MULTI LEVEL MIXED EFFECTS MODELS
• Random slopes.
• DDF adjustments.
• Random intercepts.
• Hierarchical models.
• Crossed random effects.
• Support for survey data.
• Residual error structures.
• Generalized linear models.
• BLUPs of effects and fitted values.
• Two-, three-, and higher level models.
• Continuous, binary, count, and survival outcomes.

BINARY, COUNT, AND LIMITED OUTCOMES
• Marginal effects.
• Selection models.
• Multinomial probit.
• Logistic, probit, tobit.
• Poisson and negative binomial.
• Zero inflated and left truncated count models.
• Conditional, multinomial, nested, ordered, rank ordered, and stereotype logistic.

GENERALIZED LINEAR MODELS
• Seven residuals.
• Ten link functions.
• User defined links.
• Seven distributions.
• ML and IRLS estimation.
• Nine variance estimators.

ANOVA - MANOVA
• Contrasts.
• Marginal means.
• Repeated measures.
• Balanced and unbalanced designs.
• Factorial, nested, and mixed designs.

EXACT STATISTIC
• Binomial tests.
• Exact case control statistics.
• Fisher’s exact test for r × c tables.
• Exact logistic and Poisson regression.

TESTS, PREDICTIONS, AND EFFECTS
• LR tests.
• Wald tests.
• Hausman tests.
• Marginal means.
• Adjusted means.
• Forecast models.
• Least squares means.
• Marginal and partial effects.
• Linear and nonlinear combinations.
• Predictions and generalized predictions.

CONTRASTS, PAIRWISE, COMPARISONS, AND MARGINS
• Interaction plots.
• Orthogonal polynomials.
• Multiple comparison adjustments.
• Compare means, intercepts, or slopes.
• Graph estimated means and contrasts.
• Compare to reference category, adjacent category, grand mean, etc.

GMM AND NON-LINEAR REGRESSION
• Non-linear regression.
• Generalized method of moments (GMM).

SIMPLE MAXIMUM LIKELIHOOD
• Survey data.
• Matrix estimators.
• No programming required.
• Specify likelihood using simple expressions.
• Standard, robust, bootstrap, and jackknife SEs.

PROGRAMMABLE MAXIMUM LIKELIHOOD
• Wald tests.
• Survey data.
• NR, DFP, BFGS, BHHH.
• User specified functions.
• Numeric or analytic derivatives.
• OIM, OPG, robust, bootstrap, and jackknife SEs.

TIME SERIES
• VAR.
• VECM.
• ARIMA.
• ARFIMA.
• Forecasts.
• ARCH/GARCH.
• Correlograms.
• Periodograms.
• Unit root tests.
• Dynamic factors.
• Business calendars.
• State space models.
• Multivariate GARCH.
• Filters and smoothers.
• Markov switching models.
• Impulse response functions.
• Rolling and recursive estimation.
• Unobserved components model.

SURVIVAL ANALYSIS
• Hazards.
• Competing risks.
• Cox regression (frailty).
• Time varying covariates.
• Parametric models (frailty, random effects).
• Kaplan, meier and nelson, aalen estimators.
• Left and right censoring, weibull, exponential, and gompertz models.

BAYESIAN ANALYSIS
• Gibbs sampling.
• Model comparison.
• Hypothesis testing.
• Posterior summaries.
• Add your own models.
• Convergence diagnostics.
• Linear and nonlinear models.
• Thousands of built-in models.
• Univariate and multivariate models.
• Adaptive metropolis hastings sampling.
• Continuous, binary, ordinal, and count outcomes.
• Continuous univariate, multivariate, and discrete priors.

POWER AND SAMPLE SIZE
• Power.
• Means.
• ANOVA.
• Variances.
• Effect size.
• Sample size.
• Proportions.
• Correlations.
• Cohort studies.
• Survival analysis.
• Contingency tables.
• Case control studies.
• Results in tables or graphs.
• Minimum detectable effect.
• Balanced or unbalanced designs.

TREATMENT EFFECTS
• Covariate matching.
• Multilevel treatments.
• Regression adjustment.
• Doubly robust methods.
• Endogenous treatments.
• Propensity score matching.
• ATEs on the treated (ATETs).
• Inverse probability weight (IPW).
• Average treatment effects (ATEs).
• Potential outcome means (POMs).
• Continuous, binary, count, fractional, and survival outcomes.

STRUCTURAL EQUATION MODELING
• Survey data.
• Goodness of fit.
• Correlated data.
• Multilevel models.
• Modification indices.
• Direct and indirect effects.
• Handles MAR data by FIML.
• Random slopes and intercepts.
• Groups and tests of invariance.
• Graphical path diagram builder.
• Standardized and unstandardized estimates.
• Factors scores, empirical Bayes, and other predictions.
• Continuous, binary, count, ordinal, and survival outcomes.

MULTIPLE IMPUTATION
• Predictions.
• Chained equations.
• Transform parameters.
• Manage imputed datasets.
• Fit model and pool results.
• Explore pattern of missingness.
• Multivariate normal imputation.
• Joint tests of parameter estimates.
• Nine univariate imputation methods.

SURVEY METHODS
• DEFF.
• Summary tables.
• Poststratification.
• Predictive margins.
• Multistage designs.
• Means, proportions, ratios, totals.
• Regression, instrumental variables, probit, Cox regression.
• Bootstrap, BRR, jackknife, linearized, and SDR variance estimation.

CLUSTER ANALYSIS
• Dendrograms.
• Stopping rules.
• Hierarchical clustering.
• User extensible analyses.
• Kmeans and kmedian nonhierarchical clustering.

ITEM RESPONSE THEORY
• Test characteristic curves.
• Item characteristic curves.
• Test information functions.
• Item information functions.
• Differential item functioning (DIF).
• Binary (1PL, 2PL, 3PL), ordinal, and categorical response models.

MULTI VARIETE METHODS
• Biplots.
• Rotation.
• Dendrograms.
• Factor analysis.
• Procrustean analysis.
• Discriminant analysis.
• Principal components.
• User extensible analyses.
• Multidimensional scaling.
• Correspondence analysis.

NON-PARAMETRIC METHODS
• Smoothing.
• Survival data.
• ROC analysis.
• Bootstrapping.
• Exact binomial CIs.
• Kolmogorov Smirnov tests.
• Spearman and Kendall correlations.
• Wilcoxon mann whitney, wilcoxon signed ranks, and kruskal wallis tests.

EPIDEMIOLOGY
• Cohort.
• ICD-10.
• ROC analysis.
• Case control.
• Mantel Haenszel.
• Pharmacokinetics.
• Matched case control.
• Standardization of rates.

GRAPHICS
• Pie charts.
• GUI editor.
• Bar charts.
• Line charts.
• Hi-lo charts.
• Scatterplots.
• Survival plots.
• Contour plots.
• Distribution Q–Q plots.
• Nonparametric smoothers.
• Regression diagnostic graphs.

GRAPHICAL USER INTERFACE
• Data Editor.
• Graph Editor.
• Do file Editor.
• Project Manager.
• Variables Manager.
• Clipboard preview tool.
• Multiple preference sets.
• Menus and dialogs for all features.

DOCUMENTATION
• References.
• 23 manuals.
• Quick starts.
• 12,000+ pages.
• Seamless navigation.
• Methods and formulas.
• Thousands of worked examples.

BASIC STATISTICS
• Summaries.
• Correlations.
• Z and t tests.
• Factor variables.
• Cross tabulations.
• Tests of proportions.
• Confidence intervals.
• Equality of variance tests.

OTHER STATISTICAL METHODS
• Cronbach's alpha.
• Tests of normality.
• Stepwise regression.
• Kappa measure of interrater agreement.

FUNCTIONS
• String.
• Statistical.
• Mathematical.
• Date and time.
• Random number.

INTERNET CAPABILITIES
• Ability to install new commands.

USER WRITEN COMMANDS
• User written commands for meta analysis, data management, survival, econometrics.

PROGRAMMING FEATURES
• Plugins.
• Project Manager.
• Command scripting.
• Adding new commands.
• Object oriented programming.
• Menu and dialog box programming.

MATRIX PROGRAMMING MATA
• Optimization.
• String matrices.
• LAPACK engine.
• Decompositions.
• Matrix inversions.
• Interactive sessions.
• Numerical derivatives.
• Real and complex numbers.
• Eigenvalues and eigenvectors.
• Object oriented programming.
• Large scale development projects.
• Interface to Stata datasets and matrices.

COMPATIBLE WITH

Windows Vista | 7 | 8 | 10
All Version : 32 - 64 Bit

Size : 324 MB