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Stata software statistik dan analisis diciptakan pada tahun 1985 oleh Stata Corporation. Sebagian besar penggunanya bekerja dalam penelitian, terutama dibidang ekonomi, sosiologi, ilmu politik, biomedis dan epidemiologi.
Kemampuan kinerja Stata mencakup manajemen data, analisis statistik, grafik, simulasi, regresi, dan pemograman custom.
Stata is a complete, integrated statistical software package that provides everything you need for data analysis, data management, and graphics.

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FAST, ACCURATE, AND EASY TO USE
With both a point and click interface and a powerful, intuitive command syntax, Stata is fast, accurate, and easy to use.
All analyses can be reproduced and documented for publication and review. Version control ensures statistical programs will continue to produce the same results no matter when you wrote them. See certification results and FDA document compliance for accuracy details.
COMPLETE DATA MANAGEMENT FACILITIES
Stata's data management features give you complete control of all types of data. You can combine and reshape datasets, manage variables, and collect statistics across groups or replicates. You can work with byte, integer, long, float, double, and string variables (including BLOBs and strings up to 2 billion characters).
Stata also has advanced tools for managing specialized data such as survival/duration data, time series data, panel/longitudinal data, categorical data, multiple imputation data, and survey data.


Full



STATA FEATURES
LINEAR MODELS
01. GLS.
02. Regression.
03. Constraints.
04. Quantile regression.
05. Censored outcomes.
06. Instrumental variables.
07. Endogenous regressors.
08. Three stage least squares.
09. Bootstrap, jackknife, and robust and cluster robust variance.
LONGITUDINAL DATA - PANEL DATA
01. GEE.
02. Panel unit root tests.
03. Linear mixed models.
04. Instrumental variables.
05. Random effects probit.
06. Dynamic panel data models.
07. Random and fixed effects Poisson.
08. Random and fixed effects with robust standard errors.
MULTI LEVEL MIXED EFFECTS MODELS
01. Random slopes.
02. DDF adjustments.
03. Random intercepts.
04. Hierarchical models.
05. Crossed random effects.
06. Support for survey data.
07. Residual error structures.
08. Generalized linear models.
09. BLUPs of effects and fitted values.
10. Two-, three-, and higher level models.
11. Continuous, binary, count, and survival outcomes.
BINARY, COUNT, AND LIMITED OUTCOMES
01. Marginal effects.
02. Selection models.
03. Multinomial probit.
04. Logistic, probit, tobit.
05. Poisson and negative binomial.
06. Zero inflated and left truncated count models.
07. Conditional, multinomial, nested, ordered, rank ordered, and stereotype logistic.
GENERALIZED LINEAR MODELS
01. Seven residuals.
02. Ten link functions.
03. User defined links.
04. Seven distributions.
05. ML and IRLS estimation.
06. Nine variance estimators.
ANOVA - MANOVA
01. Contrasts.
02. Marginal means.
03. Repeated measures.
04. Balanced and unbalanced designs.
05. Factorial, nested, and mixed designs.
EXACT STATISTIC
01. Binomial tests.
02. Exact case control statistics.
03. Fisher’s exact test for r × c tables.
04. Exact logistic and Poisson regression.
TESTS, PREDICTIONS, AND EFFECTS
01. LR tests.
02. Wald tests.
03. Hausman tests.
04. Marginal means.
05. Adjusted means.
06. Forecast models.
07. Least squares means.
08. Marginal and partial effects.
09. Linear and nonlinear combinations.
10. Predictions and generalized predictions.
CONTRASTS, PAIRWISE, COMPARISONS, AND MARGINS
01. Interaction plots.
02. Orthogonal polynomials.
03. Multiple comparison adjustments.
04. Compare means, intercepts, or slopes.
05. Graph estimated means and contrasts.
06. Compare to reference category, adjacent category, grand mean, etc.
GMM AND NON-LINEAR REGRESSION
01. Non-linear regression.
02. Generalized method of moments (GMM).
SIMPLE MAXIMUM LIKELIHOOD
01. Survey data.
02. Matrix estimators.
03. No programming required.
04. Specify likelihood using simple expressions.
05. Standard, robust, bootstrap, and jackknife SEs.
PROGRAMMABLE MAXIMUM LIKELIHOOD
01. Wald tests.
02. Survey data.
03. NR, DFP, BFGS, BHHH.
04. User specified functions.
05. Numeric or analytic derivatives.
06. OIM, OPG, robust, bootstrap, and jackknife SEs.
TIME SERIES
01. VAR.
02. VECM.
03. ARIMA.
04. ARFIMA.
05. Forecasts.
06. ARCH/GARCH.
07. Correlograms.
08. Periodograms.
09. Unit root tests.
10. Dynamic factors.
11. Business calendars.
12. State space models.
13. Multivariate GARCH.
14. Filters and smoothers.
15. Markov switching models.
16. Impulse response functions.
17. Rolling and recursive estimation.
18. Unobserved components model.
SURVIVAL ANALYSIS
01. Hazards.
02. Competing risks.
03. Cox regression (frailty).
04. Time varying covariates.
05. Parametric models (frailty, random effects).
06. Kaplan, meier and nelson, aalen estimators.
07. Left and right censoring, weibull, exponential, and gompertz models.
BAYESIAN ANALYSIS
01. Gibbs sampling.
02. Model comparison.
03. Hypothesis testing.
04. Posterior summaries.
05. Add your own models.
06. Convergence diagnostics.
07. Linear and nonlinear models.
08. Thousands of built-in models.
09. Univariate and multivariate models.
10. Adaptive metropolis hastings sampling.
11. Continuous, binary, ordinal, and count outcomes.
12. Continuous univariate, multivariate, and discrete priors.
POWER AND SAMPLE SIZE
01. Power.
02. Means.
03. ANOVA.
04. Variances.
05. Effect size.
06. Sample size.
07. Proportions.
08. Correlations.
09. Cohort studies.
10. Survival analysis.
11. Contingency tables.
12. Case control studies.
13. Results in tables or graphs.
14. Minimum detectable effect.
15. Balanced or unbalanced designs.
TREATMENT EFFECTS
01. Covariate matching.
02. Multilevel treatments.
03. Regression adjustment.
04. Doubly robust methods.
05. Endogenous treatments.
06. Propensity score matching.
07. ATEs on the treated (ATETs).
08. Inverse probability weight (IPW).
09. Average treatment effects (ATEs).
10. Potential outcome means (POMs).
11. Continuous, binary, count, fractional, and survival outcomes.
STRUCTURAL EQUATION MODELING
01. Survey data.
02. Goodness of fit.
03. Correlated data.
04. Multilevel models.
05. Modification indices.
06. Direct and indirect effects.
07. Handles MAR data by FIML.
08. Random slopes and intercepts.
09. Groups and tests of invariance.
10. Graphical path diagram builder.
11. Standardized and unstandardized estimates.
12. Factors scores, empirical Bayes, and other predictions.
13. Continuous, binary, count, ordinal, and survival outcomes.
MULTIPLE IMPUTATION
01. Predictions.
02. Chained equations.
03. Transform parameters.
04. Manage imputed datasets.
05. Fit model and pool results.
06. Explore pattern of missingness.
07. Multivariate normal imputation.
08. Joint tests of parameter estimates.
09. Nine univariate imputation methods.
SURVEY METHODS
01. DEFF.
02. Summary tables.
03. Poststratification.
04. Predictive margins.
05. Multistage designs.
06. Means, proportions, ratios, totals.
07. Regression, instrumental variables, probit, Cox regression.
08. Bootstrap, BRR, jackknife, linearized, and SDR variance estimation.
CLUSTER ANALYSIS
01. Dendrograms.
02. Stopping rules.
03. Hierarchical clustering.
04. User extensible analyses.
05. Kmeans and kmedian nonhierarchical clustering.
ITEM RESPONSE THEORY
01. Test characteristic curves.
02. Item characteristic curves.
03. Test information functions.
04. Item information functions.
05. Differential item functioning (DIF).
06. Binary (1PL, 2PL, 3PL), ordinal, and categorical response models.
MULTI VARIETE METHODS
01. Biplots.
02. Rotation.
03. Dendrograms.
04. Factor analysis.
05. Procrustean analysis.
06. Discriminant analysis.
07. Principal components.
08. User extensible analyses.
09. Multidimensional scaling.
10. Correspondence analysis.
NON-PARAMETRIC METHODS
01. Smoothing.
02. Survival data.
03. ROC analysis.
04. Bootstrapping.
05. Exact binomial CIs.
06. Kolmogorov Smirnov tests.
07. Spearman and Kendall correlations.
08. Wilcoxon mann whitney, wilcoxon signed ranks, and kruskal wallis tests.
EPIDEMIOLOGY
01. Cohort.
02. ICD-10.
03. ROC analysis.
04. Case control.
05. Mantel Haenszel.
06. Pharmacokinetics.
07. Matched case control.
08. Standardization of rates.
GRAPHICS
01. Pie charts.
02. GUI editor.
03. Bar charts.
04. Line charts.
05. Hi-lo charts.
06. Scatterplots.
07. Survival plots.
08. Contour plots.
09. Distribution Q–Q plots.
10. Nonparametric smoothers.
11. Regression diagnostic graphs.
GRAPHICAL USER INTERFACE
01. Data Editor.
02. Graph Editor.
03. Do file Editor.
04. Project Manager.
05. Variables Manager.
06. Clipboard preview tool.
07. Multiple preference sets.
08. Menus and dialogs for all features.
DOCUMENTATION
01. References.
02. 23 manuals.
03. Quick starts.
04. 12,000+ pages.
05. Seamless navigation.
06. Methods and formulas.
07. Thousands of worked examples.
BASIC STATISTICS
01. Summaries.
02. Correlations.
03. Z and t tests.
04. Factor variables.
05. Cross tabulations.
06. Tests of proportions.
07. Confidence intervals.
08. Equality of variance tests.
OTHER STATISTICAL METHODS
01. Cronbach's alpha.
02. Tests of normality.
03. Stepwise regression.
04. Kappa measure of interrater agreement.
FUNCTIONS
01. String.
02. Statistical.
03. Mathematical.
04. Date and time.
05. Random number.
INTERNET CAPABILITIES
01. Ability to install new commands.
USER WRITEN COMMANDS
01. User written commands for meta analysis, data management, survival, econometrics.
PROGRAMMING FEATURES
01. Plugins.
02. Project Manager.
03. Command scripting.
04. Adding new commands.
05. Object oriented programming.
06. Menu and dialog box programming.
MATRIX PROGRAMMING MATA
01. Optimization.
02. String matrices.
03. LAPACK engine.
04. Decompositions.
05. Matrix inversions.
06. Interactive sessions.
07. Numerical derivatives.
08. Real and complex numbers.
09. Eigenvalues and eigenvectors.
10. Object oriented programming.
11. Large scale development projects.
12. Interface to Stata datasets and matrices.
COMPATIBLE WITH
Windows XP Vista 7 8 10
Version 32 - 64 Bit
Size : 295 MB
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UPDATE : STATA STATISTIC 13.1 IC EDITION
Revision : 16 December 2016

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