Features. Penjualan barang (harga terakhir pelanggan, modal brg sekali klik). Pembelian barang (retur barang, fasilitas kredit). Laporan keuangan (omset, untung, arus kas).
multi user (unlimited). 10 klien aktif (Penggunaan sangat mudah) ada klien yg cuma lulus smp bisa pake!. Bisa di edit sesuai kebutuhan klien. Log user (apa saja yg dilakukan user akan terekam ex: edit, hapus data). Support barcode scan. Lacak riwayat penjualan/pembelian barang (mudah nge-cek loss barang). Sistem 'link', memudahkan penjualan barang by pcs walaupun masuknya dalam satuan dus/bal.
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. 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. STATA FEATURES LINEAR MODELS 01. Quantile regression. Censored outcomes. Instrumental variables. Endogenous regressors.
Three stage least squares. Bootstrap, jackknife, and robust and cluster robust variance.
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LONGITUDINAL DATA - PANEL DATA 01. 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 01. 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.
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BINARY, COUNT, AND LIMITED OUTCOMES 01. 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 01. Seven residuals. Ten link functions. User defined links. Seven distributions. ML and IRLS estimation.
Nine variance estimators. ANOVA - MANOVA 01. Marginal means. Repeated measures. Balanced and unbalanced designs. Factorial, nested, and mixed designs.
EXACT STATISTIC 01. Binomial tests. Exact case control statistics.
Fisher’s exact test for r × c tables. Exact logistic and Poisson regression. TESTS, PREDICTIONS, AND EFFECTS 01. 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 01. 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 01. Non-linear regression.
Generalized method of moments (GMM). SIMPLE MAXIMUM LIKELIHOOD 01. Matrix estimators. No programming required. Specify likelihood using simple expressions. Standard, robust, bootstrap, and jackknife SEs.
PROGRAMMABLE MAXIMUM LIKELIHOOD 01. NR, DFP, BFGS, BHHH. User specified functions. Numeric or analytic derivatives. OIM, OPG, robust, bootstrap, and jackknife SEs. TIME SERIES 01. 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 01. 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 01. 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 01. Cohort studies. Survival analysis. Contingency tables.
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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 01.
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 01. 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 01. Summary tables. Predictive margins. Multistage designs.
Means, proportions, ratios, totals. Regression, instrumental variables, probit, Cox regression. Bootstrap, BRR, jackknife, linearized, and SDR variance estimation. CLUSTER ANALYSIS 01. Stopping rules. Hierarchical clustering. User extensible analyses.
Kmeans and kmedian nonhierarchical clustering. ITEM RESPONSE THEORY 01. Test characteristic curves.
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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 01. Factor analysis. Procrustean analysis.
Discriminant analysis. Principal components. User extensible analyses. Multidimensional scaling. Correspondence analysis. NON-PARAMETRIC METHODS 01.
Survival data. ROC analysis. Exact binomial CIs.
Kolmogorov Smirnov tests. Spearman and Kendall correlations. Wilcoxon mann whitney, wilcoxon signed ranks, and kruskal wallis tests. EPIDEMIOLOGY 01. ROC analysis.
Case control. Mantel Haenszel. Matched case control.
Standardization of rates. Hi-lo charts. Survival plots.
Contour plots. Distribution Q–Q plots. Nonparametric smoothers. Regression diagnostic graphs. GRAPHICAL USER INTERFACE 01. Graph Editor.
Do file Editor. Project Manager. Variables Manager.
Clipboard preview tool. Multiple preference sets. Menus and dialogs for all features. DOCUMENTATION 01. Quick starts. 12,000+ pages. Seamless navigation.
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Methods and formulas. Thousands of worked examples. BASIC STATISTICS 01. Z and t tests. Factor variables. Cross tabulations.
Tests of proportions. Confidence intervals.
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Equality of variance tests. OTHER STATISTICAL METHODS 01. Cronbach's alpha. Tests of normality. Stepwise regression. Kappa measure of interrater agreement. FUNCTIONS 01.
Date and time. Random number. INTERNET CAPABILITIES 01. Ability to install new commands. USER WRITEN COMMANDS 01.
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User written commands for meta analysis, data management, survival, econometrics. PROGRAMMING FEATURES 01. Project Manager.
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Command scripting. Adding new commands. Object oriented programming. Menu and dialog box programming. MATRIX PROGRAMMING MATA 01.
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String matrices. LAPACK engine. Matrix inversions. Interactive sessions. Numerical derivatives.
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