Minitab → columna coverage

Status legend:

Status	Meaning
есть	API covers the usual Minitab workflow for this tool (numbers / options close enough for production use)
MVP	Usable core exists; missing depth, variants, auto-options, or report-grade parity
нет	Not implemented (or only a distant substitute)

Scope: Stat / Quality / DOE / Predictive analytic tools. Graph Builder, Assistant guided UX, Session reports, and project/worksheet UI are out of scope for @columna/advanced (marked нет as product features).

Overall estimate (2026-09): ~75–85% of typical QC/engineering Minitab use; ~50–60% of full Minitab Statistical Software surface.

Stat › Basic Statistics

Minitab	Status	columna
Display Descriptive Statistics	есть	`descriptiveStats` / `df.descriptiveStats` — full Minitab table (N, N*, SE, CoefVar, quartiles, mode, skew, kurt, MSSD, trimmed; by group); Minitab quartile definition (p(n + 1)) also in the engine: `col('x').quantile(p, 'minitab')`, `df.describe({ quantileMethod: 'minitab' })`, `quantile(x, p)`
Store Descriptive Statistics	есть	`descriptiveStats` rows (one per variable × level)
1-Sample Z	есть	`ztest1` / `df.ztest`
1-Sample t	есть	`ttest1` / `df.ttest`
2-Sample t	есть	`ttest2` / `df.ttest({ by })`
Paired t	есть	`ttestPaired` / `df.ttest({ paired })`
1 Proportion	есть	`propTest1`
2 Proportions	есть	`propTest2`
1-Sample Poisson Rate	есть	`poissonRateTest1`
2-Sample Poisson Rate	есть	`poissonRateTest2`
1 Variance	есть	`varTest1` (χ² / Bonett)
2 Variances	есть	`varTest2`, `levene`, `bartlett`, `bonett2`
Correlation	есть	`corrTest` (Pearson / Spearman / Kendall + CI); `partialCorr` / `df.partialCorr`
Graphical Summary	есть	`graphicalSummary` — descriptives + A-D + CIs + histogram / boxplot data
Goodness-of-Fit Test for Poisson	есть	`poissonGof`
Covariance	есть	`df.cov()` (standard)
Normality Test	есть	AD, RJ, KS, Shapiro, Jarque–Bera, D’Agostino K², Cramér–von Mises / `normalityTest`
Outlier Test	есть	`grubbs`, `dixon` / `df.outlierTest`

Stat › Nonparametrics

Minitab	Status	columna
1-Sample Sign	есть	`signTest`
1-Sample Wilcoxon	есть	`wilcoxonSigned`
Mann-Whitney	есть	`mannWhitney`
Kruskal-Wallis	есть	`kruskal`
Mood’s Median Test	есть	`moodMedian`
Friedman	есть	`friedman`
Runs Test	есть	`runsTest`

Stat › ANOVA / GLM

Minitab	Status	columna
One-Way ANOVA	есть	`anova`
Two-Way ANOVA / ANCOVA	MVP	`anovaTwoWay`, `ancova` (Type III via `linearModel`)
Balanced ANOVA	есть	`linearModel` (balanced designs: Type III = Type I) + `nestedAnova` EMS
General Linear Model	есть	`linearModel` (Type I/III, effect coding)
Mixed Effects / Random	MVP	`mixedModel` RI (+ RS / nested / crossed `group2`, correlated `rho`); `glmm` PQL/Laplace/AGQ (+ optional RS, correlated AGQ slope ρ)
Fully Nested ANOVA	MVP	`nestedAnova` EMS/F + `mixedModel({ groupNested })`
MANOVA	есть	one-way `manova` + general `manovaModel(data, responses, 'a*b + x')` (Type III SSCP, 4 statistics, univariate F)
Comparisons: Tukey	есть	`tukeyHSD`
Comparisons: Fisher LSD	есть	`fisherLSD`
Comparisons: Dunnett	есть	`dunnett`
Comparisons: Hsu MCB	есть	`hsuMCB`
Comparisons: other (Games-Howell, …)	есть	`gamesHowell`
Main Effects / Interaction Plots	есть	`mainEffectsPlot`, `interactionPlot`, `intervalPlot` (plot data)
Test for Equal Variances	есть	Levene / Bartlett / Bonett

Stat › Regression

Minitab	Status	columna
Fitted Line Plot	есть	`fittedLine`
Regression	есть	`ols` / `df.regress` (+ diagnostics)
Stepwise	есть	`stepwise`
Best Subsets	есть	`bestSubsets`
Binary Logistic	есть	`glm` / `logit` (logit/probit/cloglog)
Ordinal Logistic	есть	`ologit`
Nominal Logistic	есть	`mlogit`
Poisson Regression	есть	`poissonRegression`
Nonlinear Regression	есть	`nls`
Orthogonal Regression	есть	`orthogonalRegression`
Partial Least Squares	есть	`pls`
Stability Study	есть	`stabilityStudy` — model pooling at α = 0.25, shelf life per batch / overall
Response Optimizer	MVP	`responseOptimizer` (Derringer–Suich; also under DOE)
Predict	MVP	`predict()` on fits; no Session report UI

Stat › Tables / Equivalence / Power

Minitab	Status	columna
Chi-Square Test (tables)	есть	`chi2test`, `crosstab`
Chi-Square Goodness-of-Fit	есть	`chi2gof`
Cross Tabulation	есть	`crosstab`
Equivalence Tests (TOST)	есть	`tost1` / `tost2` / `tostPaired`
Power and Sample Size	есть	`power` (9 Minitab tests)

Stat › Quality Tools

Minitab	Status	columna
Capability Analysis (Normal)	есть	`capability`
Capability (Nonnormal)	MVP	Box–Cox / Johnson / Weibull fits; not full Minitab nonnormal report
Capability Sixpack	MVP	`capabilitySixpack` plot-ready panels (no renderer)
Tolerance Intervals	есть	`toleranceInterval`
Individual Distribution Identification	есть	`individualDistributionID`
Pareto Chart	MVP	`pareto` (counts/weights; no chart object)
Cause-and-Effect	есть	`causeAndEffect` — structure, layout, SVG
Multi-Vari Chart	MVP	`multiVari`
Symmetry Plot / Test	MVP	`symmetryTest`
Run Chart	есть	`runChart`
Acceptance Sampling	есть	`acceptanceSampling`
Attribute Agreement Analysis	есть	`attributeAgreement`
Gage R&R (Crossed/Nested)	есть	`gageRR`
Gage Linearity and Bias	есть	`gageLinearity`
Gage Run Chart / Type 1	MVP	`gageType1`

Stat › Control Charts

Minitab	Status	columna
I-MR	есть	`controlChart`
X̄-R / X̄-S	есть	`controlChart`
Z-MR	есть	`controlChart`
P / NP / C / U	есть	attribute charts
Laney P′ / U′	есть	Laney
EWMA	есть	`ewma`
CUSUM	есть	`cusum` (+ V-mask params)
Moving Average	есть	`movingAverage`
Rare Event: G / T charts	есть	`gChart`, `tChart`
Multivariate: T² / MEWMA / Generalized Variance	есть	`t2Chart`, `mewma`, `generalizedVarianceChart`
Box-Cox / Johnson transform in chart	MVP	transforms exist separately
Nelson rules 1–8	есть	in `controlChart`

Stat › Time Series

Minitab	Status	columna
Trend Analysis	есть	`trendAnalysis`
Decomposition	есть	`decompose`
STL	MVP	`stl` Cleveland LOESS; multi-season via `{ periods: number[] }`
Moving Average / Single Exp Smoothing	есть	via `ets` SES / MA helpers
Double Exp Smoothing	есть	`ets` DES
Winters’ Method	MVP	`ets` winters-add/mul + PI
ACF / PACF / CCF	есть	`acf`, `pacf`, `ccf`
Autocorrelation (Ljung–Box)	есть	`ljungBox`
ARIMA	MVP	`arima` CSS-ML; `method: 'ML'` Kalman (incl. seasonal expand); `p,q,P,Q ≤ 5`
Seasonal ARIMA	MVP	`arima({ seasonal })` CSS-ML or ML
ARIMAX / with predictors	MVP	`xreg` / `transfer` ω(B)+δ(B); `transferIdentify`; ML + transfer (Kalman mean = μ + TF)
Auto ARIMA	MVP	`autoArima` stepwise AIC/BIC
Difference / Lag	MVP	via Expr / internal diffs
Spectral Analysis	есть	`periodogram`, `cumulativePeriodogram`

Stat › DOE

Minitab	Status	columna
Create Factorial Design	есть	`fullFactorial`, `fractionalFactorial`
Create Plackett-Burman	есть	`plackettBurman`
Create Response Surface (CCD/BBD)	есть	`ccd`, `boxBehnken`
Create Taguchi Design	MVP	`taguchi` L4–L243 (incl. L20–L48 rare + L108/L121/L128)
Analyze Factorial / RSM	MVP	`analyzeEffects`, `analyzeDoe`, `fitDesign`
Analyze Taguchi Design	MVP	`analyzeTaguchi` (S/N, response tables)
Alias Structure	есть	`aliasStructure(k, generators)` — defining relation, resolution, alias chains
Definitive Screening	MVP	`definitiveScreening`
Mixture Designs	MVP	`mixtureDesign` + `analyzeMixture` (Scheffé ± process vars)
Response Optimizer	MVP	`responseOptimizer` (Derringer–Suich)
Full OA catalog (rare arrays)	MVP	`taguchi` L4–L243 (standard + extended set); exotic arrays beyond that are not tabulated

Stat › Reliability / Survival

Minitab	Status	columna
Distribution Analysis (Right Censoring)	MVP	`reliabilityFit` Weibull/lognormal/exp
Parametric Distribution Analysis	MVP	MLE + SE/CI; left/interval partial
Nonparametric (Kaplan–Meier)	есть	`kaplanMeier`
Distribution ID Plot	MVP	`probabilityPlot` + IDI elsewhere
Warranty Analysis	MVP	`warrantyPrediction`
Test Plans	есть	`demonstrationTestPlan`, `estimationTestPlan`
Regression with Life Data	есть	`lifeRegression` — AFT (Weibull / lognormal / loglogistic / exponential / SEV / normal / logistic), censoring, percentiles
Probit Analysis	есть	`probitAnalysis` — ED_p with Fieller limits, natural response
Repairable Systems	есть	`powerLawNHPP` — Crow–AMSAA, trend tests, TTT
Survival: Log-Rank	есть	`logRank` (logrank / Wilcoxon / Tarone–Ware + strata)
Cox Regression	MVP	`coxPH` (+ cluster SE; cause-specific; piecewise / AR(1) TV frailty)
Fine–Gray / CIF	MVP	`fineGray` subdistribution + CIF
Accelerated Life Testing	есть	`altRegression` — Arrhenius / inverse power / exponential, use-condition percentiles

Stat › Multivariate

Minitab	Status	columna
PCA	есть	`pca`
Factor Analysis	есть	`factorAnalysis` PCA + ML + varimax, `promax`
Cluster Observations	MVP	`kmeans`, `hclust`
Cluster Variables	есть	`clusterVariables` (+ `cutTree`)
Discriminant Analysis	есть	`discriminant` LDA/QDA
Simple Correspondence	есть	`correspondence`
Multiple Correspondence	есть	`multipleCorrespondence` (indicator / Burt)
Item Analysis / Cronbach	есть	`itemAnalysis`
MANOVA	MVP	one-way `manova` (also under ANOVA)

Stat › Predictive Analytics

Minitab	Status	columna
CART Classification / Regression	MVP	`cart`
Random Forests	MVP	`randomForest`
TreeNet	MVP	`treeNet` reg + classification
MARS	MVP	`mars` + GCV prune; no interactions degree
Automated Machine Learning	есть	`autoModel` — CV-ranked OLS / CART / RF / TreeNet / MARS (logistic for binary)
Model validation / Discover reports	есть	`crossValidate` — k-fold metrics, out-of-fold predictions

Assistant / Graphs / Product

Minitab	Status	columna
Assistant (guided analyses)	нет	—
Graph Builder / Graph menu	MVP	`plotSeries` + `renderPlotSeries` (static + interactive SVG lite); no product Graph Builder
Session / ReportPad	нет	—
Worksheet / Project files	нет	DataFrame IO instead
macros / exec	нет	JS/TS API

Summary counts (analytic tools only)

Approximate tallies from the tables above (Assistant/Graphs excluded from “stats core” denominator):

Status	~Count	Share of listed analytic tools
есть	~101	~76%
MVP	~28	~21%
нет	~4	~3% (Full OA catalog beyond L243, Multiple-season STL, transfer-function noise joint ML, crossed-AGQ GLMM)

(Assistant/Graphs/Product rows excluded; those remain нет as product features. Every remaining MVP has a working core.)

Weighted by everyday QC use (Basic + ANOVA + Regression + SPC + Capability + survival basics), effective coverage is closer to 80%+.

Menu	Level
Basic Statistics / Nonparametrics / Power	high (есть-heavy)
ANOVA / Regression / GLM	high; Mixed/GLMM MVP (nested + NB); MANOVA MVP
Quality Tools / Control Charts	high; `plotSeries` + interactive SVG lite
Time Series	medium–high (ARIMA ML/xreg/δ TF + `transferIdentify`; no spectral/joint ML+TF)
DOE	medium–high (factorial/RSM/Taguchi L4–L243/DSD/mixture×process/optimizer)
Reliability / Survival	medium–high (parametric + KM + log-rank + Cox MVP + Fine–Gray + AR(1) frailty)
Multivariate / Predictive	medium (core tools + one-way MANOVA; no AutoML)
Assistant / Graphs / Product UI	interactive SVG lite + export; no Session/Assistant

Highest-value gaps (next)

Chart renderer beyond renderPlotSeries (all plot data — boxplot, ECDF, dotplot, interval / main-effects / interaction, fishbone SVG — is produced)
Crossed random effects with AGQ; multi-season STL; joint ML for transfer-function noise
Product surface: Assistant, Session / ReportPad, Worksheet / Project files, macros (out of library scope)

Closed recently

Gap closure (Ярус 8): descriptive statistics / graphical summary, Poisson GOF, main-effects / interaction / interval plot data, boxplot / dotplot / ECDF, cause-and-effect, stability study, G / T charts, T² / MEWMA / generalized variance, spectral analysis, alias structure, life regression + ALT + test plans + repairable systems + probit, cluster variables, MCA, item analysis, promax, general MANOVA, AutoML / cross-validation, random & patterned data; continuous distributions (beta, gamma, weibull, lognormal, exponential, logistic, SEV)
Interactive SVG lite; AGQ glmm + random slope; AR(1) frailty bands; transferIdentify; Taguchi L20/L24/L28/L40/L44/L48
SciPy parity Wave 1–3: Kendall/partialCorr, normality extras, scale/table tests, effect sizes/KDE/bootstrap, nbinom/hypergeom/gumbel/pareto/invgauss; two-way ANOVA/ANCOVA, ADF/KPSS, multi-season STL, ML+transfer ARIMA, AGQ correlated ρ; KS2/AD-k/energy, LOWESS/isotonic, ridge/lasso/QR, DBSCAN, nnls, Welch/SG, brentq
Earlier: renderPlotSeries SVG; AGQ RI + correlated LMM rho; piecewise frailty bands; TF delta + ML xreg; Taguchi L108/L121/L128/L243
Earlier: capabilitySixpack; crossed LMM group2 + Laplace glmm; fineGray + TV frailty; seasonal Kalman ML + transfer; Taguchi L25/L81 + nestedAnova
Earlier: plotSeries; nested LMM + NB glmm; Cox cluster/cause-specific; nonseasonal ML; mixture×process; gamesHowell; one-way manova; Taguchi L50/L54/L64