Name: MindStat
Availability: InStock
Author: MindStat

MindStat Statistical Analysis

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Size:

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n₁

n₂

IQR (1.5×) Z-score (3σ) Grubbs Modified Z (MAD)

Wide ↔ Long

Direction

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X (Independent)

M (Mediator)

Y (Dependent)

0.3

0.30

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30%

ℹ️ Note: Loadings are estimated by iterative Maximum Likelihood — minimising F_ML = log|Σ| + tr(Σ⁻¹S) − log|S| − p over Λ/Φ/Θ (first-eigenvector is used only for start values). Validated against R (base optim / lavaan ML). Fit indices (CFI/TLI/RMSEA/SRMR) are computed from the implied covariance. Ordinal indicators use a polychoric correlation matrix + ML; full WLSMV (DWLS) is in progress.

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RBF Neural Network — Time Series Forecasting

Radial Basis Function network with k-means++ centres for non-linear forecasting

Time Series Column

Lag

Centres (K)

Horizon

Seed

Bayesian Neural Network — MC Dropout

MC Dropout for Bayesian uncertainty estimation

Feature Columns (X)

Target Column (y)

Hidden

Dropout

MC Samples

Epochs

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One-way MANOVA with Box's M, Mardia, Levene assumption checks · four multivariate F-approximations · multivariate partial η² for every statistic · Bonferroni univariate follow-up.