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⊞ Numlytix
Descriptive Statistics
Select columns to analyze:
Normality Test (Shapiro-Wilk)
Correlation
Select numeric columns:
Covariance
Select numeric columns:
Simple Linear Regression
Multiple Regression
One-Way ANOVA
Two-Way ANOVA
1-Sample t Test
2-Sample t Test
Paired t Test
1 Proportion Test
2 Proportions Test
Chi-Square Test
Mann-Whitney Test
Kruskal-Wallis Test
Sign Test
Histogram
Scatterplot
Boxplot
Select numeric columns:
Line Plot
Bar Chart
Pareto Chart
Normal Probability Plot
Dotplot
Xbar-R Control Chart
Individuals (I-MR) Chart
P Chart
C Chart (Defects per Unit)
Process Capability
Column Calculator
About Numlytix
Numlytix
Version 2.0 · Single-file Statistical Analysis Suite
Inspired by the tool® · Powered by Chart.js
Graphical Summary
1-Sample Z Test
1-Sample Poisson Rate Test
2-Sample Poisson Rate Test
1 Variance Test (Chi-Square)
2 Variances Test (F-test / Levene)
Outlier Test (Grubbs)
Goodness-of-Fit Test
Fitted Line Plot
Nonlinear Regression
Binary Logistic Regression
Poisson Regression
Analysis of Means (ANOM)
Test for Equal Variances
Main Effects Plot
Interaction Plot
Interval Plot (95% CI for Mean)
Select numeric columns:
Wilcoxon Signed Rank Test
Runs Test for Randomness
Cross Tabulation
1-Sample Equivalence Test
2-Sample Equivalence Test (TOST)
Power: 1-Sample t
Power: 2-Sample t
Power: 1 Proportion
Matrix Plot (Scatter Matrix)
Select 2–5 columns:
Bubble Plot
Marginal Plot
Empirical CDF
Select columns:
Stem-and-Leaf Plot
Individual Value Plot
Select numeric columns:
Time Series Plot
Area Graph
Pie Chart
Heatmap (Correlation)
Select numeric columns for correlation heatmap:
Xbar-S Control Chart
Run Chart
EWMA Control Chart
CUSUM Control Chart
NP Chart (Defective Units)
U Chart (Defects per Unit)
Capability Sixpack
Tolerance Intervals
Cause-and-Effect Diagram
Gage R&R Study
Principal Component Analysis
Select numeric columns for PCA:
Cluster Analysis (K-Means)
Select numeric columns:
Discriminant Analysis (LDA)
Correlogram
Select numeric columns:
Trend Analysis
Time Series Decomposition
Moving Average
Autocorrelation Function (ACF)
Partial Autocorrelation (PACF)
Create 2-Level Factorial Design
Analyze Factorial Design
Create CCD Design (Response Surface)
Low / High = boundaries for axial runs. Factorial points land inside.
Weibull Analysis
Kaplan-Meier Survival
Hazard Plot
Create Plackett-Burman Design
Runs = next multiple of 4 ≥ (factors + 1). Screening design — estimates main effects only.
Create General Full Factorial Design
For each factor: specify name, number of levels, and Low/High bounds. Intermediate levels are evenly spaced.
Normal / Half-Normal Effects Plot
Cube Plot
Contour / Surface Plot
Response Optimizer
Create Box-Behnken Design
BBD uses three levels: Low, Center, High. No extreme corners — ideal for constrained experiments.
Analyze Response Surface Design
Used by Remove Highest P / Auto Backward Elim
%
Used by Box-Cox Plot to shade the CI region
RSM Contour Plot
RSM Surface Plot (3D)
Create Mixture Design
Analyze Mixture Design
Mixture Plots
Create Taguchi Design
Enter factor names and actual level values. Levels are evenly spaced between Low and High by default.
Analyze Taguchi Design
Predict Taguchi Results
Predicted response at optimal factor settings will be calculated.
Power: 2-Level Factorial
Power: Plackett-Burman
Predict — RSM Response
Run RSM Analyze first.
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