Data Input

Data Upload

Upload Data File

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File Settings

Delimiter:

File Encoding:

First Row as Header

Select Sheet:

Download Template

Download a sample data template

Data Preview

Data Summary

Data Selection

📊 Data Quality Check

Data Structure

Missing Values by Column

✏️ Data Editor

Quick Actions

Download CSV

Row Operations

Insert at row:

Column Operations

New column name:

Delete column:

Click any cell to edit (like Excel)

🔍 Data Filtering

Filter Your Data

Select which rows to keep based on column values:

Sample Size Calculator

Sample Size & Power Calculator

Plan your study with confidence - supports t-tests, ANOVA, proportions, correlation & more

RESULTS

Power Curve

Effect Size Guide: What Numbers Should I Use?

What is Effect Size?

Effect size = How big is the difference you want to detect?

Think of it like this: If you're looking for a needle in a haystack...

Large effect: Looking for a sword (easy to find, need fewer samples)
Medium effect: Looking for a key (moderate difficulty)
Small effect: Looking for a needle (hard to find, need MANY samples)

How to Choose Your Effect Size

Best approach: Use historical data or pilot study to estimate the real difference
Six Sigma projects: Start with medium effect size for process improvements
When unsure: Use small effect size (conservative, ensures adequate power)
Breakthrough changes: You can expect large effects (e.g., new technology vs old)

Effect Size Reference Tables

Cohen's d — For Comparing Means (t-tests)

What it measures: The difference between two means, expressed in standard deviation units.

Formula: d = (Mean₁ - Mean₂) / Standard Deviation

Size	d value	Real-World Example
Small	0.2	Height difference between 15 and 16 year old girls (~0.5 inch)
Medium	0.5	Height difference between 14 and 18 year old girls (~1.5 inch)
Large	0.8	Height difference between adult men and women (~2.5 inch)

Six Sigma Tip: For process improvement projects comparing before/after, medium (0.5) is typical. Use large (0.8) only if you expect dramatic improvement (e.g., automation vs manual).

Cohen's f — For ANOVA (3+ Groups)

What it measures: The spread of group means relative to within-group variation.

When to use: Comparing 3 or more groups (e.g., 3 machines, 4 suppliers, 5 shift teams).

Size	f value	Real-World Example
Small	0.10	Subtle difference between 4 suppliers (hard to notice visually)
Medium	0.25	Noticeable difference between machines (visible in box plots)
Large	0.40	Obvious difference between methods (anyone can see it)

Six Sigma Tip: When comparing machines, shifts, or operators, start with medium (0.25) . If you're looking for any difference at all, use small (0.10) to be safe.

Cohen's w — For Chi-Square (Categorical Data)

What it measures: How much the observed proportions differ from expected.

When to use: Contingency tables, testing independence (e.g., defect type vs shift, pass/fail vs supplier).

Size	w value	Real-World Example
Small	0.10	Slight preference in customer survey (51% vs 49%)
Medium	0.30	Clear pattern in defect distribution (60% vs 40%)
Large	0.50	Strong relationship (e.g., 75% defects from one machine)

Six Sigma Tip: For Pareto analysis or defect categorization, use medium (0.30) . This detects meaningful patterns without requiring huge samples.

Correlation (r) — For Relationship Strength

What it measures: How strongly two variables move together (ranges from -1 to +1).

When to use: Testing if X and Y are related (e.g., temperature vs yield, training hours vs performance).

Size	r value	Real-World Example
Small	±0.10	Weak link: coffee consumption vs productivity
Medium	±0.30	Moderate link: study time vs exam scores
Large	±0.50	Strong link: height vs weight, practice vs skill

Six Sigma Tip: In root cause analysis, you often look for medium (0.30) correlations. Very high correlations (>0.7) may indicate obvious relationships or multicollinearity.

Cohen's f² — For Regression (R² Significance)

What it measures: How much variance in Y is explained by your predictors (X variables).

Formula: f² = R² / (1 - R²)

Size	f² value	Equivalent R²	Meaning
Small	0.02	~2%	Model explains little variance (but may still be useful)
Medium	0.15	~13%	Model explains moderate variance (typical for social sciences)
Large	0.35	~26%	Model explains substantial variance (strong predictive model)

Six Sigma Tip: For DOE (Design of Experiments) transfer functions, aim for R² > 0.70 (f² > 2.3). For screening experiments, medium (0.15) is acceptable.

Quick Decision Guide: Which Effect Size Should I Use?

	Don't know what to expect?	→ Use SMALL (conservative, won't under-power your study)
	Typical process improvement?	→ Use MEDIUM (most common in Six Sigma)
	Major change or new technology?	→ Use LARGE (breakthrough improvements)
	Have pilot data or historical data?	→ CALCULATE your actual expected effect size!

Warning: Using a LARGE effect size when the true effect is small will result in an underpowered study (high risk of missing real effects)!

How Effect Size Impacts Sample Size

Example: Two-sample t-test at α=5%, Power=80%

Effect Size	Cohen's d	n per group	Total N
Small	0.2	393	786
Medium	0.5	64	128
Large	0.8	26	52

Notice: Detecting small effects requires 15x more samples than detecting large effects!

Analysis

Statistical Analysis

Analysis Type

First Variable (Sample 1):

Second Variable (Sample 2):

Confidence Level (%)

Test Type

Hypothesized Mean/Proportion (H₀)

Hypothesized Standard Deviation (σ₀)

Hypothesized Difference (H₀)

Hypothesized Rate (λ₀)

Time Period (Exposure)

Significance Level (α)

Alternative Hypothesis

Download HTML Report

Visualization

Results Summary

Statistical Power Analysis

Detailed Statistics

Six Sigma Inferential Statistics Tool

Statistical Analysis from Your Data
Enter your sample data to calculate confidence intervals or test hypotheses.
Perfect for DMAIC projects when you have collected measurements.

Step 1: Choose Your Analysis

What do you want to do?

What type of data?

How many groups?

Step 2: Enter Your Sample Data

Step 3: Analysis Settings

Results Summary

Visual Results

Detailed Analysis

Six Sigma Interpretation

Statistical Assumptions

Download Report

Statistical Process Control

Control Chart Selection Guide

Select Control Chart Type:

Measurement Variable:

Subgroup/Sample Variable:

Subgroup Size (if no subgroup variable):

Sample Size Variable (Optional):

Control Rules Selection

Select which rules to detect out-of-control conditions:

Select Control Rules:

Rule 1: Points outside 3σ limits

Rule 2: 9 points in a row on same side of center

Rule 3: 6 points in a row steadily increasing/decreasing

Rule 4: 14 points in a row alternating up/down

Rule 5: 2 out of 3 points beyond 2σ

Rule 6: 4 out of 5 points beyond 1σ

Rule Explanations:
• Rule 1: Any point beyond control limits
• Rule 2: Process shift or bias detected
• Rule 3: Systematic trend in process
• Rule 4: Excessive variation or overcontrol
• Rule 5: Points near control limits
• Rule 6: Process moving away from center

Download Data Template

Download a template CSV file for your control chart data

Download Chart

Control Charts

Process Statistics

Out of Control Signals

Pareto Analysis

Pareto Analysis Settings

Category Variable:

Variable containing problem categories/defect types

Count Variable (optional):

Variable containing count for each category. If not selected, categories will be counted

Show Top N Categories:

Limit chart to the top N most frequent categories

Show Percentage

Show Cumulative Line

Sort in Descending Order

Color Palette

Chart Title

X-Axis Label

Y-Axis Label

Show 80% Threshold Line

Download Chart

Pareto Chart

Analysis Results

Pareto Summary

80/20 Analysis

Process Capability Analysis

Process Capability Analysis Settings

Measurement Variable:

Distribution:

Target Value:

Lower Specification Limit:

Upper Specification Limit:

Number of Sigmas:

Show Results Table

Download CSV Download Full Report

Process Capability Chart

Capability Metrics

Overall Capability

Potential (Within)

Performance

Z Benchmark

Process Capability Sixpack (Minitab Style)

Normal Probability Plot

Process Performance Metrics

Detailed Capability Analysis Results

Non-Normal Capability Analysis

Non-Normal Process Capability Analysis Settings

Measurement Variable:

Distribution Type:

Target Value:

Lower Specification Limit:

Upper Specification Limit:

Show Results Table

Show Distribution Fit Details

Download Results Download HTML Report

Non-Normal Process Capability Chart

Non-Normal Capability Analysis Results

Distribution Fitting Details:

About Non-Normal Capability Analysis

Non-normal capability analysis uses fitted distributions to properly calculate capability indices when data doesn't follow a normal distribution. Standard Cp and Cpk indices can lead to incorrect conclusions with non-normal data.

Metrics Provided:

Z-bench: Calculates process capability from percentiles of the fitted distribution
Pp(percentile): Process performance index based on percentiles
Ppk(percentile): Process performance index taking into account process centering
PPM (Parts Per Million): Expected defect rates based on the fitted distribution

Distribution Selection:

Auto (Best Fit): Automatically selects the best-fitting distribution using Anderson-Darling statistic
Manual Selection: Choose a specific distribution that might be appropriate for your process

Non-normal capability analysis is particularly important for processes with natural skewness, such as those with physical boundaries at zero (e.g., diameter, surface roughness).

Data Transformation

Data Transformation Tools

Select Variable to Transform:

Transformation Method:

Automatically select best lambda

Lambda Value:

Johnson Family:

Specification Limits (Optional):

Include Specification Limits

Lower Spec Limit (LSL):

Target Value:

Upper Spec Limit (USL):

New Variable Name:

Replace Original Variable

Show Normality Tests

Normality Test:

Download Transformed Data

Before and After Transformation

Transformation Results

Normality Test Results:

Transformed Specification Limits:

Use these values for normal capability calculations:

One-Way ANOVA Settings

Response Variable(s) (Numeric):

Factor Variable (Categorical):

Note:
• Select a numeric response variable (continuous outcome)
• Select a categorical factor variable (groups to compare)
• Numeric variables with ≤10 unique values are included as potential factors
• For continuous predictors with >10 values, use regression analysis instead

📄 Download HTML Report

This tab shows the ANOVA table, effect sizes, and statistical tests in formatted tables.

Shows group means with confidence intervals, individual data points, and effect size information.

Displays the percentage of variance explained by the factor vs. within-group variance based on eta-squared.

Diagnostic plots to check ANOVA assumptions: normality, equal variances, etc.

Two-Way ANOVA Settings

Example Data

Response Variable(s) (Numeric):

Factor 1 Variable (Categorical):

Factor 2 Variable (Categorical):

Include Interaction Term

Note:
• Select a numeric response variable (continuous outcome)
• Select two different categorical factor variables
• Numeric variables with ≤10 unique values are included as potential factors
• For continuous predictors with >10 values, use regression analysis instead
• Interaction term tests if the effect of one factor depends on the other

📄 Download HTML Report

This tab shows the Two-Way ANOVA table, variance components, and statistical tests in formatted tables.

Shows the interaction between factors. Parallel lines indicate no interaction.

Shows the main effect of each factor separately.

Displays the percentage of variance explained by each source of variation.

Diagnostic plots to check ANOVA assumptions: normality, equal variances, etc.

📊 Generalized ANOVA Settings

Variable Selection

📈 Response Variable(s):

🔢 Factor Variables:

📊 Covariate Variables:

Model Options

Include All Factor Interactions

Include Factor × Covariate Interactions

Model Type:

📄 Download Report

📈 Analysis Results

ℹ️ Generalized ANOVA Information

About Generalized ANOVA

Generalized ANOVA allows you to analyze the relationship between one continuous response variable and multiple factors and/or covariates.

Factors: Categorical variables (groups)
Covariates: Continuous variables used as controls
Interactions: Test whether the effect of one variable depends on another
Model Types: Choose between ANOVA, Linear Model, or Mixed Effects approaches

Model Interpretation

Main Effects: Individual contribution of each factor/covariate
Interaction Effects: Combined effects between variables
F-statistic: Test of significance for each effect
p-value < 0.05: Statistically significant effect

Attribute Agreement Analysis (Gage R&R for Attributes)

Attribute Agreement Analysis Report

Within Appraiser Agreement

Appraiser vs Standard Agreement

Between Appraisers Agreement

All Appraisers vs Standard

Fleiss' Kappa Statistics

Cohen's Kappa (Pairwise)

Assessment Effectiveness

Disagreement Summary by Part

Disagreement Pattern Analysis

Appraiser Bias Analysis

Assessment Agreement Plot (Minitab Style)

Agreement Chart

Kappa Confidence Intervals

Assessment Agreement Heatmap

Professional Report

Download Full Report (PDF) Download Results (Excel)

Before Regression Analysis

Check your data quality and assumptions before running regression

📊

Regression Diagnostics

Fit a linear model, check all assumptions, download a complete report

OLS Linear Regression 6 Diagnostic Tests 4 Residual Plots HTML Report Export

1 Upload Data

Browse...

2 Select Variables

3 Run Analysis

4 Export

🧪 Assumption Diagnostics

⚠ Influential Observations (Cook's D > 0.5)

📊 Residual Diagnostic Plots

Residuals vs Fitted

Normal Q-Q Plot

Scale-Location

Cook's Distance

📐 Predictor Correlation Analysis

Correlation Circle Plot

Correlation Heatmap

📋 Full Correlation Matrix

📝 Full R Statistical Output

Logistic Regression Analysis

Binary Logistic Regression Analysis

Binary Outcome Variable

Predictor Variables

Binary outcome variable must have exactly 2 unique values (e.g., 0/1, Yes/No, Success/Failure)

Show Odds Ratios & Business Impact

Confidence Level

Show Classification Results

Show Diagnostic Plots

Generate Business Insights

Show ROC Curve

Download HTML Report

📐 Model Equation

🎯 Business Insights & Strategic Recommendations

Model Summary

Model Performance Metrics

📊 Model Coefficients & Business Impact Analysis

🎯 Odds Ratios & Strategic Impact

Confusion Matrix

Classification Metrics

Diagnostic Plots

ROC Curve Analysis

ROC Statistics

🔮 Strategic Scenario Planning Tool

Enter Values for Prediction

Prediction Results

Taguchi Design of Experiments

Robust parameter design for process optimization using orthogonal arrays and signal-to-noise ratios

What is Taguchi Method?

The Taguchi method is a structured approach to find the best factor settings that make your process robust (insensitive to noise/variation). Unlike full factorial designs, Taguchi uses orthogonal arrays to test many factors with very few experiments.

Your Workflow:

Design: Select factors and levels, get an orthogonal array
Experiment: Download the template, run experiments, record responses
Analyze: Upload results for S/N ratio analysis and optimization
Confirm: Verify optimal settings with confirmation runs

When to Use Taguchi

You have 3+ factors to optimize
You want to reduce experiments vs full factorial
You care about robustness (consistency)
Factors have 2 or 3 levels each

Limitations

Assumes factors don't strongly interact
Limited to screening main effects
Confirmation experiments are essential

Download Example Dataset

1 Define Factors

How to Choose Factors and Levels

Select the process parameters you want to optimize. Each factor needs 2 or 3 levels:

2 levels: Low/High (e.g., Temperature: 180/220)
3 levels: Low/Medium/High (e.g., Speed: 100/150/200)

Number of Factors:

Factor Levels:

Replicates per Run:

1 Upload Experimental Data

Data Format

Upload your completed experiment file (CSV or Excel). It should contain:

Factor columns (the levels you tested)
Response columns (your measurements, one per replicate)

Choose CSV or Excel File

Browse...

Data Input

Data Upload

File Settings

Data Preview

Data Summary

Data Selection

📊 Data Quality Check

Data Structure

Missing Values by Column

✏️ Data Editor

Quick Actions

Row Operations

Column Operations

Click any cell to edit (like Excel)

🔍 Data Filtering

Filter Your Data

Transform Tools - Stack/Unstack/Subsets

🧪 Data Lab: Transform & Clean

🎯 Step 1: What's your goal?

🗂️ Step 2: Select columns

▶️ Step 3: Run & Save

🔍 Preview

🎯 Step 1: Pick a column to filter

🔧 Step 2: Set your condition

▶️ Step 3: Apply & Save

🔍 Preview

🎯 Step 1: What needs cleaning?

🔧 Step 2: Fill or replace NAs

▶️ Step 3: Apply & Save

🧹 Cleaning Report

🎯 Step 1: What do you need?

🔧 Step 2: Configure

▶️ Step 3: Run & Save

📝 Result

📋 Transformation History

💾 Save Current Data

📊 Quick Data Summary

Sample Size Calculator

Sample Size & Power Calculator

STEP 1: Analysis Type

STEP 2: What to Calculate?

STEP 3: Basic Parameters

STEP 4: Effect Size

STEP 5: Get Results

RESULTS

Power Curve

Effect Size Guide: What Numbers Should I Use?

What is Effect Size?

How to Choose Your Effect Size

Effect Size Reference Tables

Cohen's d — For Comparing Means (t-tests)

Cohen's f — For ANOVA (3+ Groups)

Cohen's w — For Chi-Square (Categorical Data)

Correlation (r) — For Relationship Strength

Cohen's f² — For Regression (R² Significance)

Quick Decision Guide: Which Effect Size Should I Use?

How Effect Size Impacts Sample Size

Analysis

Statistical Analysis

Visualization

Results Summary

Statistical Power Analysis

Detailed Statistics

Six Sigma Inferential Statistics Tool

Step 1: Choose Your Analysis

Step 2: Enter Your Sample Data

Step 3: Analysis Settings

Results Summary

Visual Results

Detailed Analysis

Six Sigma Interpretation

Statistical Assumptions

Data Visualization

📊 Plot Mode

📋 Variable Selection

X-Axis Variable(s)

Y-Axis Variable(s) (Optional)

🎨 Choose Plot Type

📐 Layout Options

✨ Additional Mappings