Data Input

Data Upload

Upload Data File

Browse...

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:

Transform Tools —style Stack/Unstack/Subsets

🧰 Data Transform

Inputs

Alignment method

Use index/key column (recommended)

Auto index per level (no key column)

Sort factor levels (A→Z)

Repair duplicate/invalid names

Download CSV

Preview

Unstack: one categorical 'factor' (e.g., Supplier A/B/C), one 'value' column (e.g., Output). Optionally provide an index/key to align rows.

Inputs

Name for source column

Name for values column

Drop NA after stack

Download CSV

Preview

Stack: take multiple measurement columns and stack them into one long column, with an indicator of their origin.

Condition

Operator

Keep NA rows

Download CSV

Preview

Build simple one-condition subsets quickly. For complex filtering, use your main Filtering box.

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 Results Download HTML Report

Process Capability Chart

Capability Metrics

Overall Capability

Potential (Within) Capability

Performance

Z Benchmark

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 Tool Educational Edition

This tool helps you understand and improve your linear regression model. Hover over icons for explanations.

📊 Analysis Workflow

Step 1: Upload Your Data

Choose Data File

Browse...

Step 2: Select Variables

Step 3: Run Analysis

Step 4: Generate Report

📄 Download HTML Report

🔍 Diagnostic Tests

⚠️ Influential Observations

These rows have high influence on your model:

📥 Download Influential Rows

📊 Full Statistical Output

complete output:

🔗 Correlation Analysis

Understanding relationships between your variables helps identify multicollinearity and redundant predictors.

📊 Correlation Matrix

Values closer to ±1 indicate stronger linear relationships.

🎯 Interpretation Guide

0.0 to ±0.3: Weak correlation
±0.3 to ±0.7: Moderate correlation
±0.7 to ±1.0: Strong correlation
Values > ±0.8 between predictors suggest multicollinearity

🌡️ Correlation Heatmap

Color intensity shows correlation strength.

⚠️ High Correlations Alert

📋 Detailed Correlation Matrix

📥 Download Correlation Matrix

🎓 Understanding Regression Diagnostics

🎯 What is Regression Analysis?

Regression analysis helps you understand relationships between variables and make predictions. It answers questions like "How does X affect Y?" and "What will Y be when X changes?"

📊 Key Assumptions to Check

Linearity: The relationship is roughly straight-line
Independence: Observations are unrelated to each other
Homoscedasticity: Variance stays constant across predictions
Normality: Residuals follow a bell curve
No Multicollinearity: Predictors are not too highly correlated

🚨 Red Flags to Watch For

R-squared below 0.3 (weak model fit)
VIF values above 10 (severe multicollinearity)
Significant p-values in diagnostic tests (< 0.05)
Cook's Distance above 1 (very influential points)

💡 Tips for Better Models

Start simple - use fewer predictors initially
Check data quality - look for outliers and errors
Consider transformations if assumptions are violated
Always validate on new data when possible

💡 Example Interpretations

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

Design of Experiments (DOE) Analysis

Design Configuration

Select Design Type:

Number of Factors:

Design Options

Number of Center Points:

Number of Replicates:

Technical replicates: Each experimental run will be repeated this many times

Number of Response Variables:

Specify how many different responses you will measure (e.g., Yield, Purity, Cost)

Add Blocking Variable

Number of Blocks:

Randomize Run Order

Resolution:

CCD Type:

Alpha (Star Point Distance):

Design Information

Generated Design

Download as CSV

Download as Excel

Design Properties

Design Matrix Visualization

Run Distribution

Correlation Structure

Design Efficiency Metrics

Upload Data

Choose CSV or XLSX File

Browse...

File has header row (CSV only)

Debug Info:

Data Quality

Data Preview

Variable Selection

Select your response variable replicates (e.g., Response1, Response2, Response3) and experimental factors.

Factor Type Configuration

Categorical factors should be treated as factors. Continuous numeric variables will be fitted as continuous.

Model Options

Model Summary

Fitted Model Equation:

Type II ANOVA Table

Download ANOVA

Effect Sizes (Partial η²)

Partial η² indicates the proportion of variance explained by each term.

Main Effects Plot

Shows the average response at each level of each factor.

Two-Way Interaction Plot

Non-parallel lines indicate interactions between factors.

Diagnostic Interpretation

Quick Reference

Residuals vs Fitted: Random scatter = good
Q-Q Plot: Points on line = normal
Scale-Location: Flat line = constant variance
Cook's Distance: Low values = no influential points

All Diagnostic Plots

Optimization Goal

Objective:

Target Value:

Optimal Solution

Note: If your design is saturated, some coefficients may be aliased. Predictions are still valid.

Response Surface Visualization

Show experimental points

Highlight optimal point

Contour levels:

Plot style:

Download Plot

Predicted Response Grid

Download Grid

Single Point Prediction

Average over block levels (if blocking used)

Prediction Result

Six Sigma Quality Analysis

Compare Two Settings

Compare current process settings with proposed optimal settings to evaluate quality improvement.

Current/Baseline Settings

Proposed/Candidate Settings

Specification Limits & Quality Parameters

Define your specification limits to calculate process capability and defect rates.

Target Value (Nominal):

Lower Specification Limit (LSL):

Upper Specification Limit (USL):

Process Standard Deviation (Sigma):

Cost Parameters (Optional)

Cost per defective unit ($):

Annual production volume (units):

One-time implementation cost ($):

Six Sigma Quality Comparison Results

DOE Analysis Platform - User Guide

Quick Start

Generate Design: Create your experimental design using the Design Generator
Download Template: Export the design template to conduct experiments
Run Experiments: Execute experiments and record responses
Upload Data: Import your completed DOE dataset (CSV or XLSX format)
Configure Model: Select response variable, factors, and optional blocking
Fit Model: Click 'Fit Model' to run the analysis
Review Results: Examine ANOVA, effects, and diagnostics
Optimize: Find optimal factor settings
Predict & ROI: Calculate predictions and return on investment

Design Generation

The Design Generator allows you to create various experimental designs including:

Factorial Designs: Full and fractional factorial designs for screening and characterization
Response Surface Designs: Central Composite (CCD) and Box-Behnken for optimization
Screening Designs: Plackett-Burman for many factors with few runs
D-Optimal Designs: Computer-generated designs for irregular regions or constraints

After generating a design, download it as a template, run your experiments following the run order, and record your response values. Then upload the completed data for analysis.

Data Format Requirements

Structure: Each row = one experimental run
Columns: Include factors (X variables), response (Y variable), and optionally a blocking variable
Format: CSV or Excel (.xlsx) files supported
Headers: First row should contain column names
Missing Values: Rows with missing values will be automatically removed

Example Data Structure:

Temperature, Pressure, Catalyst, Block, Yield
180,       50,       A,        1,     85.2
200,       50,       A,        1,     89.1
180,       70,       B,        2,     87.5
...

Feature Descriptions

Design Generator

Design Types: Multiple design families for different objectives
Factor Specification: Define factor names and levels
Design Options: Add center points, replicates, and blocking
Efficiency Metrics: D-, A-, and G-efficiency calculations
Export Templates: Download as CSV or Excel with instructions

ANOVA & Effects

Type II ANOVA: Tests significance of each factor/interaction
Effect Sizes: Partial R² shows variance explained by each term
Main Effects Plot: Shows average response at each factor level
Interaction Plot: Non-parallel lines indicate interactions

Diagnostics

Residuals vs Fitted: Should show random scatter (no patterns)
Q-Q Plot: Points should follow diagonal line (normality check)
Scale-Location: Checks for constant variance (homoscedasticity)
Cook's Distance: Identifies influential observations

Optimization

Objective: Choose to maximize, minimize, or hit a target
Grid Search: Evaluates all combinations of observed factor levels
Contour Plot: Visualizes response surface for two factors
Optimal Settings: Recommended factor levels to achieve goal

Prediction & ROI

Point Prediction: Predict response at specific factor settings
Cost-Benefit: Compare baseline vs candidate settings
Economic Analysis: Calculate net benefit and ROI
Payback Period: Units needed to recover changeover costs

Choosing the Right Design

Design Selection Guide:

Full Factorial: Use when you have 2-4 factors and want to study all interactions
Fractional Factorial: Use for 5+ factors when main effects and 2-way interactions are of interest
Plackett-Burman: Use for screening 7+ factors to identify the vital few
Central Composite (CCD): Use for optimization after identifying important factors
Box-Behnken: Use for optimization when you want to avoid extreme corner points
D-Optimal: Use when you have constraints or an irregular experimental region

About Blocking

Blocking removes systematic variation from nuisance factors (e.g., Day, Operator, Batch). The block variable is included additively in the model (no interactions with factors). This improves precision by accounting for block-to-block variation.

Use blocking when experiments are conducted in groups that may differ
Examples: different days, batches, machines, or operators
Block effects are not typically of interest but improve analysis quality

Tips & Best Practices

Design First: Always create your experimental design before collecting data to ensure efficiency
Randomization: Run experiments in randomized order to avoid systematic bias
Factor Types: Categorical factors (e.g., Catalyst A/B) should be marked as factors. Continuous variables (e.g., Temperature) can remain numeric.
Interactions: Start with 2-way interactions. Add higher-order only if justified by subject matter knowledge.
Sample Size: Ensure adequate replication. Rule of thumb: ≥3 replicates per factor combination.
Diagnostics: Always check diagnostic plots. Patterns indicate model problems or need for transformation.
Outliers: Investigate influential points (high Cook's distance) - they may be errors or important findings.
Transformations: If residuals show non-normality or heteroscedasticity, consider log or Box-Cox transformation of response.

Statistical Concepts

P-value Interpretation:

p < 0.001: *** (highly significant)
p < 0.01: ** (very significant)
p < 0.05: * (significant)
p < 0.10: . (marginally significant)
p ≥ 0.10: not significant

R² and Adjusted R²:

R²: Proportion of variance explained by model
Adjusted R²: Accounts for number of predictors (preferred for model comparison)
Values closer to 1.0 indicate better fit

RMSE (Root Mean Square Error):

Average prediction error in same units as response
Lower values indicate better fit
Compare to response standard deviation to assess model quality

Design Efficiency:

D-efficiency: Maximizes determinant of information matrix (overall precision)
A-efficiency: Minimizes average variance of parameter estimates
G-efficiency: Minimizes maximum prediction variance
Values closer to 1.0 indicate more efficient designs

Need Help?

This application is designed for Design of Experiments (DOE) analysis with support for design generation, factorial designs, blocking, and economic optimization. For technical questions about DOE methodology, consult standard references such as Montgomery's 'Design and Analysis of Experiments' or Box, Hunter & Hunter's 'Statistics for Experimenters'.

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 —style Stack/Unstack/Subsets

🧰 Data Transform

Inputs

Preview

Inputs

Preview

Condition

Preview

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

🔍 Comparison Setup

📊 Distribution Overview

🎨 Customize Your Plot

💾 Export Plot

Advanced Visualization

📊 Advanced Plot Setup

📋 Data Requirements

🎯 Map Your Data Columns

🎨 Additional Mappings

✏️ Customize Your Advanced Plot

📁 Upload Your Data

📋 Data Preview

💾 Export Plot