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Statistical methods
Lattice ships 158 deterministic tools — ANOVA, regression, DoE, time series, and more. Pick a method to see when it applies, what numbers Lattice returns, and how to phrase the request.
Bayesian Inference
Bayesian A/B test | Lattice Statistical Analysis Platform
Perform a precise Bayesian A/B test to compare conversion rates. Get direct probability, lift, and expected loss for clear, data-driven decision making.
Bayesian Regression for Hierarchical Data Analysis | Lattice
Perform Bayesian regression on grouped data with random effects. Get clear posterior estimates and model convergence metrics for your statistical analysis.
Bayesian t-test: Comparing Groups with Probabilistic Analysis
The Bayesian t-test provides a flexible alternative to traditional Welch tests. Use it for group comparisons with Bayesian inference to gain reliable insights.
Causal Inference
Difference-in-Differences Analysis on Lattice
Calculate treatment effects using difference-in-differences to compare groups over time. Evaluate parallel trends to ensure your causal results are valid.
Instrumental Variables Regression for Causal Analysis | Lattice
Use instrumental variables to estimate causal effects when you have endogeneity. Analyze data with 2SLS to address bias and uncover true relationships.
Propensity Score Matching for Observational Data | Lattice
Perform propensity score matching to estimate causal effects in observational studies. Ensure common support and covariate balance for valid inference.
Regression Discontinuity Design Analysis | Lattice Platform
Evaluate policy impacts at thresholds using regression discontinuity design. Analyze LATE results and verify data integrity with McCrary density testing.
Clustering
DBSCAN Density Clustering for Irregular Data Patterns
Use DBSCAN density clustering on Lattice to identify groups of any shape and automatically detect noise in your dataset without needing to pre-set k.
Gaussian mixture clustering | Soft Clustering on Lattice
Use Gaussian mixture clustering to group data into overlapping clusters with soft probabilities and shape analysis for smarter segmentation.
Hierarchical Clustering: Organizing Data into Nested Groups
Use hierarchical clustering in Lattice to build nested groups from your data without picking the number of clusters first. Visualize your patterns.
K-means Clustering: Group Numeric Data on Lattice
Use k-means clustering to group numeric data into distinct sets. Analyze your information to find patterns and segment your dataset with clarity.
Correlation & Association
Correlation Coefficient Analysis for Lattice Data
Calculate correlation coefficient metrics to quantify relationships between variables. Understand variable dependencies with clear, plain-language insights.
Correlation Heatmap: Visualizing Variable Relationships on Lattice
Generate a correlation heatmap to quickly visualize how multiple variables interact. Identify patterns and strengths in your data with visual insight.
Partial Correlation on Lattice | Analyze Net Relationships
Use partial correlation on Lattice to isolate the relationship between two variables while controlling for covariates and identifying direct effects.
Data Preparation
Data Transformation Log and Box-Cox on Lattice
Adjust skewed data distributions using log or Box-Cox data transformation on Lattice. Improve analysis accuracy by normalizing your numeric variables.
Missing Value Imputation on Lattice
Handle incomplete data with precise missing value imputation techniques on Lattice. Choose from mean, median, or custom strategies to ensure data integrity.
Multiple Imputation MICE: Fill Missing Data Accurately
Use multiple imputation MICE to handle missing values by preserving relationships between variables. Get reliable statistical results without data loss.
Outlier Detection in Python | Lattice Data Analysis
Perform outlier detection using IQR or Z-score methods in Lattice. Identify anomalies without altering your source data to ensure accurate results.
Descriptive Statistics
Box Plot: Visualize Data Distribution and Outliers with Lattice
Use a box plot to compare data groups and detect outliers. Gain insights into your distribution with statistical summary visualization on Lattice.
Descriptive Statistics: First-Look Summary of Any CSV in Lattice
Get mean, median, std, skew, and kurtosis for every numeric column with one prompt. Lattice flags missingness, outliers, and dtype surprises before you start any analysis.
Histogram: Visualize Data Distribution on Lattice
Create a histogram to understand your data distribution. Identify patterns, gaps, or skewness using your dataset on Lattice for clearer data analysis.
Violin Plot: Visualize Data Distribution Patterns
Create a violin plot to display data density and summary statistics. Combine KDE density curves with box plots to analyze distribution shapes and variances.
Design of Experiments
Box-Behnken Design for Efficient Process Optimization
Use Box-Behnken design to map complex relationships between 3-7 process factors without testing extreme, dangerous combinations, saving experimental time.
Central Composite Design (CCD): Generate a 2nd-order DoE in Plain English
Generate a Central Composite Design for response-surface modeling on Lattice. Pick factor names and ranges; the engine returns a randomized run sheet with axial and center points.
D-optimal design: Custom Experimental Plans on Lattice
Create efficient D-optimal design plans for constrained or irregular experiments. Select the best test runs from your custom candidate set for high precision.
Fractional Factorial Design for Efficient Experiments
Use fractional factorial design to identify critical factors while saving experimental runs. Reduce costs and maintain statistical clarity in your project.
Full Factorial Design | Systematic Experiment Planning
Create a full factorial design to map every possible combination of your process factors. Gain complete coverage of factor interactions for accuracy.
Statistical Inference Tests
One-way ANOVA: Compare Means Across 3 or More Groups
Run one-way ANOVA on Lattice in plain English. Pick the response and grouping column, get an F statistic, p-value, and post-hoc — every number reproducible by trace_id.
Chi-Square Test for Independence on Lattice
Use the chi-square test to analyze categorical data and determine if relationships between variables are significant or just random chance.
Kruskal-Wallis Test: Compare Groups Without Normal Data
Use the Kruskal-Wallis test to analyze multi-group differences without assuming normal distribution. Identify if group rankings indicate a real trend.
Mann-Whitney U test on Lattice
Evaluate differences between two independent groups using the Mann-Whitney U test. Identify meaningful group median shifts without normal assumptions.
Two-Sample T-Test for Statistical Comparison | Lattice
Use the two-sample t-test in Lattice to compare means between two independent groups. Calculate significance and effect size to drive data decisions.
Machine Learning
CART Decision Tree Analysis for Interpretable Predictions
Use a CART decision tree to visualize classification or regression rules. Understand key data splits and feature importance with clear, logic-based output.
Linear Discriminant Analysis on Lattice
Perform linear discriminant analysis to classify groups and visualize decision boundaries. Identify key features for clear category separation.
Neural Network Classifier for Complex Pattern Prediction
Use a neural network classifier to model non-linear relationships in tabular data. Improve predictive accuracy while maintaining deterministic results.
Random Forest Classifier for Tabular Data Analysis on Lattice
Use the random forest classifier on Lattice to build predictive models. Interpret feature importance through SHAP to identify key drivers of your data.
XGBoost Gradient Boosting for Predictive Modeling on Lattice
Use XGBoost gradient boosting to build high-performance predictive models. Analyze feature importance and gain accurate insights with Lattice.
Optimization
Desirability Function Optimization with Lattice
Find the ideal process parameters using desirability function optimization. Use Lattice to balance trade-offs and reach your target performance goals.
Multi-Response Optimization in Lattice
Find the perfect process balance for multiple goals simultaneously using multi-response optimization. Ensure quality and efficiency in your data.
Process Window Optimization: Find Reliable Operating Ranges
Use process window optimization to identify safe operating ranges in Lattice that meet all your quality and efficiency targets for consistent results.
Data Visualization
Bar Chart for Data Comparison | Lattice Data Analysis
Use a bar chart to visualize categorical data and compare values across groups. Identify trends, compare totals, or calculate averages with Lattice.
Line Plot: Visualize Trends and Time Series Data on Lattice
Create a clear line plot to track trends, time series data, or grouped measurements. Identify patterns and display confidence intervals with ease.
QQ Plot Analysis: Verify Distribution Assumptions in Lattice
Use a QQ plot to compare your data against normal or exponential distributions. Identify outliers and confirm if your data follows a specific pattern.
Residual Plot Analysis on Lattice
Use a residual plot to check your regression model's accuracy. Quickly visualize errors and identify patterns to ensure your model's reliability.
Scatter Plot Generator | Lattice Data Analysis
Visualize relationships between two numeric variables using a scatter plot on Lattice. Identify patterns, trends, and outliers in your dataset easily.
Quality (SPC / Cpk / MSA)
Cpk Process Capability Analysis | Lattice Data Tool
Evaluate manufacturing stability and product quality with Cpk process capability. Use with SPC to detect drift and minimize defective parts per million.
Evaluate Rater Consistency using Intraclass Correlation ICC
Use intraclass correlation ICC to measure agreement between multiple raters. Identify if your scoring system is reliable or needs adjustment.
MSA Gauge R&R Study: Evaluate Measurement System Stability
Perform an MSA gauge R&R study to analyze measurement system variation and ensure data reliability before calculating process capability or SPC limits.
X-bar R control chart for Process Stability on Lattice
Use the X-bar R control chart to monitor process variation and mean shifts. Detect manufacturing instability before calculating Cpk to ensure quality.
Regression Modeling
Multivariate Linear Regression Analysis with Lattice
Understand how multiple independent variables influence a continuous outcome using linear regression. Validate your data with diagnostic insights.
Logistic Regression for Binary Outcome Analysis on Lattice
Perform logistic regression to predict binary outcomes and measure effect sizes using odds ratios and AUC metrics for precise data-driven insights.
Poisson Regression | Count Data Analysis on Lattice
Analyze count data and event rates with Poisson regression. Get incidence rate ratios and overdispersion diagnostics to ensure valid statistical results.
Reports & Exports
APA-style report export: Create Academic Documents on Lattice
Generate professional APA-style report export files directly from your data analysis. Ensure your citations and tables meet academic standards instantly.
PDF Report Export for Data Analysis | Lattice Platform
Generate professional PDF report export files directly from your data analysis. Ensure clear document structure and layout for your professional findings.
PowerPoint report export for Lattice analysis results
Generate professional slides from your analysis. Use PowerPoint report export to transform data tables and insights into structured 16:9 or 4:3 decks.
Response Surface Methodology
Lack-of-Fit Test for Experimental Models on Lattice
Evaluate model adequacy using the lack-of-fit test. Determine if your response surface model captures system trends or requires higher-order complexity.
Quadratic Response Surface Analysis | Lattice
Use a quadratic response surface to model non-linear relationships between your factors and outcomes, identifying the optimal process window for your data.
3D Response Surface Plot | Visualize Process Optimization
Visualize 3D response surface plots to identify ideal process windows. Analyze interaction effects and curvatures with Lattice for precise data modeling.
Response Surface Contour Plot for Process Optimization
Visualize your response surface contour plot to identify ideal factor settings. Analyze your experimental data for better process optimization results.
Survival Analysis
Accelerated Failure Time Model on Lattice
Use the accelerated failure time model for survival analysis. Predict time-to-event outcomes using parametric distributions to quantify life expectancy changes.
Cox Proportional Hazards | Survival Analysis in Lattice
Perform Cox proportional hazards regression to measure the influence of multiple risk factors on survival time and estimate hazard ratios for your data.
Kaplan-Meier survival curve: Analyze Time-to-Event Data
Calculate a Kaplan-Meier survival curve to estimate survival probability over time while accounting for censored data and comparing groups via log-rank test.
Weibull survival model for Reliability & Lifetime Analysis
Calculate Weibull survival model parameters to estimate product reliability, MTBF, and wear-out patterns. Improve failure prediction with clear metrics.
Single-variable Tests (Factor Screening)
Two-Factor Interaction Plot for Data Analysis | Lattice
Visualize how two variables affect a response with a two-factor interaction plot. Detect complex variable relationships and improve process optimization.
Levene's test for Variance Homogeneity in Lattice
Use Levene's test to check if different groups have equal variance. This validation ensures your data is ready for reliable ANOVA or t-test results.
Main Effects Plot | Visualize Factor Impact on Lattice
Visualize how different factor levels influence your response. A main effects plot helps you identify key drivers and compare variable impact quickly.
Pareto chart of effects for factor prioritization
Use a Pareto chart of effects in Lattice to rank process variables by statistical significance and simplify your experimental design process.
Tukey HSD post-hoc: Compare Group Differences in Lattice
Use Tukey HSD post-hoc to identify which specific groups differ after an ANOVA. Compare multiple pairs while controlling for error and identifying gaps.
Time Series Analysis
ARIMA Time Series Forecast | Lattice Data Analysis
Use ARIMA time series forecast to project future values based on past patterns. Get statistical confidence intervals and automated model selection in Lattice.
Time Series Changepoint Detection for Structural Breaks
Identify hidden shifts in your data using time series changepoint detection. Pinpoint the exact moments when your trends or averages change significantly.
Time Series Trend Test | Detect Data Direction | Lattice
Use this time series trend test to determine if your data is increasing, decreasing, or stationary. Get unbiased results with integrated trend analysis.