What is the data actually telling you about the process?

Shows how the average response shifts across the levels of each factor in a study.

Tests whether the means of three or more independent groups differ significantly.

Compares the variances of two independent populations to check whether they are equal.

Fits a model to screening-design results and pinpoints the factors driving the response.

Generates a factorial experiment plan and analyzes factor and interaction effects.

Designs and analyzes experiments where factors are ingredient proportions summing to a constant.

Builds an efficient plan that screens many continuous factors and detects curvature.

Finds the factor settings that yield the most desirable outcomes across one or more responses.

Compares categorical or discrete values using bars whose length reflects each quantity.

Summarizes a distribution through its quartiles, median, spread, and outliers.

Extends a scatterplot to show three continuous variables, with bubble size as the third.

Maps how a response changes across two continuous inputs using shaded contour lines.

Quantifies the strength and direction of linear relationships between variables.

Visualizes where data points concentrate across a two-dimensional space using color intensity.

Computes summary measures such as mean, median, spread, and shape for a dataset.

Combines key statistics with multiple plots of the data in one consolidated view.

Displays the distribution of numeric data as adjacent frequency bars.

Estimates a statistic's sampling distribution by repeatedly resampling one dataset.

Tests whether observed category frequencies match an expected distribution.

Measures the degree to which two continuous variables change together.

Fits several distributions to data and identifies which one fits best.

Evaluates whether a dataset follows a normal distribution.

Guides selection of the correct statistical test through a structured decision tree.

Tests whether a sample proportion differs significantly from a target value.

Compares a sample mean to a target value when the population standard deviation is unknown.

Tests whether a sample mean differs from a known population mean with known variance.

Evaluates the consistency and reliability of a pass/fail inspection process.

Assesses how consistently appraisers classify items into categories.

Builds a randomized data-collection form for an attribute agreement study.

Generates a randomized data-collection worksheet for a Gage R&R study.

Quantifies measurement variation from operators and gauges measuring the same parts.

Evaluates a gauge's accuracy and bias across its measurement range.

Assesses measurement variation when each operator measures different parts.

Assesses a gauge's inherent repeatability and bias using repeated measurements of one part.

Breaks total variation down into its distinct identifiable sources.

Reduces many correlated variables into a smaller set of uncorrelated components.

Compares three or more related groups using a nonparametric ranked test.

Compares three or more independent groups without assuming a normal distribution.

Compares two independent groups using ranks instead of means.

Compares the medians of two or more groups by counting values above and below the overall median.

Tests whether a sample median equals a specified target value.

Tests whether a sample median equals a target using signed ranks.

Checks whether a sequence of values is arranged in a truly random order.

Measures process capability when each item is classified as conforming or not.

Checks process stability and capability when variation differs between subgroups.

Assesses process stability and capability assuming normally distributed data.

Combines six capability charts and metrics for between- and within-subgroup variation.

Evaluates a machine's short-term capability against its specifications per ISO 22514-3.

Evaluates process capability when the data does not follow a normal distribution.

Measures how well a non-normally distributed process meets its specification limits.

Assesses process capability without assuming any specific data distribution.

Measures process capability for defect rates that follow a Poisson distribution.

Generates random values from a binomial distribution of successes in fixed trials.

Generates random values following a normal bell-shaped distribution.

Generates random values from an exponential distribution modeling time between events.

Generates random values from an F-distribution defined by two degrees of freedom.

Generates random values from a right-skewed lognormal distribution.

Generates random counts from a Poisson distribution of events in a fixed interval.

Generates random values from a heavier-tailed t-distribution.

Generates random values from a Weibull distribution used in reliability analysis.

Models how predictors relate to a two-outcome categorical response.

Fits a best-fit line through data points to capture the overall trend.

Quantifies how predictors relate to a continuous response variable.

Models predictors against an unordered categorical response with three or more outcomes.

Models predictors against an ordered categorical response with three or more levels.

Fits a relationship between two variables that both contain measurement error.

Models many predictors against one or more responses by extracting latent components.

Estimates how long a product stays within its quality specifications over time.

Determines test sample size and duration to prove a reliability target at set confidence.

Determines sample size and duration needed to estimate reliability parameters.

Fits a life distribution to failure data that includes censored observations.

Models how a repairable system's failure rate changes as it accumulates operating time.

Prepares raw warranty field-return data for reliability analysis.

Forecasts expected future warranty returns from a fitted reliability model.

Defines inspection sampling plans based on an acceptable quality level.

Builds AQL inspection plans for pass/fail attribute data.

Compares sampling plans side by side using their operating characteristic curves.

Builds an acceptance sampling plan for continuous measurement data.

Creates AQL inspection plans for continuous measured quality characteristics.

Transforms data toward normality before capability analysis or control charting.

Monitors the number of defects in samples of constant size over time.

Detects small sustained shifts in the mean by accumulating deviations from target.

Detects small sustained shifts using a weighted average of recent and past values.

Monitors individual measurements and their moving range for single-value processes.

Tracks individual, between-subgroup, and within-subgroup variation together.

Converts non-normal data toward normality for analyses that require it.

Monitors defective proportions while correcting for overdispersion.

Monitors defects per unit while correcting for overdispersion.