Guides
Find a tool, follow the steps, and try it with sample data.
All guides
94 guides
Run your first analysis
Import sample measurements, run an analysis, and add the results to a report.
Read guideMeans: k-Sample (ANOVA)
Compare several group means
Test whether several independent groups share a common population mean.
Example: four adhesive formulations
Read guideAutocorrelation
Check dependence in a time series
Examine how regularly spaced observations relate to earlier values.
Example: hourly chilled-water temperature
Read guideCategorical: Independence (χ²)
Check whether two categories are associated
Compare observed category counts with the counts expected under independence.
Example: shipping service and arrival condition
Read guideConfidence Intervals
Estimate a population mean
Estimate the uncertainty in a mean from a sample size, mean and standard deviation.
Example: Cell A mean seal strength
Read guideConfidence Intervals
Estimate a population variance
Estimate uncertainty in squared spread from a sample size and standard deviation.
Example: Cell A seal-strength variance
Read guideConfidence Intervals
Estimate a population standard deviation
Estimate uncertainty in spread using the original measurement unit.
Example: Cell A seal-strength standard deviation
Read guideConfidence Intervals
Estimate a difference between means
Estimate the difference between two independent population means.
Example: Cell A versus Cell B mean strength
Read guideConfidence Intervals
Estimate a variance ratio
Estimate relative spread in two independent, approximately normal samples.
Example: Cell A versus Cell B strength variation
Read guideConfidence Intervals
Estimate a population proportion
Estimate an event proportion and its binomial confidence interval.
Example: Cell A leak-test pass proportion
Read guideConfidence Intervals
Estimate a difference between proportions
Estimate a difference in event proportions from two independent samples.
Example: Cell A versus Cell B leak-test passes
Read guideConfidence Intervals
Estimate an event rate
Estimate the number of events per unit of exposure and its uncertainty.
Example: packaging stoppages per operating hour
Read guideDisplay Descriptive Statistics
Summarize numerical measurements
Review the center, spread and shape of a numeric column, with optional group summaries.
Example: packing time by zone
Read guideDisplay Descriptive Statistics
Summarize numerical measurements
Review the center, spread and shape of a numeric column, with optional group summaries.
Example: parcel mass by zone
Read guideDisplay Descriptive Statistics
Summarize item counts by packing zone
Review the center, spread and shape of a numeric column, with optional group summaries.
Example: items per order by zone
Read guideDistributions
Find an exact binomial probability
Calculate the chance of a specified count in independent trials.
Example: exactly two nonconforming labels
Read guideDistributions
Find a cumulative binomial probability
Calculate the chance of a count at or below a specified value.
Example: at most two nonconforming labels
Read guideDistributions
Find a cumulative event-count probability
Calculate the chance of a count under a Poisson model.
Example: at most five support arrivals in one hour
Read guideDistributions
Find a Poisson count cutoff
Find the smallest count whose cumulative probability reaches a target.
Example: a 95% cutoff for hourly support arrivals
Read guideDistributions
Find a waiting-time tail probability
Calculate the chance of exceeding a time under an exponential model.
Example: waiting more than twenty minutes
Read guideDistributions
Find a waiting-time percentile
Find the time below which a selected share of an exponential model lies.
Example: a 90th-percentile waiting time
Read guideDistributions
Read a probability density
Evaluate the height of a continuous distribution at a specified value.
Example: waiting-time density at twelve minutes
Read guideDistribution Fit Analysis
Compare fitted distributions
Review candidate distributions and transformations without treating a fit test as a model winner.
Example: bench-test completion times
Read guideEqual Variances
Compare variation across groups
Test whether independent groups have equal population variances.
Example: assembly torque at four stations
Read guideVariances: Two-Sample (F)
Compare two variances
Estimate the ratio of variation in two independent, approximately normal samples.
Example: existing and revised cutting fixtures
Read guideNormal Distribution Plot
Find a normal tail probability
Show the area below a value under a specified normal model.
Example: bottle fills below 245 mL
Read guideNormal Distribution Plot
Find a normal interval probability
Show the area between two values under a specified normal model.
Example: bottle fills between 245 and 255 mL
Read guideNormal Distribution Plot
Find a normal percentile
Find the value below which a selected share of a normal model lies.
Example: the 99th percentile of bottle fill
Read guideNormality
Assess a normal distribution model
Use a test and probability plot to check whether a normal model is reasonable for a sample.
Example: connector insertion forces
Read guideOutlier Test
Investigate an extreme observation
Test one unusual value while retaining the measurements for investigation.
Example: solids-content aliquots
Read guidePower & Sample Size
Plan a one-sample study
Choose a sample size for detecting a meaningful difference from a reference mean.
Example: a sachet-fill sample size
Read guidePower & Sample Size
Plan an equal-size two-sample study
Choose the number of observations per group for a planned mean comparison.
Example: two equal-size sachet-fill groups
Read guidePower & Sample Size
Check power for a fixed sample
Estimate the chance of detecting a specified difference with an available sample size.
Example: fifty sachets per group
Read guidePower & Sample Size
Find a detectable mean difference
Estimate the difference needed to reach a target power at a fixed sample size.
Example: sixty sachets per group
Read guidePower & Sample Size
Plan a one-proportion study
Choose a sample size for comparing a proportion with a baseline.
Example: a pass-rate increase from 93% to 97%
Read guidePower & Sample Size
Plan a two-proportion study
Choose equal group sizes for a planned comparison of two proportions.
Example: pass rates of 98% versus 94%
Read guideProportions
Compare a proportion with a benchmark
Test a stated event proportion using a count of events and trials.
Example: first-scan label readability
Read guideProportions
Compare two proportions
Test a difference in event proportions from independent counts.
Example: first-scan label readability
Read guideLinear Regression
Fit a linear regression model
Estimate how a numeric response changes with several predictors and inspect the unexplained variation.
Example: electricity use in drying batches
Read guideMeans: One-Sample (t)
Compare a mean with a target
Estimate how far a population mean is from a stated value.
Example: sachet net mass
Read guideMeans: Two-Sample (t)
Compare two independent means
Estimate the difference between two population means, with its uncertainty.
Example: cushioning force
Read guideMeans: Two-Sample (t)
Compare paired measurements
Estimate a mean difference from matched pairs.
Example: firmware scan time
Read guidePlots
Build a histogram
Show how often measurements fall within each interval.
Example: packing times
Read guidePlots
Build a box plot
Compare the center and spread of measurements across groups.
Example: packing zones
Read guidePlots
Build an individual-value plot
Show each measurement beside the other observations in its group.
Example: adhesive strength
Read guidePlots
Build a dotplot
Compare measurements using dots stacked within shared intervals.
Example: adhesive strength
Read guidePlots
Build a scatter plot
Plot two measurements from each observation to explore their relationship.
Example: drying batches
Read guidePlots
Build a scatterplot matrix
Inspect several pairs of numeric variables in one set of charts.
Example: drying batches
Read guidePlots
Build a correlation heatmap
Compare the direction and strength of pairwise associations between numeric variables.
Example: drying batches
Read guidePlots
Build a bar chart from counts
Compare supplied values across categories and groups.
Example: shipping damage
Read guidePlots
Build an interval chart
Compare sample means and the uncertainty around them.
Example: adhesive strength
Read guidePlots
Build a general line chart
Connect measurements or summaries in the selected order.
Example: packing times by item count
Read guidePlots
Build a main-effects chart
Compare observed response means at the levels of each factor.
Example: pouch-seal strength
Read guidePlots
Build an interaction chart
Compare how an observed response changes across combinations of two factors.
Example: pouch-seal strength
Read guidePlots
Build a time-series chart
Follow measurements in collection order to see changes over successive observations.
Example: batch viscosity
Read guidePlots
Build a Pareto chart
Rank category counts and show how their cumulative share grows.
Example: shipping damage
Read guideFishbone Diagram
Build a Fishbone investigation
Organize possible causes of a problem and plan the checks needed to investigate them.
Example: weak pouch seals
Read guideCreate Random Data
Generate Normal random data
Create simulated values from a chosen probability model, with a fixed seed when you need to repeat the sample.
Example: pin diameters
Read guideCreate Random Data
Generate Uniform random data
Create simulated values from a chosen probability model, with a fixed seed when you need to repeat the sample.
Example: dispatch delays
Read guideCreate Random Data
Generate Exponential random data
Create simulated values from a chosen probability model, with a fixed seed when you need to repeat the sample.
Example: arrival gaps
Read guideCreate Random Data
Generate Binomial random counts
Create simulated values from a chosen probability model, with a fixed seed when you need to repeat the sample.
Example: nonconforming items per tray
Read guideCreate Random Data
Generate Poisson random counts
Create simulated values from a chosen probability model, with a fixed seed when you need to repeat the sample.
Example: surface defects per panel
Read guideCreate Random Data
Generate several independent columns
Create simulated values from a chosen probability model, with a fixed seed when you need to repeat the sample.
Example: three fill-volume samples
Read guideStandardize & Normalize
Standardize measurements with z-scores
Transform numeric columns separately to prepare measurements for a subsequent analysis.
Example: drying batches
Read guideStandardize & Normalize
Center measurements about the mean
Transform numeric columns separately to prepare measurements for a subsequent analysis.
Example: drying batches
Read guideStandardize & Normalize
Scale measurements to [0, 1]
Transform numeric columns separately to prepare measurements for a subsequent analysis.
Example: drying batches
Read guideStandardize & Normalize
Scale measurements to [−1, 1]
Transform numeric columns separately to prepare measurements for a subsequent analysis.
Example: drying batches
Read guideDesign of Experiments
Analyze a full factorial design
Use recorded experimental runs to estimate factor effects and interactions.
Example: pouch-seal strength
Read guideDesign of Experiments
Create a full factorial design
Plan runs at every combination of the chosen factor levels.
Example: pouch-seal strength
Read guideDesign of Experiments
Analyze a fractional factorial design
Estimate effects from a fraction of the possible factor combinations and check what remains confounded.
Example: label adhesion
Read guideDesign of Experiments
Create a fractional factorial design
Plan fewer factor combinations while keeping the effects that remain confounded visible.
Example: label adhesion
Read guideDesign of Experiments
Analyze a central composite design
Fit a curved response to experimental runs and read predictions at stated factor settings.
Example: coating durability
Read guideDesign of Experiments
Create a central composite design
Plan corner, axial and center runs to study a curved response.
Example: coating durability
Read guideDesign of Experiments
Analyze a Box–Behnken design
Fit a curved response from edge and center runs, then check the model before using its predictions.
Example: ink mixing
Read guideDesign of Experiments
Create a Box–Behnken design
Plan runs at edge midpoints and the center to study a curved response.
Example: ink mixing
Read guidec Chart
Build a c chart
Monitor defect counts when each inspected unit offers the same inspection opportunity.
Example: glass-panel defects
Read guideCapability & Performance
Compare process performance with specifications
Describe the location and spread of measurements against supplied specification limits.
Example: alignment-pin diameters
Read guideCUSUM Chart
Build a CUSUM chart
Accumulate departures from a reference mean to look for small persistent changes.
Example: adhesive dispense mass
Read guideEWMA Chart
Build an EWMA chart
Use a weighted average of recent measurements to look for small process changes.
Example: sealing temperature
Read guideI-MR Chart
Build an I-MR chart
Monitor individual measurements and the changes between consecutive readings.
Example: adhesive viscosity
Read guidenp Chart
Build an np chart
Monitor nonconforming-unit counts when every inspection sample has the same size.
Example: bottle-cap inspection
Read guidep Chart
Build a p chart
Monitor the proportion of nonconforming units when inspection sample sizes vary.
Example: carton labeling
Read guideu Chart
Build a u chart
Monitor defects per inspection unit when the number of units inspected varies.
Example: weld defects
Read guideX̄–R control chart
Build an X̄–R chart
Use subgrouped measurements to review changes in process mean and variation.
Example: bottle filling
Read guideX̄–S Chart
Build an X̄–S chart
Use subgroup averages and standard deviations to monitor process mean and variation.
Example: coating thickness
Read guideZ-MR Chart
Build a Z-MR chart
Compare measurements from different products on a standardized scale.
Example: three spacer products
Read guideBias
Check measurement bias
Compare repeated measurements with a known reference to estimate their average offset.
Example: pressure indicator
Read guideCg / Cgk (Type 1)
Run a Type 1 gauge study
Use repeated measurements of one reference part to compare measurement spread and bias with a tolerance.
Example: length comparator
Read guideGRR (ANOVA)
Run a crossed GRR study
Use repeated measurements by several operators to separate measurement variation from differences between parts.
Example: valve bores
Read guideGRR (Average & Range)
Run an Average & Range GRR study
Use repeated measurements by several operators to estimate measurement scatter with the Average & Range method.
Example: spacer thickness
Read guideGRR (Range)
Run a short Range GRR study
Use paired readings from two operators to make an initial estimate of overall measurement variation.
Example: clip width
Read guideKappa (Attributes)
Check agreement between inspectors
Compare categorical ratings between inspectors and with an optional reference decision.
Example: cosmetic inspection
Read guideLinearity
Check bias across a measuring range
Use repeated readings of several reference standards to see whether measurement bias changes with size.
Example: optical gauge
Read guideStability
Check measurement stability over time
Use repeated readings of the same reference to follow changes in measurement average and short-term spread.
Example: bench balance
Read guide