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Standard Edition The powerful statistical analysis and regression you’d expect from an expensive statistics package. Except without the complexity or the cost.

The leading software package for in-depth statistical analysis in Microsoft Excel for over 20-years.

Analyse-it is the unrivaled statistical add-in for Excel. Describe and visualize data, uncover the relationships hidden in your data, and get answers to the important questions so you can make informed, intelligent decisions.

  • Analyse-it has helped tremendously. Previously I used Prism and Microsoft Excel, but Analyse-it has made my life so much easier and saved so much time.
    Man Khun Chan, M.Sc., ART
    Test Development Medical Technologist
    The Hospital For Sick Children, Toronto, Canada
  • I purchased Analyse-it and I love it. Thank you so much for the superb and easy to use program. Technical support is the best I have ever seen.
    Addy Alsumidaie, MD, Ph.D.
    Director, Clinical Development
    Johnson and Johnson
  • We have incorporated Analyse-it into our real-time daily QA analysis, making us both more efficient and proactive in addressing QA issues. Analyse-it has saved us time and provides the statistics and professional reports we need.
    Dalton Seegert
    Medical Technologist QA/Ancillary Testing
    Veterans Administration
  • Using Analyse-it we have improved several of our quality control testing steps, better controlled our decision making and are still undergoing process improvements.
    Laura Smith
    Production manager
    The Binding Site
  • Although we only scratch the surface of Analyse-it’s capabilities, we have a very high volume of use for the statistics we need. It’s saved us time and the reports look professional.
    Michael Savage
    Chemistry Supervisor
    Baptist Hospital East

Describe and visualize data to gain greater insight

You won't get far if you don't understand the data you're looking at. Powerful descriptive statistics and customizable plots quickly reduce a large amount of data into easy to understand summaries, and to identify outlying and interesting observations.

Make informed decisions

It’s important to know when the effect of a factor or intervention is real and significant. The wide range of statistical hypothesis tests and estimators ensure you make confident informed decisions. Don’t waste your time and money implementing ineffective interventions or costly changes that won’t be effective.

Advanced capabilities to uncover relationships

Identify relationships between variables in your data so you can understand what's important. Correlation and scatter plots help you spot hidden relationships between variables. Simple regression based on functions such as linear, log, power, or polynomials, and advanced models like multiple regression, ANOVA, ANCOVA and GLM let you model the relationship between variables so you can understand underlying mechanisms and make predictions. Analyse-it's interactive approach makes it easy to iteratively perfect the model until you're happy with it.

Reduce complexity to identify what's important

With complex datasets you often can't see the wood for the trees. What's important can be swamped by the sheer amount of data or the number of variables you're dealing with. With PCA you can quickly identify how the variables are related, and distinct groups of observations, then visualize them with monoplots and biplots (an idle boast: it's probably the most advanced implementation of biplots available in any commercial package!). Latent underlying variables can be easily uncovered with factor analysis.

We haven't forgotten about categorical data...

Many statistical packages excel with continuous numeric data, but neglect categorical data. Analyse-it doesn't! Categorical data analysis is supported throughout with an extensive range of statistical procedures. You can describe data, test hypotheses, or estimate an effect. We've also included some insightful plots you won't find elsewhere, like Mosaic plots, so you can really see how the variables interact.

Integrated into Microsoft Excel, so it's easy to use...

That's right. Analyse-it integrates into Microsoft Excel 2007, 2010, 2013, and 2016 for Microsoft Windows. There's virtually no learning curve, and the intuitive user interface and logical task-based workflow makes sense to those of us that aren’t programmers or full-time statisticians.

All your data and results are kept in Excel workbooks, making it easy to collaborate and share them with colleagues, and meaning there's no locked-in file format.

Software you can trust

We've developed statistical software for more than 25 years, have a reputation for high-quality software, and have worked worked with some large companies on the back of that solid reputation.

But it's our customers that matter - the researchers, manufacturers, and laboratories, including many of the world's top companies and universities. Check out the latest reviews on Capterra, in the meantime here are a few of our customers...

Accurate and reliable

You might have heard Microsoft Excel isn't up to the job of statistical analysis. It isn't. The built-in functions just aren't built for accuracy. So we don't use a single one. Instead, Analyse-it handles all of the calculations internally, using reliable algorithms and IEEE 754 double floating point precision. And the results to prove it? See Analyse-it's performance against the NIST-StRD.

Validated, tested at every stage

We've conducted thousands of tests to put Analyse-it through its paces. They cover all releases and service packs of Microsoft Excel 2007, 2010, 2013 and 2016. What's more, validation tests are run automatically after every change to the software so you can be confident the statistics are correct. And stay correct. See our development & validation process.

Dependable support

No need to resort to old textbooks. Analyse-it includes detailed help that’s written in plain English. And if you get stuck we’re here for extra help - aiming to answer your queries within 24 hours.

Technical specification

System requirements
  • Microsoft Excel 2007, 2010, 2013 or 2016 (32- and 64-bit)
  • Microsoft Windows Vista, 7, 8, 10, Server 2003, 2008, 2012, or 2016
  • 2GB RAM minimum recommended
  • 80MB disk space
  • Sum, Mean, Variance, SD, CV%, Skewness, Kurtosis
  • Geometric Mean, Harmonic Mean new in v4.50
  • Median, Minimum, Maximum, Range, IQR
  • Quantiles
  • Mode
  • Histogram with optional normal overlay
  • Frequency polygon
  • Dot plot – jittered, aligned, spread points and vary point symbol/color
  • Skeletal box plot, Tukey outlier box plot, Quantile box plot
  • Mean error bar plot, Mean confidence diamond plot
  • CDF plot with optional Kolmogorov-Smirnov confidence band
  • Normal Q-Q plot with optional Lilliefors confidence band
  • Shapiro-Wilk, Anderson-Darling, and Kolmogorov-Smirnov tests for normality
  • Z test for mean with known population SD
  • Student’s one sample t-test for mean
  • Wilcoxon test for mean/median
  • Sign test for median
  • X2 test for variance
  • Mean estimate with t-based or Z-based confidence interval
  • Median with Thompson-Savur confidence interval
  • Hodges-Lehmann pseudo-median with Tukey confidence interval
  • Variance with X2-based confidence interval
  • Frequency table – frequency, cumulative frequency, relative frequency, cumulative relative frequency
  • Frequency bar plot with optional cumulative frequency line
  • Frequency pie plot
  • Binomial exact test for proportions
  • Score Z test for binomial proportions
  • Pearson X2 and Likelihood ratio G2 test for multinomial proportions
  • Proportion with Clopper-Pearson exact or Wilson score confidence interval
Compare groups
  • Descriptive statistics by group
  • Side-by-side dot plots, mean plots, box plots by group
  • Z test for difference in means with known population SDs
  • Student’s independent samples t-test for difference between means
  • Welch’s t-test for difference between means with unequal variances
  • Wilcoxon-Mann-Whitney test for difference between means/medians
  • 1-way between-subjects ANOVA for equality of means
  • Welch’s ANOVA for equality of means with unequal variances
  • Kruskal-Wallis test for equality of mean/medians
  • Mean difference with t-based, Welch-Satterthwaite t-based, or Z-based confidence interval
  • Cohen’s and Hedge’s g standardized mean difference with non-central t-based confidence interval
  • Hodges-Lehmann location shift with Moses confidence interval
  • Multiple comparisons procedures: Student’s t (individual comparisons), Tukey-Kramer (all pairs), Dunnett (against control), Hsu (with best), Scheffe (all contrasts), Steel (non-parametric against control), Dwass-Steel-Critchlow-Fligner (non-parametric all pairs), Wilcoxon (non-parametric individual comparisons)
  • Mean-Mean scatter plot for multiple comparisons
  • F-test for variance ratio
  • Bartlett, Levene, and Brown-Forsythe tests for homogeneity of variances
Compare pairs
  • Descriptive statistics for each group
  • Side-by-side dot plots, mean plots, box plots by group
  • Difference plot with identity line and optional histogram of differences
  • Z-test for difference in means with known population SDs
  • Student’s paired samples t-test for difference between means
  • Wilcoxon signed ranks test for difference between means/medians
  • Sign test for difference between medians
  • 1-way within-subject ANOVA for equality of means
  • Friedman test for equality of medians
  • Mean difference with t-based or Z-based confidence interval
  • Cohen’s and Hedge’s g standardized mean difference with non-central t-based confidence interval
  • Median difference with Thompson-Savur confidence interval
  • Hodges-Lehmann location shift with Tukey confidence interval
Contingency tables
  • Contingency table
  • Grouped frequency plot
  • Stacked frequency plot
  • Pearson X2 test for independence / equality of proportions
  • Likelihood ratio G2 test for independence
  • Mosaic plot for association with color by category or residual
2x2 related tables
  • McNemar-Mosteller exact test for symmetry / marginal homogeneity
  • Score Z test for difference between proportions
  • Proportion difference (risk difference) with Newcombe score or Tango score confidence interval
  • Odds ratio with Binomial exact or Wilson score confidence interval
2x2 tables
  • Fisher exact test for independence
  • Score Z test for difference between proportions
  • Proportion difference (risk difference) with Miettinen-Nurminen score, Newcombe score, or Tango score confidence interval
  • Proportion ratio (risk ratio) with Miettinen-Nurminen score or Newcombe confidence interval
  • Odds ratio with Hypergeometric exact or Miettinen-Nurminen score confidence interval
Fit model – ANOVA / ANCOVA / Regression
Linear Fits
  • Simple linear regression
  • Polynomial regression (2nd to 6th order)
  • Logarithmic regression
  • Exponential regression
  • Power regression
  • Multiple linear regression
  • ANOVA new in v4.80
  • ANCOVA new in v4.80
  • Advanced models with simple, crossed, polynomial and factorial terms, with categorical explanatory variables coded as dummy variables
Other Fits
  • Binary logistic regression
  • Model equation
  • Summary of fit – R2, AIC, BIC
  • Parameter estimates – beta, confidence intervals, VIF, standardized beta
  • Scatter plot with fit line and optional confidence bands
  • F-test effect of model
  • Predicted against actual plot
  • F-test effect of each term in model
  • Leverage plot for effect of each term
  • Residual plot – raw, standardized
  • Sequence and Lag-1 plots
  • Outlier and Influence plot
  • Cook's D influence
  • Predict Y for X
  • Effect means for categorical variables new in v4.80
  • Main effect and interaction plots for categorical variables new in v4.80
  • Multiple comparisons of effect means: Student’s t (individual comparisons), Tukey-Kramer (all pairs), Dunnett (against control), Hsu (with best), Scheffe (all contrasts) new in v4.80
  • F-test for lack of fit test for simple regression models
  • Save model variables back to the dataset: Fitted Y, Residuals, Standardized Residuals, Studentized Residuals, Leverage, Cook's Influence
  • Correlation matrix with color map on coefficients
  • Covariance matrix
  • Scatter plot
  • Scatter plot matrix
  • Vary points by color of based on a factor
Correlation / Association
  • Pearson r correlation coefficient with Fisher’s Z confidence interval
  • Pearson test for linear association
  • Spearman rs correlation coefficient with Fisher’s Z confidence interval
  • Kendall tau correlation coefficient with Samara-Randles confidence interval
  • Kendall test for monotonic association
Item reliability new in v4.80
  • Cronbach’s alpha (standardized and unstandardized)
  • Deleted Cronbach’s alpha for each item
PCA new in v3.80
  • Eigenvalues / Eigenvectors
  • Coefficient matrix with color map to reveal relationships
  • Classic Gabriel bi-plot with variables as vectors and observations as points
  • Gower-Hand bi-plot with variables and observations as points
  • Predict new observations / variables
  • Reflect, rotate, and scale bi-plot
  • Correlation mono-plot to show relationship between variables
  • Scree plot
Common Factor Analysis new in v3.90
  • Maximum likelihood factor extraction
  • Factor pattern / structure matrices with color map to reveal structure
  • 12 factor orthogonal/oblique rotations including Varimax, Oblimin
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