Multiple Correspondence Analysis (MCA) is an exploratory multivariate technique designed to analyze and visualize relationships among several categorical variables. It can be seen as the extension of Correspondence Analysis (CA), which is limited to two categorical variables, to the case of more than two variables.
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Correspondence Analysis (CA) in Excel
Correspondence Analysis (CA) is an exploratory multivariate statistical technique designed to analyze and visualize relationships in categorical data. It is most often applied to contingency tables—tables that cross-classify observations into categories—making it particularly useful for survey data, market research, linguistics, ecology, and the social sciences.
Generalized Estimating Equations (GEE) in Excel
Generalized Estimating Equations (GEE) are a method for analyzing correlated response data, such as repeated measurements on the same subject or observations clustered within groups. GEE extends the framework of Generalized Linear Models (GLMs) to situations where the assumption of independence between observations is not appropriate.
One-way Analysis of Variance (ANOVA)
One-Way ANOVA is used to determine whether there are statistically significant differences between the means of three or more independent (unrelated) groups.
Principal Component Analysis (PCA)
Principal component analysis (PCA) is a dimensionality reduction method that transforms a large set of variables into a smaller one but still containing most of the information in the large set.
XYZ 3D Scatter Plot in Excel using BESH Stat
Excel doesn’t offer a built-in 3D scatter plot. It do offer a 3D line chart (multiple line series presented side by side) but that cannot be easily adapted to scatter plot unless one of the variables is integer with only handful of distinct values. Recently I discovered a Gabor’s Doka the 3D scatter plot for …
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BESH Stat version 0.21 released
I’m happy to announce the release of new BESH Stat version 0.21. The main new feature in this version is the Negative Binomial regression model. It fits the NB2 parametrization model. Results match those provided by R glm.nb function.
Negative Binomial Regression model (NB2)
Negative binomial regression is used for modeling count variables, usually for over-dispersed count outcome variables (e.g. accounting for the extra-Poisson variation). BESH Stats fits negative binomial regression with the traditional NB2[1] parametrization
Poisson regression (Generalized linear model GLM)
Poisson regression is fundamental to the modeling of count data. It is the simplest count regression model; and similar to regular multiple linear regression except that the dependent variable is an observed count that follows the Poisson distribution.
Somers’ D
BESH Stat starting version 0.21 offers the computation of the Somers’ d statistics which is a non-parametric measure of the strength and direction of association that exists between an ordinal dependent variable and an ordinal independent variable in contingency table.