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Cochran's Q test in Excel tutorial

Univariate plots

Before using advanced analysis methods you must first of all reveal the data in order to identify trends, locate anomalies or simply have available essential information such as the minimum, maximum or mean of a data sample. XLSTAT offers you a large number of descriptive statistics and charts which give you a useful and relevant preview of your data. Although you can select several variables (or samples)...

Parallel coordinates plots

What is Parallel Coordinates Visualization This visualization method is useful for data analysis when you need to describe groups using variables. For example, this method could be used on groups generated by Agglomerative Hierarchical Clustering. Using this method you are able to visually determine which variables are discriminative. Structure of a Parallel Coordinates plot If you consider N observations...

Cochran's Q test

What is Cochran’s Q test The Cochran’s Q test is presented as a particular case of the Friedman’s test (comparison a k paired samples) when the variable is binary. As a consequence, the null H0 and alternative Ha hypotheses for the Cochran’s Q test are: H0: the k treatments are not significantly different. Ha: the k treatments are significantly different. Asymptotic p-value computation is available....

McNemar's test

McNemar's test definition and advantages McNemar’s test is a special case of the Cochran’s Q test when there are only two treatments. As for the Cochran’s Q test, the variable of interest is binary. However, the McNemar’s test has two advantages: Obtaining an exact p-value is possible; The data can be summarized in a 2x2 contingency table. In the case of the two-tailed (or two-sided) test, the null...

Complete disjunctive tables (Creating dummy variables)

What is a complete disjunctive table A disjunctive table is a drill-down of a table defined by n observations and q qualitative variables V(1), V(2), ... V(q) into a table defined by n observations and p indicators (or dummy variables) where p is the sum of the numbers of categories of the q variables: each variable V(j) is broken down into a sub-table with q(j) columns where column k contains 1's...

Quantiles estimation

Quantiles and percentiles Quantiles are defined by ordering data into q equally sized data subsets and noting the boundaries. The kth q-quantile for a random variable X is the value x such that the probability that the random variable will be less than x is at most k / q and the probability that the random variable will be more than x is at most (q − k) / q. Let 0 < q < 1. The q-quantile of a variable...

Redundancy analysis (RDA)

What is Redundancy Analysis Redundancy Analysis (RDA) was developed by Van den Wollenberg (1977) as an alternative to Canonical Correlation Analysis (CCorA). Redundancy Analysis allows studying the relationship between two tables of variables Y and X. While the Canonical Correlation Analysis is a symmetric method, Redundancy Analysis is non-symmetric. In Canonical Correlation Analysis, the components...

Ordinary Least Squares regression (OLS)

Equations for the Ordinary Least Squares regression Ordinary Least Squares regression (OLS) is more commonly named linear regression (simple or multiple depending on the number of explanatory variables). In the case of a model with p explanatory variables, the OLS regression model writes: Y = β0 + Σj=1..p βjXj + ε where Y is the dependent variable, β0, is the intercept of the model, X j corresponds...

Principal Component Regression (PCR)

What is Principal Component Regression PCR (Principal Components Regression) is a regression method that can be divided into three steps: The first step is to run a PCA (Principal Components Analysis) on the table of the explanatory variables, Then run an Ordinary Least Squares regression (OLS regression) also called linear regression on the selected components, Finally compute the parameters of the...