Chi_squared

Calculates the Chi-squared statistic from a contingency table constructed using two input vectors.
Both parameters can be a categorical (non-numeric) variable, allowing flexibility in input types.
Variable <code>a</code> is used as rows, and <code>b</code> as columns in the contingency table.

Provides tools to measure connection and independence between variables
without relying on linear models. Includes functions to compute Eta squared,
Chi-squared, and Cramer V. The main advantage of this package is that it works
without requiring parametric assumptions. The methods implemented are based on
educational material and statistical decomposition techniques, not directly on
previously published software or articles.

Pietro Frascino

Connection

Measures of Independence and Connection Without Linear Models

Chi_squared function

<dl><dt>a</dt>
<dd>First array or vector. Can be numeric or a categorical factor.</dd>
<dt>b</dt>
<dd>Second array or vector. Can be numeric or a categorical factor.</dd></dl>

Arguments

Chi-squared Statistic for Association Between Two Variables — Chi_squared

<dl>

<dt>a</dt>
<dd>First array or vector. Can be numeric or a categorical factor.</dd>


<dt>b</dt>
<dd>Second array or vector. Can be numeric or a categorical factor.</dd>

</dl>

Chi_squared: Chi-squared Statistic for Association Between Two Variables

Description

Usage

Value

Arguments

Examples