Analytics Database provides built-in support for performing chi-square tests, which you can use to analyze relationships between categorical variables.
TD_ChiSq performs Pearson's chi-squared (χ2) test for independence, which determines if there is a statistically significant difference between the expected and observed frequencies in one or more categories of a contingency table (also called a cross tabulation). This function takes two or more columns as input and returns a result set that contains the chi-square statistic, the degrees of freedom, and the p-value for the test.
- One-tailed, upper-tailed
- One-sample
- Unpaired
Why Use Chi-Square Test?
The chi-square test is a statistical method used in analytics to determine whether there is a significant relationship between two categorical variables.
- For example, imagine you want to determine whether there is a relationship between the level of education and the employment status of a group of individuals. You can use the chi-square test to analyze the data and determine whether there is a significant association between the two variables.
- The chi-square test compares the observed frequencies of each category with the expected frequencies. If there is a significant difference between the observed and expected frequencies, you can conclude that there is a relationship between the two variables.
Overall, the chi-square test is a tool for analyzing categorical data and identifying relationships between variables. Used in fields like marketing, social sciences, healthcare, and finance, and so on.