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chi square distribution
This term is a technical statistical expression used primarily in academic, scientific, and data analysis contexts. It describes a specific mathematical model used to test how well observed data fits an expected distribution or to determine if two categorical variables are independent.
Because it refers to a specific mathematical concept, it is treated as a singular entity in technical writing. It is almost never used in casual conversation and is strictly confined to the register of statistics and probability theory.
Meanings
A continuous probability distribution that arises from the sum of the squares of k independent standard normal random variables, commonly used in hypothesis testing for goodness of fit and independence in contingency tables.
The researcher used a chi square distribution to determine if the observed frequencies differed significantly from the expected values.
Examples
The professor explained how the chi square distribution is derived from the sum of squared normal variables.
The professor explained how the chi square distribution is derived from the sum of squared normal variables.
I need to check the table for the chi square distribution to find the critical value.
The test statistic follows a chi square distribution with k minus one degrees of freedom.
Does this data actually fit a chi square distribution or is the sample size too small?
The analyst used the chi square distribution to determine if the observed frequencies differed significantly from the expected ones.
The analyst used the chi square distribution to determine if the observed frequencies differed significantly from the expected ones.
The shape of the chi square distribution changes as the degrees of freedom increase.
I wonder if a chi square distribution is the most appropriate model for this specific dataset.
We assume that the variance estimator follows a chi square distribution under the null hypothesis.
The lecture focused on the relationship between the gamma distribution and the chi square distribution.
The lecture focused on the relationship between the gamma distribution and the chi square distribution.