Key Points
References
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Key Concepts
Pearson's chi square test (goodness of fit)
https://en.wikipedia.org/wiki/Chi-squared_test
A chi-squared test, also written as χ2 test, is any statistical hypothesis test where the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. Without other qualification, 'chi-squared test' often is used as short for Pearson's chi-squared test. The chi-squared test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories.
In the standard applications of this test, the observations are classified into mutually exclusive classes, and there is some theory, or say null hypothesis, which gives the probability that any observation falls into the corresponding class. The purpose of the test is to evaluate how likely the observations that are made would be, assuming the null hypothesis is true.
Chi-squared tests are often constructed from a sum of squared errors, or through the sample variance. Test statistics that follow a chi-squared distribution arise from an assumption of independent normally distributed data, which is valid in many cases due to the central limit theorem. A chi-squared test can be used to attempt rejection of the null hypothesis that the data are independent.
do the observations for this experiment, fit the model we created?
testing - null hypothesis assumes no relationship exists in the model variables
set a significance level ( alpha ) of X% as the target discriminator ( 5% )
degrees of freedom = n - 1 ( data points ) ( dof = 5 )
Potential Value Opportunities
Potential Challenges
Candidate Solutions
Step-by-step guide for Example
sample code block