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Chi-square test of independence is a statistical test to determine whether two independent samples (groups of observations) have an equal distribution of observations. A chi-square test of independence can be used to test whether two sample distributions have equal frequency, which can be used to determine whether or not two sets of data from two different sources are identical. When performing a chi-square test of independence, you will need to have data in two groups (which will be labeled as “group A” and “group B”). Group A contains observations that are expected to have an equal frequency
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For example, a researcher may conduct a study on the effects of a new medicine on the overall mortality rate in the community. They might use chi-square tests for independence to examine whether patients who received the new medicine had higher mortality rates than those who did not. This is an example of the use of a chi-square test to infer causation or the association between two variables. And then in one of my last paragraphs, I said (in all caps, of course): Let’s discuss some common ways this problem can be approached:
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Chi-Square test is a popular statistical test used to determine the significance of a correlation between two variables. This test involves calculating the probability of occurrence of an event based on two random variables, and can help in making inferences about the strength of the association. It is commonly used in research, where the results of a correlation study can be used to support or reject the null hypothesis, and hence make predictions or interpret the results. In this assignment, we will examine the Chi-square test of independence in statistical analysis, with specific focus on how to perform the test, and its significance