Here are the essential concepts you must grasp in order to answer the question correctly.
Chi-Square Distribution
The Chi-Square distribution is a statistical distribution that is commonly used in hypothesis testing, particularly in tests of independence and goodness of fit. It is defined by its degrees of freedom, which are determined by the sample size and the number of parameters estimated. The distribution is right-skewed, meaning it has a longer tail on the right side, and it approaches a normal distribution as the degrees of freedom increase.
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Critical Values
Critical values are the threshold points that define the boundaries of the acceptance region in hypothesis testing. They are determined based on the desired level of confidence (c) and the degrees of freedom associated with the test. For a Chi-Square test, critical values are used to decide whether to reject the null hypothesis, with values falling beyond the critical points indicating significant results.
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Level of Confidence
The level of confidence, denoted as 'c', represents the probability that the confidence interval will contain the true parameter value. Common levels of confidence include 90%, 95%, and 99%. A higher level of confidence corresponds to a wider confidence interval, which reflects greater certainty about the parameter estimate but less precision. In this case, a confidence level of 0.99 indicates a 99% certainty that the true value lies within the calculated interval.
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