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The characteristics of the chi-square distribution are: A. The value of chi-square is never negative. B. The chi-square distribution is positively skewed. C. There is a family of chi-square distributions.
The most common use for a chi-square test is a "goodness of fit" test. Suppose you have a set of observations. These may be classified according to one or more characteristics. You also have a hypothesis about what the distribution should be. The chi-square statistic is an indicator of how well the observed values agree with the values that you might expect if your hypothesis were true.
You could calculate it by integrating the chi-square probability distribution function but you are likely to be much better off using a table in a book or on the web.
i don't know. i think very complicated.
A chi-square test is often used as a "goodness-of-fit" test. You have a null hypothesis under which you expect some results. You carry out observations and get a set of results. The expected and observed results are used to calculate the chi-square statistic. This statistic is used to test how well the observations match the values expected under the null hypothesis. In other words, how good the fit between observed and expected values is.