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.
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.
Negative?
Critical values of a chi-square test depend on the degrees of freedom.
A chi square is square of standard normal variate, so all values are positive
A large value for the chi-squared statistic indicates that one should be suspiciuous of the null hypothesis, because the expected values and the observed values willdiffer by a large amount
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.
Never! In Tai Chi, for power, the hips should *never* be squared!
You seem to be referring to the Pearson chi-square test-of-fit statistic. To do this you need not only the observed values in a frequency table (which you have) but the expected (or theoretical) values for that table.In practical situations the expected values are obtained by making some educated guess about what distribution the observed values came from, estimating the parameters of that distribution and then using the estimated distribution to obtain the required expected values to calculate the chi-square.In short, you need more information.
It means that the observed values in the experiment all exactly match the expected values. That is unlikely, unless the experiment was "fixed".
It is necessary to have a null hypothesis. This must be used to calculate expected values of the variable under study for various categories. These must be at least 5: if not, you need to combine categories. You also need the observed values. Finally, you need to know the degrees of freedom for the chi-square variable.
A Chi-square table is used in a Chi-square test in statistics. A Chi-square test is used to compare observed data with the expected hypothetical data.