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What conclusion is appropriate if a chi-square test produces a chi-square statistic near zero?
A chi-square statistic which is near zero suggests that the observations are exceptionally consistent with the hypothesis.
Can you get a negative chi square statistic?
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.
Is Chi-square a test of association?
The Chi-squared statistic can be used to test for association.
What does it mean when tests are chi-square based?
A chi-squared test is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true.
What will produce a large value for the chi-square statistic?
A chi-square statistic can be large if either there is a large difference between the observed and expected values for one or more categories. However, it can also be large if the expected value in a category is very small. In the first case, it is likely that the data are not distributed according to the null hypothesis. In the second case, it can often mean that that, because of low expected values, adjacent categories need to be combined before the chi-square statistic is calculated.
What conclusion is appropriate if a chi-square test produces a chi-square statistic near zero?
A chi-square statistic which is near zero suggests that the observations are exceptionally consistent with the hypothesis.
Can you get a negative chi square statistic?
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.
Is Chi-square a test of association?
The Chi-squared statistic can be used to test for association.
What statistic is commonly used when the data are reported in the form of frequencies?
Chi-square
What is the p value of a test statistic of 1.369?
The answer depends on what the test statistic is: a t-statistic, z-score, chi square of something else.
What does it mean when tests are chi-square based?
A chi-squared test is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true.
What do large values of a chi square statistic indicate?
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
What will produce a large value for the chi-square statistic?
A chi-square statistic can be large if either there is a large difference between the observed and expected values for one or more categories. However, it can also be large if the expected value in a category is very small. In the first case, it is likely that the data are not distributed according to the null hypothesis. In the second case, it can often mean that that, because of low expected values, adjacent categories need to be combined before the chi-square statistic is calculated.
Can a chi square ever tell us whether one variable causes variations in the second variable?
Regrettably, no. The most a chi-square statistic can do is to participate in the measurement of the level of association of the variation between two variables.
Can population percentage be used to calculate chi-square goodness of fit test?
No, it cannot be used to measure that.
What does the Chi-square tell me?
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.
What is A chi-square test of significance is essentially concerned with?
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.
