In a goodness of fit test using chi-square, how many categories or populations d

Question

In a goodness of fit test using chi-square, how many categories or populations do you need to be able to do this analysis? A) At least 3 (B) greater than 3 (C) At least 2 (D) At least 4 In a simple linear regression, the proportion of the total sample variability around y that is explained by the simple linear regression equation is called (A) Regression coefficient B) Coefficient of determination (C) Correlation coefficient (D) Slope of the regression line In testing the null hypothesis that the means for three different populations are the same using ANOVA. the F-distribution distribution is used. (A) True (B) False What happens to the coefficient of multiple determination, R^2, as more independent variables are added to the multiple regression model? (A) It increases (B) It decreases (C) It remains unchanged (D) None of the above. When independent variables are highly linearly correlation among the independent variables in a multiple regression analysis is called (A) the coefficient of correlation (B) multicollinearity (C) the coefficient of determination (D) multiple correlation coefficient

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NULL AND ALTERNATIVE HYPOTHESES

The null and alternative hypotheses for a goodness of fit test look different than some of our other hypothesis tests. One reason for this is that a chi-square goodness of fit test is a nonparametric method. This means that our test does not concern a single population parameter. Thus the null hypothesis does not state that a single parameter takes on a certain value.

We start with a categorical variable with n levels and let pi be the proportion of the population at level i. Our theoretical model has values of qi for each of the proportions. The statement of the null and alternative hypotheses are as follows:

At least two categories are needed. C option

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