Question 11 The Chisquare test measures differences in frequ

Question 1.1. The Chi-square test measures differences in frequency counts rather than differences in size (such as the t-test and ANOVA).

   True

   False

Question 2.2. Confidence intervals provide an indication of how much variation exists in the data set.

   True

   False

Question 3.3. Chi-square tests rarely have type I errors.

   True

   False

Question 4.4. The goodness of fit test determines if a data set distribution/shape matches a standard or hypothesized distribution.

   True

   False

Question 5.5. The Chi-square test for independence is an extension of the goodness of fit test to see if multiple groups are distributed according to expected distributions for each variable.

   True

   False

Question 6.6. For a one sample confidence interval, the interval is calculated around the calculated sample mean (m).

   True

   False

Question 7.7. While rejecting the null hypothesis for the goodness of fit test means distributions differ, rejecting the null for the test of independence means the variables interact.

   True

   False

Question 8.8. Point estimates provide less confidence in indicating a parameter’s value than a confidence interval.

   True

   False

Question 9.9. The goodness of fit test requires the expected distribution to be equally distributed across the categories.

   True

   False

Question 10.10. Compared to the ANOVA test, Chi-Square procedures are not powerful (able to detect small differences).

   True

   False

Solution

Q1.1 TRUE

Q2.2 TRUE (Because margin of error in confidence interval provides the idea about the variation)

Q3.3 TRUE

Q4.4 TRUE

Q5.5 FALSE (chi sqauare test for independence is only used to test if the attributes are independent of each other)

Q6.6 TRUE

Q7.7 TRUE

Q8.8 TRUE (interval estimates provides the whole interval inn which the actual parametric value expected to lie, while point estimate result in single estimate, and if it it is wrong we don\'t have othre option. thus interval estimate is more appropriate)

Q1.9 FALSE (not necessary that expected frequency is equally distributed)

Q1.10 TRUE (Because ANOVA is parametric test and chi square is non parametric test and we know that parametric tests are more powerful than nn parametric tests)