The chi-square test for independence is an extension of the

Question

Question 1: Compared to the ANOVA test, Chi-Square procedures are not powerful (able to detect small differences).

• True
• False

Question 2: Chi-square tests are parametric in nature - requiring data that fit a specific distribution/shape.

• True
• False

Question 3: The null hypothesis for the test of independence states that no correlation exists between the variables.

• True
• False

Question 4: Point estimates provide less confidence in indicating a parameter's value than a confidence interval.

• True
• False

Question 5: For a two sample confidence interval, the interval shows the difference between the means.

• True
• False

Question 6: The Chi-square test measures differences in frequency counts rather than differences in size (such as the t-test and ANOVA).

• True
• False

Question 7: For a one sample confidence interval, if the interval contains the μm , the corresponding t-test will have a statistically significant result - rejecting the null hypothesis.

• True
• False

Question 8: A confidence interval is generally created when statistical tests fail to reject the null hypothesis - that is, when results are not statistically significant.

• True
• False

Question 9: The probability that the actual population mean will be outside of a 98% confidence interval is

• 1%
• 2%
• 4%

Question 10: 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