This document contains 3 sets of 10 multiple choice questions about statistical analysis techniques including chi square tests, analysis of variance (ANOVA), confidence intervals, and contingency tables. The questions cover topics such as the differences between chi square and ANOVA, how to calculate confidence intervals and determine statistical significance, how to set up and interpret contingency tables, and how to perform and understand chi square tests for independence and goodness of fit.
1. ASH BUS 308 Week 5 Quiz (3 Set) NEW
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Question 1. Compared to the ANOVA test, Chi Square
procedures are not powerful (able to detect small differences).
Question 2. In confidence intervals, the width of the interval
depends only on the variation within the data set.
Question 3. The percent confidence interval is the range
having the percent probability of containing the actual
population parameter.
Question 4. The Chi Square test can be performed on
categorical (nominal) level data.
Question 5. For a one sample confidence interval, the interval
is calculated around the estimated population or standard.
Question 6. The chi square test is very sensitive to small
differences in frequency distributions.
Question 7. The probability that the actual population mean
will be outside of a 98% confidence interval is
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.
Question 9. A contingency table is a multiple row and
multiple column table showing counts in each cell.
Question 10. For a one sample confidence interval, if the
interval contains the population mean, the corresponding t-test
will have a statistically significant result – rejecting the null
2. hypothesis.
BUS 308 Week 5 Quiz Set 2
Question 1. A contingency table is a multiple row and
multiple column table showing counts in each cell.
Question 2. The Chi Square test for independence needs a
known (rather than calculated) expected frequency
distribution.
Question 3. For a two-sample confidence interval, the
interval shows the difference between the means.
Question 4. Statistical significance in the Chi Square test
means the population distribution (expected) is not the source
of the sample (observed) data.
Question 5. The chi square test is very sensitive to small
differences in frequency distributions.
Question 6. The chi square test measures differences in
frequency counts rather than measures differences (such as
done in the t and ANOVA tests).
Question 7. The Chi Square test can be performed on
categorical (nominal) level data.
Question 8. The degrees of freedom for both forms of the Chi
Square test are calculated the same way.
Question 9. In confidence intervals, the width of the interval
depends only on the variation within the data set.
Question 10. Compared to the ANOVA test, Chi Square
procedures are not powerful (able to detect small differences).
BUS 308 Week 5 Quiz Set 3
Question 1. For a one sample confidence interval, if the
interval contains the population mean, the corresponding t-test
will have a statistically significant result – rejecting the null
hypothesis.
3. Question 2. While rejecting the null hypothesis for the
goodness of fit test indicates that distributions differ, rejecting
the null for the test of independence means the variables
interact.
Question 3. A contingency table is a multiple row and
multiple column table showing counts in each cell.
Question 4. For a one sample confidence interval, the interval
is calculated around the calculated sample mean.
Question 5. Having expected frequencies of 5 or less in a Chi
Square test can increase the likelihood of a type I error –
wrongly rejecting the null hypothesis.
Question 6. The degrees of freedom for the goodness of fit
test equals
Question 7. For a one sample confidence interval, the interval
is calculated around the estimated population or standard.
Question 8. The null hypothesis for the test of independence
states that no correlation exists between the variables.
Question 9. The chi square test is very sensitive to small
differences in frequency distributions.
Question 10. The chi square test measures differences in
frequency counts rather than measures differences (such as
done in the t and ANOVA tests).
