© 2013 Pearson Education, Inc. Active Learning Lecture Slides For use with Classroom Response Systems Essential Statistics: Exploring the World through Data, 1e by Gould and Ryan Chapter 10: Association between Categorical Variables Slide
© 2013 Pearson Education, Inc. When two categorical variables are analyzed, they are often displayed in a summary table that displays frequencies for the outcomes, which is called a A.one-way table B.two-way table C.three-way table D.frequency distribution table Slide
© 2013 Pearson Education, Inc. When two categorical variables are analyzed, they are often displayed in a summary table that displays frequencies for the outcomes, which is called a A.one-way table B.two-way table C.three-way table D.frequency distribution table Slide
© 2013 Pearson Education, Inc. True or False The expected counts are the numbers of observations we would see in each cell of the summary table if the null hypotheses were true. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False The expected counts are the numbers of observations we would see in each cell of the summary table if the null hypotheses were true. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False Expected counts are actually long-run averages. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False Expected counts are actually long-run averages. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False When calculating expected counts in a table, it makes a difference which variable you consider first. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False When calculating expected counts in a table, it makes a difference which variable you consider first. A.True B.False Slide
© 2013 Pearson Education, Inc. The statistic that measures the amount that our expected counts differ from our observed counts is called the A. z-statistic B. t-statistic C. chi square statistic D. F-statistic Slide
© 2013 Pearson Education, Inc. The statistic that measures the amount that our expected counts differ from our observed counts is called the A. z-statistic B. t-statistic C. chi square statistic D. F-statistic Slide
© 2013 Pearson Education, Inc. True or False If the data conform to the null hypothesis, then the value of the chi-square statistic will be small. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False If the data conform to the null hypothesis, then the value of the chi-square statistic will be small. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False Small values of the chi-square statistic make us suspicious of the null hypothesis. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False Small values of the chi-square statistic make us suspicious of the null hypothesis. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False One reason why the chi-square statistic uses squared differences is that by squaring the differences, we always get a positive value. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False One reason why the chi-square statistic uses squared differences is that by squaring the differences, we always get a positive value. A.True B.False Slide
© 2013 Pearson Education, Inc. The chi-square distribution A.allows for only positive values B.is (usually) not symmetric C.is (usually) right-skewed D.all of the above Slide
© 2013 Pearson Education, Inc. The chi-square distribution A.allows for only positive values B.is (usually) not symmetric C.is (usually) right-skewed D.all of the above Slide
© 2013 Pearson Education, Inc. When the expected counts are exactly the same as the observed counts for every cell of the table, then the chi-square statistic has a value of A.–1 B.1 C.0 D.infinity Slide
© 2013 Pearson Education, Inc. When the expected counts are exactly the same as the observed counts for every cell of the table, then the chi-square statistic has a value of A.–1 B.1 C.0 D.infinity Slide
© 2013 Pearson Education, Inc. True or False The shape of the chi-square distribution depends on a parameter called the degrees of freedom. The lower the degrees of freedom, the more skewed the shape of the chi-square distribution. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False The shape of the chi-square distribution depends on a parameter called the degrees of freedom. The lower the degrees of freedom, the more skewed the shape of the chi-square distribution. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False The chi-square distribution provides a good approximation to the sampling distribution of the chi-square statistic only if the sample size is large. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False The chi-square distribution provides a good approximation to the sampling distribution of the chi-square statistic only if the sample size is large. A.True B.False Slide
© 2013 Pearson Education, Inc. The chi-square distribution provides a good approximation to the sampling distribution of the chi-square statistic only if the sample size is large. The sample size is large enough if each expected count is A.5 or higher. B.10 or higher C.15 or higher D.25 or higher Slide
© 2013 Pearson Education, Inc. The chi-square distribution provides a good approximation to the sampling distribution of the chi-square statistic only if the sample size is large. The sample size is large enough if each expected count is A.5 or higher. B.10 or higher C.15 or higher D.25 or higher Slide
© 2013 Pearson Education, Inc. If we test the association, based on two independent samples, between the grouping variable and the categorical response variable, the test is called a A.test for dependence B.test for independence C.test of homogeneity D.goodness-of-fit test Slide
© 2013 Pearson Education, Inc. If we test the association, based on two independent samples, between the grouping variable and the categorical response variable, the test is called a A.test for dependence B.test for independence C.test of homogeneity D.goodness-of-fit test Slide
© 2013 Pearson Education, Inc. If we test the association, based on one sample and two categorical response variables, the test is called a A.test for dependence B.test for independence C.test of homogeneity D.goodness-of-fit test Slide
© 2013 Pearson Education, Inc. If we test the association, based on one sample and two categorical response variables, the test is called a A.test for dependence B.test for independence C.test of homogeneity D.goodness-of-fit test Slide
© 2013 Pearson Education, Inc. For tests of homogeneity and independence the null hypothesis is A.There is no association between the two variables. B.There is an association between the two variables. C.The two variables are equal. D.The means of the variables are equal. Slide
© 2013 Pearson Education, Inc. For tests of homogeneity and independence the null hypothesis is A.There is no association between the two variables. B.There is an association between the two variables. C.The two variables are equal. D.The means of the variables are equal. Slide
© 2013 Pearson Education, Inc. True or False For both tests, if the p-value is less than or equal to the significance level, α (alpha), then we reject the null hypothesis. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False For both tests, if the p-value is less than or equal to the significance level, α (alpha), then we reject the null hypothesis. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False One drawback with chi-square tests is that they reveal only whether two variables are associated, not how they are associated. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False One drawback with chi-square tests is that they reveal only whether two variables are associated, not how they are associated. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False In a controlled experiment, using randomized assignment, if we reject the null hypothesis that the variables were independent, we can conclude that one variable does affect the other variable. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False In a controlled experiment, using randomized assignment, if we reject the null hypothesis that the variables were independent, we can conclude that one variable does affect the other variable. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False Causal conclusions can be based on a single observational study. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False Causal conclusions can be based on a single observational study. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False In the special case in which both categorical variables have only two categories, the test of homogeneity is identical to a z-test of two proportions, using a two-tailed alternative hypothesis. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False In the special case in which both categorical variables have only two categories, the test of homogeneity is identical to a z-test of two proportions, using a two-tailed alternative hypothesis. A.True B.False Slide
© 2013 Pearson Education, Inc. Which of the following are good guiding principles when evaluating individual research articles? A.Pay attention to how randomness is used B.Don’t rely solely on the conclusions of any single paper. C.Extraordinary claims require extraordinary evidence. D.All of the above Slide
© 2013 Pearson Education, Inc. Which of the following are good guiding principles when evaluating individual research articles? A.Pay attention to how randomness is used B.Don’t rely solely on the conclusions of any single paper. C.Extraordinary claims require extraordinary evidence. D.All of the above Slide
© 2013 Pearson Education, Inc. Which of the following are good guiding principles when evaluating individual research articles? A.Be wary of conclusions based on very complex statistical or mathematical models. B.Stick to non peer-reviewed journals. C.Confirm the accuracy on Wikipedia D.All of the above Slide
© 2013 Pearson Education, Inc. Which of the following are good guiding principles when evaluating individual research articles? A.Be wary of conclusions based on very complex statistical or mathematical models. B.Stick to non peer-reviewed journals. C.Confirm the accuracy on Wikipedia D.All of the above Slide
© 2013 Pearson Education, Inc. True or False Data dredging is the practice of stating our hypotheses after first looking at the data. Data dredging makes it more likely that we will mistakenly reject the null hypothesis. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False Data dredging is the practice of stating our hypotheses after first looking at the data. Data dredging makes it more likely that we will mistakenly reject the null hypothesis. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False A meta-analysis considers all studies done to test a particular treatment and tries to reconcile different conclusions, attempting to determine whether other factors, such as publication bias, played a role in the reported outcomes. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False A meta-analysis considers all studies done to test a particular treatment and tries to reconcile different conclusions, attempting to determine whether other factors, such as publication bias, played a role in the reported outcomes. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False An outcome of an experiment or study that is large enough to have a real effect on people’s health or lifestyle is said to have clinical significance. A.True B.False Slide
© 2013 Pearson Education, Inc. True or False An outcome of an experiment or study that is large enough to have a real effect on people’s health or lifestyle is said to have clinical significance. A.True B.False Slide