**7.14 Learning Activity**

In these activities you will use the file *Census.sav*. The overall goal is to run the **Independent-Samples T Test**, to interpret the output and visualize the results with an error bar chart.

The file Census.sav, a PASW Statistics data file from a survey done on the general adult population. Questions were included about various attitudes and demographic characteristics.

1. We want to see whether men and women differ in their mean socioeconomic index (*sei*) and their age when their first child was born (*agekdbrn*). First, use the Explore procedure to view the distributions of these two variables by gender. Are they similar or different? Do you see any problems with doing a t test?

2. Now do a t test for each variable, by gender. Is the homogeneity of variance assumption met, or not? What do you conclude about mean differences by gender?

3. Create an error bar chart for each variable by gender. Is the graph consistent with the result from the t test?

4. Now do the same analysis with the variable *race*, testing whether there are differences in *sei *and *agekdbrn *comparing whites to blacks. Although *race *has three categories, you can use only two categories in the t test. As before, first use the Explore procedure to view the distributions of these two variables by race? Are they similar or different? Do you see any problems with doing a t test?

5. Now do a t test for each variable, by white versus black. Is the homogeneity of variance assumption met, or not? What do you conclude about mean differences between whites and blacks?

6. Create an error bar chart for each variable by race. Is the graph consistent with the result from the t test?

7. *For those with more timed: *How could you display an error bar chart with only the categories of white and black, not other? There are at least two methods.

**8.10 Learning Activity**

The overall goal of this learning activity is to use the **Paired-Samples T Test**.

The SPSS customer satisfaction data file SPSS_CUST.SAV. This data file was collected from a random sample of SPSS customers asking about their satisfaction with the software, service, and other features, and some background information on the customer and their company.

1. One variable in the customer survey asked about agreement that SPSS products are a good value (*gdvalue*). A second question asked about agreement that SPSS offers high quality products (*hiqualty*). Use a paired-samples t test to see whether the means of these two questions differ (they are measured on a five-point scale). What do you conclude?

2. Then test whether there is a mean difference between agreement that SPSS products are easy to learn (*easylrn*) and SPSS products are easy to use (*easyuse*). What do you conclude?

3. Could we use a paired-sample t test to compare how long a customer has used SPSS products (*usespss*) and how frequently they use SPSS (*freqspss*)? Why or why not?

**9.20 Learning Activity**

The overall goal of this learning activity is to use One-Way ANOVA with post hoc tests to explore the relationship between several variables. You will use the PASW Statistics data file *Census.sav*.

The file Census.sav, a PASW Statistics data file from a survey done on the general adult population. Questions were included about various attitudes and demographic characteristics.

1. Investigate how the number of siblings (*sibs*) varies by highest degree (*degree*). Ask for appropriate statistics.

2. Is the assumption of homogeneity of variance met? Is the ANOVA test significant at the .01 level?

3. Do a post hoc analysis, if justified. Ask for both the Bonferroni and Scheffe tests? What do you conclude from these tests? Which education groups have different mean numbers of children? Are the Bonferroni and Scheffe tests consistent?

4. Create an error bar chart to display the mean differences for *sibs *by *degree*. Is the error bar chart a correct representation of which means are different?

5. Now do another analysis of political position (*polviews*) by *degree*. Repeat the same

**7.14 Learning Activity**

In these activities you will use the file *Census.sav*. The overall goal is to run the **Independent-Samples T Test**, to interpret the output and visualize the results with an error bar chart.

The file Census.sav, a PASW Statistics data file from a survey done on the general adult population. Questions were included about various attitudes and demographic characteristics.

1. We want to see whether men and women differ in their mean socioeconomic index (*sei*) and their age when their first child was born (*agekdbrn*). First, use the Explore procedure to view the distributions of these two variables by gender. Are they similar or different? Do you see any problems with doing a t test?

2. Now do a t test for each variable, by gender. Is the homogeneity of variance assumption met, or not? What do you conclude about mean differences by gender?

3. Create an error bar chart for each variable by gender. Is the graph consistent with the result from the t test?

4. Now do the same analysis with the variable *race*, testing whether there are differences in *sei *and *agekdbrn *comparing whites to blacks. Although *race *has three categories, you can use only two categories in the t test. As before, first use the Explore procedure to view the distributions of these two variables by race? Are they similar or different? Do you see any problems with doing a t test?

5. Now do a t test for each variable, by white versus black. Is the homogeneity of variance assumption met, or not? What do you conclude about mean differences between whites and blacks?

6. Create an error bar chart for each variable by race. Is the graph consistent with the result from the t test?

7. *For those with more timed: *How could you display an error bar chart with only the categories of white and black, not other? There are at least two methods.

**8.10 Learning Activity**

The overall goal of this learning activity is to use the **Paired-Samples T Test**.

The SPSS customer satisfaction data file SPSS_CUST.SAV. This data file was collected from a random sample of SPSS customers asking about their satisfaction with the software, service, and other features, and some background information on the customer and their company.

1. One variable in the customer survey asked about agreement that SPSS products are a good value (*gdvalue*). A second question asked about agreement that SPSS offers high quality products (*hiqualty*). Use a paired-samples t test to see whether the means of these two questions differ (they are measured on a five-point scale). What do you conclude?

2. Then test whether there is a mean difference between agreement that SPSS products are easy to learn (*easylrn*) and SPSS products are easy to use (*easyuse*). What do you conclude?

3. Could we use a paired-sample t test to compare how long a customer has used SPSS products (*usespss*) and how frequently they use SPSS (*freqspss*)? Why or why not?

**9.20 Learning Activity**

The overall goal of this learning activity is to use One-Way ANOVA with post hoc tests to explore the relationship between several variables. You will use the PASW Statistics data file *Census.sav*.

1. Investigate how the number of siblings (*sibs*) varies by highest degree (*degree*). Ask for appropriate statistics.

2. Is the assumption of homogeneity of variance met? Is the ANOVA test significant at the .01 level?

3. Do a post hoc analysis, if justified. Ask for both the Bonferroni and Scheffe tests? What do you conclude from these tests? Which education groups have different mean numbers of children? Are the Bonferroni and Scheffe tests consistent?

4. Create an error bar chart to display the mean differences for *sibs *by *degree*. Is the error bar chart a correct representation of which means are different?

5. Now do another analysis of political position (*polviews*) by *degree*. Repeat the same

**7.14 Learning Activity**

In these activities you will use the file *Census.sav*. The overall goal is to run the **Independent-Samples T Test**, to interpret the output and visualize the results with an error bar chart.

1. We want to see whether men and women differ in their mean socioeconomic index (*sei*) and their age when their first child was born (*agekdbrn*). First, use the Explore procedure to view the distributions of these two variables by gender. Are they similar or different? Do you see any problems with doing a t test?

2. Now do a t test for each variable, by gender. Is the homogeneity of variance assumption met, or not? What do you conclude about mean differences by gender?

3. Create an error bar chart for each variable by gender. Is the graph consistent with the result from the t test?

4. Now do the same analysis with the variable *race*, testing whether there are differences in *sei *and *agekdbrn *comparing whites to blacks. Although *race *has three categories, you can use only two categories in the t test. As before, first use the Explore procedure to view the distributions of these two variables by race? Are they similar or different? Do you see any problems with doing a t test?

5. Now do a t test for each variable, by white versus black. Is the homogeneity of variance assumption met, or not? What do you conclude about mean differences between whites and blacks?

6. Create an error bar chart for each variable by race. Is the graph consistent with the result from the t test?

7. *For those with more timed: *How could you display an error bar chart with only the categories of white and black, not other? There are at least two methods.

**8.10 Learning Activity**

The overall goal of this learning activity is to use the **Paired-Samples T Test**.

The SPSS customer satisfaction data file SPSS_CUST.SAV. This data file was collected from a random sample of SPSS customers asking about their satisfaction with the software, service, and other features, and some background information on the customer and their company.

1. One variable in the customer survey asked about agreement that SPSS products are a good value (*gdvalue*). A second question asked about agreement that SPSS offers high quality products (*hiqualty*). Use a paired-samples t test to see whether the means of these two questions differ (they are measured on a five-point scale). What do you conclude?

2. Then test whether there is a mean difference between agreement that SPSS products are easy to learn (*easylrn*) and SPSS products are easy to use (*easyuse*). What do you conclude?

3. Could we use a paired-sample t test to compare how long a customer has used SPSS products (*usespss*) and how frequently they use SPSS (*freqspss*)? Why or why not?

**9.20 Learning Activity**

The overall goal of this learning activity is to use One-Way ANOVA with post hoc tests to explore the relationship between several variables. You will use the PASW Statistics data file *Census.sav*.

1. Investigate how the number of siblings (*sibs*) varies by highest degree (*degree*). Ask for appropriate statistics.

2. Is the assumption of homogeneity of variance met? Is the ANOVA test significant at the .01 level?

3. Do a post hoc analysis, if justified. Ask for both the Bonferroni and Scheffe tests? What do you conclude from these tests? Which education groups have different mean numbers of children? Are the Bonferroni and Scheffe tests consistent?

4. Create an error bar chart to display the mean differences for *sibs *by *degree*. Is the error bar chart a correct representation of which means are different?

5. Now do another analysis of political position (*polviews*) by *degree*. Repeat the same

**7.14 Learning Activity**

**7.14 Learning Activity**

**7.14**7.14

*Learning Activity**Learning Activity*

*Learning Activity*

*Census.sav*. The overall goal is to run the **Independent-Samples T Test**, to interpret the output and visualize the results with an error bar chart.

In these activities you will use the file *Census.sav*. The overall goal is to run the **Independent-Samples T Test**, to interpret the output and visualize the results with an error bar chart.*Census.sav***Independent-Samples T Test**

*sei*) and their age when their first child was born (*agekdbrn*). First, use the Explore procedure to view the distributions of these two variables by gender. Are they similar or different? Do you see any problems with doing a t test?

1. We want to see whether men and women differ in their mean socioeconomic index (*sei*) and their age when their first child was born (*agekdbrn*). First, use the Explore procedure to view the distributions of these two variables by gender. Are they similar or different? Do you see any problems with doing a t test?*sei**agekdbrn*

2. Now do a t test for each variable, by gender. Is the homogeneity of variance assumption met, or not? What do you conclude about mean differences by gender?2. Now do a t test for each variable, by gender. Is the homogeneity of variance assumption met, or not? What do you conclude about mean differences by gender?

3. Create an error bar chart for each variable by gender. Is the graph consistent with the result from the t test?3. Create an error bar chart for each variable by gender. Is the graph consistent with the result from the t test?

*race*, testing whether there are differences in *sei *and *agekdbrn *comparing whites to blacks. Although *race *has three categories, you can use only two categories in the t test. As before, first use the Explore procedure to view the distributions of these two variables by race? Are they similar or different? Do you see any problems with doing a t test?

4. Now do the same analysis with the variable *race*, testing whether there are differences in *sei *and *agekdbrn *comparing whites to blacks. Although *race *has three categories, you can use only two categories in the t test. As before, first use the Explore procedure to view the distributions of these two variables by race? Are they similar or different? Do you see any problems with doing a t test?4. Now do the same analysis with the variable *race*, testing whether there are differences in *sei *and *agekdbrn *comparing whites to blacks. Although *race *has three categories, you can use only two categories in the t test. As before, first use the Explore procedure to view the distributions of these two variables by race? Are they similar or different? Do you see any problems with doing a t test?*race**sei **agekdbrn **race *

6. Create an error bar chart for each variable by race. Is the graph consistent with the result from the t test?6. Create an error bar chart for each variable by race. Is the graph consistent with the result from the t test?

*For those with more timed: *How could you display an error bar chart with only the categories of white and black, not other? There are at least two methods.

7. *For those with more timed: *How could you display an error bar chart with only the categories of white and black, not other? There are at least two methods.7. *For those with more timed: *How could you display an error bar chart with only the categories of white and black, not other? There are at least two methods.*For those with more timed: *

**8.10 Learning Activity**

**8.10 Learning Activity**

**8.10**8.10

*Learning Activity**Learning Activity*

*Learning Activity*

The overall goal of this learning activity is to use the **Paired-Samples T Test**.

The overall goal of this learning activity is to use the **Paired-Samples T Test**.**Paired-Samples T Test**

The SPSS customer satisfaction data file SPSS_CUST.SAV. This data file was collected from a random sample of SPSS customers asking about their satisfaction with the software, service, and other features, and some background information on the customer and their company.The SPSS customer satisfaction data file SPSS_CUST.SAV. This data file was collected from a random sample of SPSS customers asking about their satisfaction with the software, service, and other features, and some background information on the customer and their company.

*gdvalue*). A second question asked about agreement that SPSS offers high quality products (*hiqualty*). Use a paired-samples t test to see whether the means of these two questions differ (they are measured on a five-point scale). What do you conclude?

1. One variable in the customer survey asked about agreement that SPSS products are a good value (*gdvalue*). A second question asked about agreement that SPSS offers high quality products (*hiqualty*). Use a paired-samples t test to see whether the means of these two questions differ (they are measured on a five-point scale). What do you conclude?1. One variable in the customer survey asked about agreement that SPSS products are a good value (*gdvalue*). A second question asked about agreement that SPSS offers high quality products (*hiqualty*). Use a paired-samples t test to see whether the means of these two questions differ (they are measured on a five-point scale). What do you conclude?*gdvalue**hiqualty*

*easylrn*) and SPSS products are easy to use (*easyuse*). What do you conclude?

2. Then test whether there is a mean difference between agreement that SPSS products are easy to learn (*easylrn*) and SPSS products are easy to use (*easyuse*). What do you conclude?2. Then test whether there is a mean difference between agreement that SPSS products are easy to learn (*easylrn*) and SPSS products are easy to use (*easyuse*). What do you conclude?*easylrn**easyuse*

*usespss*) and how frequently they use SPSS (*freqspss*)? Why or why not?

3. Could we use a paired-sample t test to compare how long a customer has used SPSS products (*usespss*) and how frequently they use SPSS (*freqspss*)? Why or why not?3. Could we use a paired-sample t test to compare how long a customer has used SPSS products (*usespss*) and how frequently they use SPSS (*freqspss*)? Why or why not?*usespss**freqspss*

**9.20 Learning Activity**

**9.20 Learning Activity**

**9.20**9.20

*Learning Activity**Learning Activity*

*Learning Activity*

*Census.sav*.

The overall goal of this learning activity is to use One-Way ANOVA with post hoc tests to explore the relationship between several variables. You will use the PASW Statistics data file *Census.sav*.*Census.sav*

The file Census.sav, a PASW Statistics data file from a survey done on the general adult population. Questions were included about various attitudes and demographic characteristics.The file Census.sav, a PASW Statistics data file from a survey done on the general adult population. Questions were included about various attitudes and demographic characteristics.

*sibs*) varies by highest degree (*degree*). Ask for appropriate statistics.

1. Investigate how the number of siblings (*sibs*) varies by highest degree (*degree*). Ask for appropriate statistics.*sibs**degree*

2. Is the assumption of homogeneity of variance met? Is the ANOVA test significant at the .01 level?

2. Is the assumption of homogeneity of variance met? Is the ANOVA test significant at the .01 level? 2. Is the assumption of homogeneity of variance met? Is the ANOVA test significant at the .01 level?

*sibs *by *degree*. Is the error bar chart a correct representation of which means are different?

4. Create an error bar chart to display the mean differences for *sibs *by *degree*. Is the error bar chart a correct representation of which means are different?4. Create an error bar chart to display the mean differences for *sibs *by *degree*. Is the error bar chart a correct representation of which means are different?*sibs **degree*

5. Now do another analysis of political position (*polviews*) by *degree*. Repeat the same

5. Now do another analysis of political position (*polviews*) by *degree*. Repeat the same5. Now do another analysis of political position (*polviews*) by *degree*. Repeat the same*polviews**degree*