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(solution) My question did not get answered yesterday so I am raising the


My question did not get answered yesterday so I am raising the price.  This is a VERY fare price and I do have a tight turn time now.  Please do QUICK but Quality work.  Please answer the questions within the Word doc and send it back.  The attached Word doc must be used.  Note: in the Word doc are pictures of charts needed to answer the questions. 

If you can show your work when it is possible/necessary that would be amazing!  

Please answer questions in the "a. b. c...." format.

Note there are 8 questions

12.20 For the following data, assuming a between-groups design, determine:

Group 1: 11, 17, 22, 15

Group 2: 21, 15, 16

Group 3: 7, 8, 3, 10, 6, 4

Group 4: 13, 6, 17, 27, 20

a. df between

b. df within

c. df total

d. The critical value, assuming a p value of 0.05

e. The mean for each group and the grand mean

f. The total sum of squares

g. The within-groups sum of squares

h. The between-groups sum of squares

i. The rest of the ANOVA source table for these data

j. Tukey HSD values


12.24 An incomplete one-way between-groups ANOVA source table is shown below. Compute the missing values.

 

12.40 Consideration of Future Consequences and two kinds of hypothesis testing: Two samples of students, one comprised of social science majors and one comprised of students with other majors, completed the Consideration of Future Consequences scale (CFC). The accompanying tables include the output from soft-ware for an independent-samples t test and a one-way between-groups ANOVA on these data.

a. Demonstrate that the results of the independent-samples t test and the one-way between-groups ANOVA are the same. (Hint: Find the t statistic for the t test and the F statistic for the ANOVA.)

b. In statistical software output, ?Sig.? refers to the actual p level of the statistic. We can compare the actual p level to a cutoff p level such as 0.05 to decide whether to reject the null hypothesis. What are the ?Sig.? levels for the two tests here? the independent-samples t test and the one-way between-groups ANOVA? Are they the same or different? Explain why this is the case.

c. In the CFC ANOVA, the column titled ?Mean Square? includes the estimates of variance. Show how the F statistic was calculated from two types of variance. (Hint: Look at the far-left column to determine which estimate of variance is which.)

d. Looking at the table titled ?Group Statistics,? how many participants were in each sample?

e. Looking at the table titled ?Group Statistics,? what is the mean CFC score for the social science majors?


15.18 Decide which of the three correlation coef?cient values below goes with each of the scatterplots presented in Exercise 15.17 (scatter plots above).

a. 0.545

b. 0.018

c. ? 0.20


15.20 For each of the pairs of correlation coefficients provided, determine which one indicates a stronger relation between variables:

a. ? 0.28 and ? 0.31

b. 0.79 and 0.61

c. 1.0 and ? 1.0

d. ? 0.15 and 0.13


15.34 Externalizing behavior, anxiety, and correlation: A study on the relation between rejection and depression in adolescents (Nolan, Flynn, & Garber, 2003) also collected data on externalizing behaviors (e.g., acting out in negative ways, such as causing ?ghts) and anxiety. They wondered whether externalizing behaviors were related to feelings of anxiety. Some of the data are presented in the accompanying table.

a. Create a scatterplot of these data. Be sure to label both axes.

b. What does the scatterplot suggest about the relation between these two variables?

c. Would it be appropriate to calculate a Pearson correlation coef?cient? Explain your answer.

d. Construct a second scatterplot, but this time add a participant who scored 1 on externalizing behaviors and 45 on anxiety. Would you expect the correlation coef?cient to be positive or negative now? Small in magnitude or large in magnitude?

e. The Pearson correlation coef?cient for the ?rst set of data is 0.65; for the second set of data it is 0.12. Explain why the correlation changed so much with the addition of just one participant.


15.36 Direction of a correlation: For each of the following pairs of variables, would you expect a positive correlation or a negative correlation between the two variables? Explain your answer.

 

a. How hard the rain is falling and your commuting time

b. How often you say no to dessert and your body fat

c. The amount of wine you consume with dinner and your alertness after dinner


15.56 Mental health and partial correlation: A study by Nolan and colleagues (2003) examined the relation be-tween externalizing behaviors (acting out) and anxiety in adolescents. Depression has been shown to relate to both of these variables. What role might depression play in the observed positive relation between these variables? The correlation matrix below displays the Pearson correlation coef?cients, as calculated by computer software, for each pair of the variables of interest: depression, externalizing, and anxiety. The Pearson correlation coef?cients for each pair of variables are at the intersection in the chart of the two variables. For example, the correlation coef?cient for the association between depression (top row) and externalizing (second column of correlations) is 0.635, a very strong positive correlation.  

a. Given that the authors calculated correlation coef?cients, what kind of variables are depression, anxiety, and externalizing? Explain your answer.

b. What is the correlation coef?cient for the association between depression and anxiety? Explain what this correlation coef?cient tells us about the relation between these variables.

c. What is the correlation coef?cient for the association between anxiety and externalizing? Explain what this correlation coef?cient tells us about the relation between these variables.

d. The partial correlation of anxiety and externalizing is 0.17, controlling for the variable of depression. How is this different from the original Pearson correlation coef?cient between these two variables?

e. Why is the partial correlation coef?cient different from the original Pearson correlation coef?cient between these two variables? What did we learn by calculating a partial correlation?

f. Why can we not draw causal conclusions with respect to these findings?


12.20 For the following data, assuming a between-groups design, determine:

 

Group 1: 11, 17, 22, 15

 

Group 2: 21, 15, 16

 

Group 3: 7, 8, 3, 10, 6, 4

 

Group 4: 13, 6, 17, 27, 20

 

a. df between

 

b. df within

 

c. df total

 

d. The critical value, assuming a p value of 0.05

 

e. The mean for each group and the grand mean

 

f. The total sum of squares

 

g. The within-groups sum of squares

 

h. The between-groups sum of squares

 

i. The rest of the ANOVA source table for these data

 

j. Tukey HSD values

 

12.24 An incomplete one-way between-groups ANOVA source table is shown below. Compute the

 

missing values. 12.40 Consideration of Future Consequences and two kinds of hypothesis testing: Two samples of

 

students, one comprised of social science majors and one comprised of students with other majors,

 

completed the Consideration of Future Consequences scale (CFC). The accompanying tables include the

 

output from soft-ware for an independent-samples t test and a one-way between-groups ANOVA on

 

these data. a. Demonstrate that the results of the independent-samples t test and the one-way between-groups

 

ANOVA are the same. (Hint: Find the t statistic for the t test and the F statistic for the ANOVA.)

 

b. In statistical software output, ?Sig.? refers to the actual p level of the statistic. We can compare the

 

actual p level to a cutoff p level such as 0.05 to decide whether to reject the null hypothesis. What are

 

the ?Sig.? levels for the two tests here? the independent-samples t test and the one-way betweengroups ANOVA? Are they the same or different? Explain why this is the case.

 

c. In the CFC ANOVA, the column titled ?Mean Square? includes the estimates of variance. Show how the

 

F statistic was calculated from two types of variance. (Hint: Look at the far-left column to determine

 

which estimate of variance is which.)

 

d. Looking at the table titled ?Group Statistics,? how many participants were in each sample?

 

e. Looking at the table titled ?Group Statistics,? what is the mean CFC score for the social science majors? 15.18 Decide which of the three correlation coef?cient values below goes with each of the scatterplots

 

presented in Exercise 15.17 (scatter plots above).

 

a. 0.545

 

b. 0.018

 

c. ? 0.20

 

15.20 For each of the pairs of correlation coefficients provided, determine which one indicates a stronger

 

relation between variables:

 

a. ? 0.28 and ? 0.31

 

b. 0.79 and 0.61

 

c. 1.0 and ? 1.0

 

d. ? 0.15 and 0.13

 

15.34 Externalizing behavior, anxiety, and correlation: A study on the relation between rejection and

 

depression in adolescents (Nolan, Flynn, & Garber, 2003) also collected data on externalizing behaviors

 

(e.g., acting out in negative ways, such as causing ?ghts) and anxiety. They wondered whether

 

externalizing behaviors were related to feelings of anxiety. Some of the data are presented in the

 

accompanying table. a. Create a scatterplot of these data. Be sure to label both axes.

 

b. What does the scatterplot suggest about the relation between these two variables?

 

c. Would it be appropriate to calculate a Pearson correlation coef?cient? Explain your answer.

 

d. Construct a second scatterplot, but this time add a participant who scored 1 on externalizing

 

behaviors and 45 on anxiety. Would you expect the correlation coef?cient to be positive or negative

 

now? Small in magnitude or large in magnitude?

 

e. The Pearson correlation coef?cient for the ?rst set of data is 0.65; for the second set of data it is 0.12.

 

Explain why the correlation changed so much with the addition of just one participant.

 

15.36 Direction of a correlation: For each of the following pairs of variables, would you expect a positive

 

correlation or a negative correlation between the two variables? Explain your answer.

 

a. How hard the rain is falling and your commuting time

 

b. How often you say no to dessert and your body fat

 

c. The amount of wine you consume with dinner and your alertness after dinner 15.56 Mental health and partial correlation: A study by Nolan and colleagues (2003) examined the

 

relation be-tween externalizing behaviors (acting out) and anxiety in adolescents. Depression has been

 

shown to relate to both of these variables. What role might depression play in the observed positive

 

relation between these variables? The correlation matrix below displays the Pearson correlation

 

coef?cients, as calculated by computer software, for each pair of the variables of interest: depression,

 

externalizing, and anxiety. The Pearson correlation coef?cients for each pair of variables are at the

 

intersection in the chart of the two variables. For example, the correlation coef?cient for the association

 

between depression (top row) and externalizing (second column of correlations) is 0.635, a very strong

 

positive correlation. a. Given that the authors calculated correlation coef?cients, what kind of variables are depression,

 

anxiety, and externalizing? Explain your answer.

 

b. What is the correlation coef?cient for the association between depression and anxiety? Explain what

 

this correlation coef?cient tells us about the relation between these variables.

 

c. What is the correlation coef?cient for the association between anxiety and externalizing? Explain what

 

this correlation coef?cient tells us about the relation between these variables.

 

d. The partial correlation of anxiety and externalizing is 0.17, controlling for the variable of depression.

 

How is this different from the original Pearson correlation coef?cient between these two variables?

 

e. Why is the partial correlation coef?cient different from the original Pearson correlation coef?cient

 

between these two variables? What did we learn by calculating a partial correlation?

 

f. Why can we not draw causal conclusions with respect to these ?ndings?

 


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