Here is the data:

WalkingGroup	Mood	Computers	Energy
1	2	13.47	5.6
1	2.04	19.67	4.7
1	2.83	11.59	6
1	3.07	16.9	3.07
1	3.19	14.32	6.19
1	3.2	12.64	6.32
1	3.21	16.68	4.21
1	3.22	23.82	4.56
1	3.25	17.87	6.7
1	3.27	19.41	6.27
1	3.37	18.16	6.3
1	3.4	21.97	6.4
1	3.52	22.98	6.57
1	3.57	17.3	5.57
1	3.58	21.3	5.58
1	3.65	20.52	5.65
1	3.7	17.34	7
1	3.72	21.82	6.7
1	3.75	23.7	6.8
1	3.8	15.41	6.8
1	3.8	22.58	5.98
1	3.84	16.15	5.9
1	3.88	16.95	5.8
1	3.93	18.51	5.9
1	3.95	21.76	5
1	3.97	18.8	4.97
1	3.99	23.71	4.77
1	4	17.18	4
1	4	16.92	4
1	4	13.21	4
1	4	17.2	6
1	4	19.69	6
1	4	21.29	7
1	4.01	17.06	4.01
1	4.11	11.18	4.11
1	4.11	16.92	4.11
1	4.12	12.09	4.12
1	4.18	20.25	4.18
1	4.19	17.89	4.19
1	4.21	12.11	5.67
1	4.31	20.18	4.31
1	4.32	17.58	4.32
1	4.33	18.25	4.33
1	4.34	23.99	6.34
1	4.41	19.31	6.5
1	4.53	15.31	6.53
1	4.53	12.37	7
1	4.54	20.91	5.65
1	4.55	21.42	4.55
1	4.68	18.71	4.68
1	4.68	19.14	4.68
1	4.7	20.14	4.7
1	4.79	12.9	4.79
1	4.81	15.84	4.81
1	4.9	19.69	4.9
1	4.95	21.46	4.95
1	5	13.55	5
1	5	10.09	5
1	5	23.59	5.87
1	5	18.74	6.45
1	5	17.81	5
1	5	20.4	5
1	5	15.49	5.87
1	5	14.88	5.98
1	5	17.51	5.74
1	5	19.64	6
1	5	29.46	5
1	2.71	20.31	5.4
1	5	19.82	6
1	3	15.15	6
1	4.47	20.99	6.47
1	2.91	21.32	6.5
1	5	22.08	6.54
1	3.09	13.43	7
1	3	20.85	7
0	4.6	18.3	6.1
0	4.8	17.38	6.1
0	5	21.79	6.6
0	3.69	14.94	5
0	3.4	17.89	5.4
0	4.82	15.8	3.8
0	4.17	18.07	6.2
0	2.59	22.3	5.8
0	3.44	18.5	7
0	3.45	23.84	6.2
0	2.17	25.6	5.1
0	3.4	11.42	5.8
0	4	19.03	4.9
0	4.66	18.75	5.6
0	4.63	16.67	6.7
0	2.33	15.42	6.98
0	4.84	24.63	4.78
0	1.37	22.68	3.06
0	3.53	17.48	3.17
0	1.93	18.55	4.28
0	4.12	16.76	5.29
0	4.47	19.44	5.3
0	4	18.25	7
0	4	20.21	6.2
0	4.4	13.3	5.2
0	4	12.43	4.1
0	5	21.18	4.6
0	4.16	15.08	5.45
0	2.09	20.89	5.65
0	4.05	22.59	6.5
0	2.4	20.36	5.87
0	5	18.5	3.51
0	2.72	23.35	3.53
0	2.64	10.1	3.54
0	3.81	12.84	3.62
0	3.5	26.01	3.69
0	3.17	12.36	3.81
0	4.88	21.54	6.4
0	4.26	12.22	7
0	4.04	20.34	6
0	2.39	12.75	6
0	3.06	15.63	6.5
0	2.59	16.26	6.5
0	5	25.02	6.25
0	3.03	23.66	3.04
0	3.45	22.92	4.05
0	4.46	13.32	4.06
0	3.5	15.91	4.12
0	3.92	18.95	4.16
0	4.57	19.78	4.17
0	1.91	21.39	3.17
0	5	19.48	4.26
0	3.62	18.88	4.35
0	2.42	25.95	4.4
0	5	16.85	4.42
0	2.61	9.45	4.46
0	4.42	19.97	4.46
0	3.54	17.67	5.47
0	4.46	24.86	5.42
0	3.28	18.34	4.57
0	3.3	20.02	4.63
0	4.17	18.49	4.66
0	4.06	16.4	4.8
0	1.67	21.7	4.82
0	3.41	18.88	4.84
0	4.52	22.71	3.88
0	3	15.51	4.88
0	4	17.28	5
0	4.35	17.48	5
0	3.41	21.55	5
0	4.88	16.45	5
0	4	23.99	5
0	3.29	24.92	5
0	3.51	18.66	6.6
0	1.21	21.38	5

Use SPSS to answer the following questions.

This SPSS assignment is based on Activity 7.6 in your textbook. Please start by looking at this activity in your textbook so you understand the research question which relates to the effect of exercise on workers. The data for all 3 of the analyses you will run below is above. Note that in the dataset those labeled "0" were in the not walking group and those labeled "1" were in the walking group.

1. First, we are going to look at the effect of exercise on mood(see data in mood column!).

a) If you want to know whether exercise had any effect on average mood among workers, what type of statistical test should you run and why? (2 pts)

b) Write the statistical null hypothesis in words (talk about exercise and mood) AND then in symbols (i.e., with some type(s) ofm). (2 pts)

c) Now enter the data into SPSS, provide appropriate labels for the variables (e.g., mood/condition), and then run the correct analysis. (3 pts)

d) Highlight the sample means on the output and label them "d". Note, higher scores = more positive mood. (1 pt)

e) Highlight the t-statistic and label it "e". (1 pt)

f) Assuming a two-tailed test, highlight the p valuethat is relevant to this t-statistic and label it "f". Be careful to find the p value linked to the t statistic and not to the Levene's test! (1 pt)

NOTE: if you can't highlight in your screen shot, just include the screen shot and list the means, test statistic, and p value next to the appropriate letters above.

g) Based on this p value, would you say that you have strong evidence against your null hypothesis? Why or why not? (1 pt)

h) Now look at the effect size that SPSS computed. Find the Cohen's d point estimate. Label it with "h". What size effect is this? (1 pt)

i) Now look at the 95% CI around the mean difference that SPSS computed. Highlight the LB and UB with "i" (1 pt)

j) Based on the p value, your effect size, and your CI, what would you conclude about the exercise and mood? (2 pts)

2. Now let's consider the effect of exercise on productivity. See the column labeled "computers" for this data (since they were building computers).

a) What scale of measurement is being used to measure # computers (N, O, I, R) and why is this important to consider? (2 pts)

b) Write the statistical null hypothesis in words (talk about exercise and productivity) AND then in symbols (i.e., with some type(s) ofm). (2 pts)

c) Now enter the data into SPSS, provide appropriate labels for the variables (e.g., productivity/condition), and then run the correct analysis. (3 pts)

d) Highlight the sample means on the output and label them "d". Note, higher scores = greater productivity. (1 pt)

e) Highlight the t-statistic and label it "e". (1 pt)

f) Assuming a two-tailed test, highlight the p valuethat is relevant to this t-statistic and label it "f". Be careful to find the p value linked to the t statistic and not to the Levene's test! (1 pt)

NOTE: if you can't highlight in your screen shot, just include the screen shot and list the means, test statistic, and p value next to the appropriate letters.

g) Based on this p value, would you say that you have strong evidence against your null hypothesis? Why or why not? (1 pt)

h) Now look at the effect size that SPSS computed. Find the Cohen's d point estimate. Label it with "h". What size effect is this? (1 pt)

i) Now look at the 95% CI around the mean difference that SPSS computed. Highlight the LB and UB with "i" (1 pt)

j) Based on the p value, the effect size, and the CI, what would you conclude about the exercise and productivity? (2 pts)

3. Finally, let's look at the effect of exercise on workers' energy. See column labeled "energy".

a) We've been assuming a two-tailed test in all these analyses? Why might a researcher have made this choice? (2 pts)

b) Again, write the statistical null hypothesis in words (talk about exercise and energy) AND then in symbols (i.e., with some type(s) ofm). (2 pts)

c) Now enter the data into SPSS, provide appropriate labels for the variables (e.g., energy/condition), and then run the correct analysis. (3 pts)

d) Highlight the sample means on the output and label them "d". Note, higher scores = more energy. (1 pt)

e) Highlight the t-statistic and label it "e". (1 pt)

f) Assuming a two-tailed test, highlight the p valuethat is relevant to this t-statistic and label it "f". Be careful to find the p value linked to the t statistic and not to the Levene's test! (1 pt)

NOTE: if you can't highlight in your screen shot, just include the screen shot and list the means, test statistic, and p value next to the appropriate letters.

g) Based on this p value, would you say that you have strong evidence against your null hypothesis? Why or why not? (1 pt)

h) Now look at the effect size that SPSS computed. Find the Cohen's d point estimate. Label it with "h". What size effect is this? (1 pt)

i) Now look at the 95% CI around the mean difference that SPSS computed. Highlight the LB and UB with "i" (1 pt)

j) Based on your p value, your effect size, and your CI, what would you conclude about the exercise and energy? (2 pts)

4. (from textbook) You evaluated the effect of walking during lunch hour using three different DVs (mood, productivity, and energy). Based on all the evidence, does it seem like walking during lunch is a good idea or a bad idea? Compose a final report (several sentences!) of the results with your final scientific conclusion. Your conclusion should make a final recommendation to the owners of the factory explaining the potential benefits of walking suggested by the data. (5 pts)