In this assignment, you will learn to enter data in SPSS and to conduct simple correlational statistical analyses. Problem #1. A health researcher wants to know how Body-mass index (BMI) correlates with exercise. She collected the data on these two variables (BMI and average minutes of exercise per day) from 25 adult females. Enter that data below into SPSS naming the variables 'Exercise' and 'BMI'. Make sure to define them as numeric and continuous (scale) variables. (4 points)

BMI Daily Exercise ( average in minutes)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25	17 17.4 18.2 18.5 18.7 18.9 19.4 19.8 20 20.1 20.6 20.8 21 21.2 21.4 21.7 22.5 23 23.6 25.7 26.7 28.8 29 32.3 36.7	44.5 43.4 41.7 40.5 42.2 39.9 27.6 38.9 31.4 38.2 16.4 36.6 34.5 35.4 44 33.7 30.9 31 17 25.1 21 17.7 15 12 6

Research Question: How does exercise correlate with BMI in otherwise healthy adult female? In other words, how well can we predict BMI using exercise in healthy adult female? (i.e., Is there a statistically significant relationship between BMI and exercise in adult female?) Null hypothesis: Research/Alternative hypothesis: Variable 1 and level of measurement: Variable 2 and level of measurement: Statistical analysis used: Key statistics and interpretation: Correlation coefficient: Statistical significance: Coefficient of determination/Effect size/Shared Variance: Assumptions: 1. Significant outliers: 2. Linearity: 3. Normality: Reject or Fail to Reject the Null: Interpretation/Conclusion:

Data on the same two variables for men and for teenagers between 15-18 ages resulted in: Men: r = -.78 p = .000 (n=28) Shared variance = .61 (61% variance in BMI is explained/predicted by exercise) Teenagers between 15-18: r = .43 p = .03 (n=53) Shared variance = .1849 (18.5% variance in BMI is explained/predicted by exercise) Of the three groups (women, men and teenagers between ages 15-18), for which group is the variable exercise better predictor of BMI (or vice versa)? Based on what information are you concluding thusly?

Problem #2. Two sportswriters ranked 10 pro football teams for best team between 2010-2015. The teams and rankings by the two writers respectively are as follows. Enter this data into SPSS, name the variables "Writers1ranking" and "Writers2ranking", set them as numeric and ordinal variables. (4 points)

	Teams	Writer 1 rank	Writer 2 rank
1	49ers	1	4
2	Broncos	2	3
3	Cowboys	3	1
4	Packers	4	2
5	Raiders	5	7
6	Colts	6	5
7	Dolphins	7	10
8	Titans	8	6
9	Jaguars	9	8
10	Bills	10	9

Research Question: How much do these two sportswriters agree with each other when it comes these 10 football teams? In other words, how consistent or inconsistent are these rankings with each other? (Is there a statistically significant relationship between the two writers' rankings of the football teams?) Null hypothesis: Research/Alternative hypothesis: Variable 1/ Level of measurement: Variable 2/ Level of measurement: Statistical analysis used: Key statistics and interpretation: Correlation coefficient: Statistical significance: Effect size/Shared Variance: Reject or Fail to Reject the Null: Interpretation/Conclusion: (Data are ordinal, thus using non-parametric correlation test - No assumptions need to be checked.)