Credit Hours vs. Sleeping Hours (Group Research Powerpoint )

1. Credit Hours vs. Sleeping Hours(Group Research Powerpoint) Purpose : Does the amount of credit hours taken by a student each semester directly effect the amount of sleep they get per week.

2. Purpose of the study • Does the amount of credit hours taken by a student each semester directly effect the amount of sleep they get per week?

3. Study Design Focus of Study: Students Variables: Number of credit hours Hours of sleep each week. Method of Collection: 220 Students surveyed in person at random collected over a 2 week period.

4. Study Design • Credit hours vs. sleeping hours, was conducted by five students attending Math 1060 at Salt Lake Community College. This study was focused on students, comparing the amount of college credit hours they take each semester to the amount of sleep they get each week. Each collector was assigned to ask 44 student (totaling 220) the amount of credit hours they were taking and how many hours of sleep, on average, they were receiving per week. Data was collected over a two-week period that involved students across the United States.

5. First Quantitative Variable Number of credit hours • Mean: 11.73 (12 hours 13 minutes) • Standard Deviation: 10.65 • Five-number summary: 1,9,12,15,20 • Range: 19 • Mode: 12 • Outliers (0, 24): None

6. Second Quantitative Variable Hours of sleep • Mean: 6.84 (7 hours 24 minutes) • Standard Deviation: 25.43 • Five-Number summary: 3,5,8,8,12 • Range : 9 • Mode: 7 • Outliers (.5 or 12.5): None

7. Difficulties/Surprises • correlation coefficient: = -.30. • Shows that the correlation between variable 1 (credit hours) and variable 2 (sleep hours) were not closely related.

8. Analysis • As I stated above the R-value (correlation coefficient) was -.30. The value of r must be that -1 <r< +1. A positive correlation indicates a relationship between x and y variables in that when x increases, values for y will also increase. In a negative correlation a -1 shows a perfect negative fit. This indicates the relationship between x and y shows that as the x variable increases, the y variable will decrease. Our finding for r was -.30, this shows that a lack of correlation took place or a nonlinear relationship exists between the two variables. • Our sample size was 220 students that ranged from one credit class to twenty credits per semester. Two hundred and twenty students is a good amount to survey, however since our data is constricted to a small amount it effected our correlation.

9. Interpretation and Conclusions • Is the amount of credit hours that a student takes per semester directly correlated to the amount of sleep they get per week? According to the data, at a -.3, the correlation between the two is nonexistent. Out of the 220 students surveyed, we found that these two variables have very little to do with the other.

10. Simple linear regression results • Dependent Variable: Credit Hours (Var 2) • Independent Variable: Sleep Hours Var 1) • var1 = 8.03176 - 0.10193302 • var2 Sample size: 220 • R (correlation coefficient) = -0.2998 • R-sq = 0.08986513 • Estimate of error standard deviation: 1.2497518

11. Parameter Estimates • Slope Intercept = -0.10193302x + 8.03176

12. Analysis of variance table for regression model:

14. Credit Hours (per semester)

15. Sleep Hours