1. Group participation McKie Delahunty- created slides 1,4,5,6,7,8,11,12 & compiled all for powerpoint Jenica Hansen- created slides 2,3 Semhar Moges- created zero slides/ out of town/ I made her slides from her written report Devan Turner- created slides 9,10 Kelsey Kurth- created slides 13, 14

2. Group 2 Presentation McKie Delahunty Jenica Hansen Semhar Moges Devan Turner Kelsey Kurth Sleep, school, or both? For SLCC students is the number of hours worked in a week related to the number of credit hours taken?”

3. Our Purpose: With the demands of school work in classes, rising tuition, and many of the other expenses college students face, we decided to see if : For SLCC students, is the number of hours worked in a week related to the number of credit hours taken during the semester?

4.  Using a systematic sampling method of every fourth person, SLCC students will be surveyed about how many hours they work per week, and how many credit hours they’re taking.  Sample Size: 125 SLCC Students - 5 group members each survey 25 individuals  Survey with two questions: - How many hours do you work per week? - How many credits are you taking? Our Plan:

5. Data Table on following two slides

6. Hours per week Credits taken Hours per week Credits taken 248323 15121512 208405 015169 145015 183247 189169 3051210 8012 0 369 328204 0605 107207 81409 1210163 Hours per week Credits taken Hours per week Credits taken 18605 227128 05308 1541811 36164 201 12 0884 165156 072310 8122812 15384 03136 208178 3222810 18583

7. Hours per week Credits taken Hours per week Credits taken 941516 177016 8015 28121314 2410406 941517 1042015 128015 288614 16102212 29121015 14 2514 182516 5064013 0153016 Hours per week Credits taken Hours per week Credits taken 0203317 1012356 023436 35121721 19152716 15 6 4112319 499409 28133112 5662812 14212816 28121716 3014239 2915 399

8. 1 st Variable Statistics, Histogram, and Box Plot  Hours Worked in One Week  Mean: 18.853  Standard Deviation: 12.7453  Five-Number Summary: - 17.1875, 10, 17, 28.125, 55.3125  Range: 56  Mode: 0  Outliers: 56

9. 2 nd Variable statistics, Histogram, and Box Plot  Stats summary for Credit Hours taken in a semester  Mean: 9  Standard Deviation: 4.7  Five-Number Summary: -4.5, 6, 9, 13, 23.5  Range: 23  Mode: 12  Outliers: none

10. Linear Correlation  Correlation between Hours Per Week and Credits Taken is: - 0.1159299099 Regression Equation Results Sample Size: 125 Dependent Variable: Credits TakenIndependent Variable: Hours Per Week  Credits Taken= 10.575729 – 0.042873103 Hours Per Week  a= -.0428731023b= 10.57572925r= -.1159299099 r-sq=.013439744  R (correlation coefficient) = -0.1159 or (-.1159299099) SALT LAKE COMMUNITY COLLEGE STUDENTS Students Hours Worked Related to Credits Taken ( Data below obtained from 125 students)

11. Scatter Plot with Line of Regression

12.  Fairly minimal  Deciding a research topic.  formulating a research plan with appropriate variables.  difficult for our group to get together and coordinating through email. Everyone had a busy summer and a couple lived far away.  Creating Boxplots in excel! Difficulties and Suprises

13.  The distribution was created a frequency plot out of the hours worked in one week by our sample population.  The histogram and boxplot created from our first variable are skewed to the right. ^This means that our median lies in the left of our data plot and there lies towards the right a few variables with higher values. We have one outlier at 56 hours worked in one week.  Out of our sample we found that students tend to work between 15 and 25 hours a week.  The scatter plot shows there is zero correlation between the data. Our  Our data has a weak linear correlation coefficient. This proves no correlation between the two data sets. Coefficient=0.11574 Analysis

14.  By observing the data (calculations, graphs and plots) it was found that there is not a correlation between the two sets of data collected. We found that r(125)= 0.12, p > 0.05 which shows that the correlation coefficient was less than the critical value of the data.  Thus proving that there is no way we can be 95% confident that there is a relationship between the two sets of data. In order for us to be confident there is a relationship between the sets of data there would have needed to be a positive relation in students taking more or less credits at SLCC based on how many hours they are currently working, which there was not. Interpretation

15.  Our research question “For SLCC students is the number of hours worked in a week related to the number of credit hours taken?” was answered.  The predictor variable in this question was number of hours worked and the response variable was number of credit hours taken.  We found that there is not a relationship between the two variables. You can tell this by seeing how scattered our data was. This shows that there are other reasons for explaining how many credit hours SLCC students take. Conclusion