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Math 1040 Group 7 Term Project Mary Jo Bond Laura Weber Emmy Maddalone Beau Hintze

Math 1040 Group 7 Term Project Mary Jo Bond Laura Weber Emmy Maddalone Beau Hintze. Introduction. For SLCC students is the number of credit hours taken a semester related to the number of fast food meals eaten per week?

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Math 1040 Group 7 Term Project Mary Jo Bond Laura Weber Emmy Maddalone Beau Hintze

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  1. Math 1040 Group 7 Term ProjectMary Jo Bond Laura WeberEmmy Maddalone Beau Hintze

  2. Introduction • For SLCC students is the number of credit hours taken a semester related to the number of fast food meals eaten per week? • The purpose of our study is to find out if there is a correlation between the amount of credit hours a student is taking and the number of times the eat fast food per week • It is logical to think that the more time taken up by classes, studying, tests, group projects, and all other school related activities would reduce time given to other areas of a student’s life • College students are already more prone to eating fast food than most other demographics, so maybe the time lost to schoolwork is affecting their dieting habits

  3. Study Design • The study design we used for gathering our data was the stratified method • We first divided the population into male and female strata and then obtained a simple random sample from each stratum (male and female) of about twenty subjects per group member and in total surveyed 55 male and 54 female students • By separating the students we can also see if gender plays a factor into whether or not a student would choose a fast food meal over a homemade meal

  4. Data Table

  5. Fast Food Occurrences Data: Mean: 2.97248 Standard Deviation: 2.32323 Five Number Summary: Min: 1, Q1: 1, Q2 (Median): 3, Q3: 4, Max: 12 Range: 12 Mode: 2 Outliers: None

  6. Histogram for First Quantitative Variable

  7. Box Plot for First Quantitative Variable

  8. Credit Hours Data: Mean: 8.00917 Standard Deviation: 3.35409 Five Number Summary: Min: 1, Q1:6 , Q2 (Median):8 , Q3:11 , Max: 18 Range: 17 Mode: 6 Outliers: None

  9. Histogram for Second Quantitative Variable

  10. Box Plot for Second Quantitative Variable

  11. Correlation Data: Correlation Coefficient R= 0.9209310270943356 Rounded: R= 0.9209 Equation for Line of Regression: y = 1.3296x + 4.0571

  12. Scatter plot for Line of Regression

  13. Difficulties & Surprises Encountered: The first challenge I encountered, was that many students on campus were either in a rush, or did not want to stop and answer questions. For those who did stop, some of them seemed embarrassed about their number of fast food visits and this led me to wonder if some respondents were modifying the numbers to “sound better”, creating response bias. Secondly, as a group, we were to each survey 20 people at random, but some group members surveyed more, leaving us with 109 subjects. This presented a challenge when using the table at www.gifted.uconn.edu. As per the directions, since my specific degree of freedom was not on the correlation table, I took the next, lowest available “df” which was 100. I was somewhat surprised to see such a strong correlation. I did expect for there to be the possibility of a relationship between the two variables, because common sense dictates that those who are bogged down with classes, might be more likely to take advantage of the convenience of fast food.

  14. Analysis: R(100) = .9209, p > .05 The above information indicates that there is a statistically significant relationship between the number of fast food occurrences per week, and the number of credit hours taken per semester by SLCC students. The distribution of data shows a well-plotted line that also reflects this correlation, and the critical value of 0.195 being less than the correlation coefficient of .9209 indicates a strong relationship as well.

  15. Interpretations and Conclusions It is safe to say that a SLCC student could be selected at random, and one could expect for there to be a strong correlation between the number of fast food visits they make weekly, and the number of credit hours that they are enrolled in. Upon examining the data, constructing histograms, and box plots, I could see that a relationship between the two quantitative variables was likely. This was further confirmed when seeing the scatter plot and the line of regression. But I was only I able to confidently conclude that there was in fact a relationship when I established the critical value and did the comparison with the correlation coefficient.

  16. Credits: • Mary Jo Bond - Slides 1-4; Group Leader/Submission • Laura Weber - Slides 5-7 • Emmy Maddalone – Slides 8-12; Compilation of PowerPoint Presentation. • Beau Hintze – Slides 13-15

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