Math 1040 Semester Project

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Math 1040 Semester Project. ~ Group 6 ~ Kimberly Jorgensen, Daniel Lollathin and Whitney Woodruff. Research Question. Is there any relation between the number of hours of sleep an individual gets and the amount of caffeinated beverages consumed daily?. Data Collection. - PowerPoint PPT Presentation

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Math 1040 Semester Project~ Group 6 ~Kimberly Jorgensen, Daniel Lollathin and Whitney Woodruff

Research Question•Is there any relation between the number

of hours of sleep an individual gets and the amount of caffeinated beverages consumed daily?

Data Collection

•We decided to take a systematic sampling approach and sample between ten and twenty individuals each, resulting in a sufficient sample size of over 30 people.

Variable 1 Data – Hours of sleep

Variable 1 Statistics– Hours of sleep

• Mean: 6.78• Standard deviation: 1.36975• Five-number summary

▫MIN: 2▫Q1: 6▫Median: 7▫Q3: 8▫MAX: 10

• Range: 8• Mode: 7• Outliers: 2

Variable 2 Data – Caffeinated drinks

Variable 1 Statistics– Caffeinated drinks

• Mean: 1.5• Standard deviation: 1.6507• Five-number summary

▫MIN: 0▫Q1:0▫Median: 1▫Q3: 2▫MAX: 7.5

• Range: 7.5• Mode: 0• Outliers: 5, 5, 5, 6, 6, 7.5

Calculations

Correlation coefficient: -.2956

Line of regression: Y=-.2466x+7.1499

What does this mean?

•The critical value for the line of regression in this case is near .205. With a result closer to .3 it would appear that there is a very weak relationship between our variables, but it is still statistically significant.

Difficulties and Surprises Encountered

•We felt like one difficulty was the determination on the amount of caffeine consumed. Different types of drinks hold varying amounts of caffeine.

•It is difficult to compare the amounts of caffeine per drink

Difficulties and Surprises Encountered (cont.)•Another difficulty in this type of study is

the awareness and honesty of the individual being sampled. Being unaware how much sleep vs. caffeine consumed makes this study difficult.

•We were also surprised that there was not a stronger correlation between the two variables.

•Kimberly – Data collection, PowerPoint assembly, Results, (Slides 1,4,7,10,11,12)

•Daniel – Data collection, Summary, Conclusion, Difficulties/Surprises, (Slides 2,3,13,14)

•Whitney – Data collection, Graphs, (Slides 5,6,8,9)

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