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Coffee Drinking

Coffee Drinking. Rose, Dan, Chris, Megan. Why coffee?. We as students see a difference in the attitudes of teachers depending on if they have their morning or afternoon cup of coffee. T-Scores.

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Coffee Drinking

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  1. Coffee Drinking Rose, Dan, Chris, Megan

  2. Why coffee? • We as students see a difference in the attitudes of teachers depending on if they have their morning or afternoon cup of coffee.

  3. T-Scores • For this case, we didn’t have any other information other then the data we collected. Since we don’t know the population standard deviation, we will use a T-score. • We are trying to estimate the population mean.

  4. Confidence Interval • Confidence intervals help us try to estimate a population parameter. • By taking samples we can create a more accurate confidence interval, which better represents the population.

  5. 90% Confidence Interval • α: .10 • Mean: 7.3 • T-score: 1.753 • S: 7.80 • E: 3.42 • 3.88<µ<10.72 • We can be 90% confident that the true population mean fl.oz of coffee teachers drink is between 3.88fl.oz and 10.73fl.oz

  6. 95% Confidence Interval • α: .05 • Mean: 7.3 • T-score: 2.131 • S: 7.8 • E: 6.15 • 1.15<µ<13.45 • We can be 95% confident that the mean amount of 7.3 fluid ounces of coffee that teachers at Upper Perk drink in the morning is between the confidence interval of 1.15 and 13.45 fluid ounces

  7. 98% Confidence Interval • α: .02 • Mean: 7.3 • T-score: 2.602 • S: 7.8 • E:5.07 • 2.33<µ<12.37 • We can be 98% confident that the mean amount of fluid ounces drank by teachers is between 2.33 and 12.37 fluid ounces

  8. 99% Confidence Interval • α: .01 • Mean: 7.3 • T-score: 2.947 • S: 7.8 • E: 5.75 • 1.55<µ<13.05 • We can be 99% confidence that teachers at UPHS drink between 1.55fl.oz and 13.05fl.oz

  9. Possible Bias • On our survey we didn’t ask if they drink decaf or regular. • Just going through the list of teachers and picking 4 random teachers from each graded homeroom. • Whether they drink their coffee out of a thermos or a mug, we also didn’t take into consideration. • I didn’t see a few of the teachers so Mr. Kunkle picked new ones for me who were free.

  10. Sampling errors • I didn’t see a few of the teachers so Mr. Kunkle picked new ones for me who were free. • For a more random sample I could have placed numbers next to all the teachers names and randomly selected them from the calculator.

  11. Real-World Impact • We all know people like this… • http://www.youtube.com/watch?v=x5h2_eIzoYU

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