FOR NJOSH ONLY

Business 256

James Reisdorf

June 14th, 2017

Introduction:

At the beginning of each day most American’s need an extra boost. Most days we drag ourselves out of bed with one thing on our mind "caffeine". Whether it be a fast-paced lifestyle and constant change in schedule often entices college students to choose caffeinated beverages as a quick source of energy. Our group has set out to prove the hypothesis that—the earlier in which a student's classes begin will influence an increase in caffeine consumption. Nicole L. Olson wrote her Honors Thesis paper on the caffeine consumption of students at the University of New Hampshire, the largest percentage of college students who consumed caffeine had an average of one caffeinated drink per day. We believe that the students at TCC on average consume more than one caffeinated beverage daily. To test this assumption, we conducted a survey of 30 students at TCC, the information and data analyzed is presented as follows.

Part I:

We gathered the data for our survey using a nonrandom convenience sampling method. If we were to complete this process again, we would use a systematic random sampling approach to ensure a more accurate sample statistic.

Part II:

1. Please circle your gender;

Female Male Other if other is selected please specify gender: ______________

2. How old are you? ______________

3. How many courses are you currently taking?

1 2

3 4+

4. What time is your earliest class?

7:30am 8:30am 9:30am 10:30am

11:30am 12:30pm 1:30pm 3:30pm after 5pm

5. Are you in a degree program?

Yes, what is your major? _________________

No

6. How much longer until you graduate from TCC?

this quarter 1 quarter

2–3 quarters 4 or more quarters

7. Which type of caffeine do you prefer? Please circle.

Coffee Energy Drinks Tea Soda Other,

if other is selected please describe the type of caffeine you prefer:_____________________

8. If you consume caffeine, what time, or times of the day do you consume caffeine? Please check all that apply;

Early Morning (5am – 8am)

Mid-Morning (8am – 11am)

Lunchtime (11am – 2pm)

Afternoon (2pm – 5pm)

Evening (5pm or later)

9. How many caffeinated drinks do you have a day? ___________________________________

10. Do you stop to get your caffeinated drink on your way here or do you make it from home? Please circle one

On the way Make at Home Both

Part III:

In conducting this survey, we’ve observed that out of the 30 students surveyed the average age was 22.7 years old. We also wanted to know, out of the female and males we surveyed how old they were compared to the sample mean. The average age of females is 22.8 years old, and the average age of males is 22.6 years old. Here we can also see that the average, median and mode were all within the same age of around 22 years old. Each of the age groups of all, female and male sit about 5 years from the average sample age of students at TCC.

While our statistical analysis is all about the amount of caffeine students consume at TCC, we’ve found that the average amount of caffeine students consume per day is 1.6 drinks. The mean, median and mode are very similar between them, therefore this analysis is not skewed.

Part IV:

We are interested in estimating the true population mean of the amount of caffeine that students consume a day. Within a sample size of 30, the average student at TCC consumes 1.6 servings of caffeine a day. Assuming the population standard deviation is unknown with a 0.05 significance level. The following calculation is how we determined our point estimate and the standard error.

Since, , is unknown a t test will be used along with the degrees of fitness of 29 and .

We are 95% confident that the true population mean of the amount of caffeinated drinks a student haves in a day is between 1.25 and 1.96 drinks. Our best estimate is that the average student drinks 1.6 caffeinated drinks per day.

Part V:

In the survey conducted by Nicole L. Olson, the average caffeine consumption was about one caffeinated drink per day amoung the college students at the University of New Hampshire. We believe the average caffeine consumption at TCC per day is greater than that of the University of New Hampshire as our hypothesis stated.

1. We want to test to see if our sample mean is greater than the population mean of the amount of caffeinated drinks students consume per day, therefore the null and alternate hypothesis are as follows;

2. The appropriate statistical test is a t test. The formulas I will be using are as follows;

3.

4. This is a one tail test, of If the sample t test is greater than the population t test, then the decision will be to reject the null hypothesis.

5. The values are as follows;

6. The following formulas were used to calculate the sample t value. This value will be the deciding critical value determining if we reject or fail to reject the null hypothesis.

7. Since , is greater than , the statistical decision is to reject the null hypothesis.

8. In other words, the average amount of caffeine students’ intake is greater than the expected average of one caffeinated beverage per day.

Part VI:

We want to determine if there is a correlation between the age of students at TCC and the amount of caffeine they consume in a day. In this analysis the dependent variable will be the amount of caffeine consumed, and age is the independent variable. The coefficient of determination is, , which is 0.02. Thus 2% of the change in the dependent variable is explained by the change in the independent variable. The coefficient of correlation is derived by taking the square root of .

The coefficient of determination is, , which is 0.02. Thus 2% of the change in the dependent variable is explained by the change in the independent variable. The coefficient of correlation is derived by taking the square root of . Therefore, there is a weak almost nonexistent correlation between our variables.

If we were to use this linear regression line to predict the caffeine intake based on the age of the student in, , form the regression line would be as follows;

Part VII:

We want to see if the types of caffeine students prefer at TCC is uniformly distributed within the categories as well as the time students consume. Both of these hypothesis are testing within a 0.05 significance level using the chi-square goodness to fit test.

For the preference of caffeine consumption based on type (categories: coffee, energy drink, tea, soda or no caffeinated drinks) and the sum of the observed chi squared was and based on a degree of freedom of 4 and a significance level of .05, our critical value was 9.488. And because our observed chi squared was equal to 26.26 the expected value did not equal the observed value and therefore is not uniformly distributed. The data shows a favor for coffee.

For the time of consumption during the day sorted by time of their classes (categories: early, mid-morning, lunch, afternoon, evening or no caffeine consumption) had an expected value of 8.67 people per time-period and when calculated with a degree of freedom of 5 and a significance level of .05, our critical value was 11.070. The total chi squared value equaled 19.76 and therefore the observed values did not equal the expected values and therefore is no uniformly distributed and shows a favor for early morning consumption.

Results / Summary Section:

In our research, we asked the question whether the early start times of a student’s classes would affect their caffeine consumption during the day. In order to include as many variables as possible, we included 10 questions for each sampler to answer. Our hypothesis before testing was that the student was to have their class, the more likely they were to consume caffeine. Our alternative hypothesis was that there would be poor correlation between class start times and the consumption of caffeine.

In questions 8 we asked, at what point in the day do you normally consume caffeine and the general trend of the data leaned towards early morning and mid-morning caffeine consumption.

In question number 4, we asked at what time was the sampler was taking classes the data had a general trend in favor of 7:30 classes and 8:30 classes.

These two questions result correlate in a positive relationship (the earlier the class, the more likely they are to consume caffeine in the early or mid-morning) and therefore the hypothesis that you are more likely to consume caffeine if your classes start in the morning cannot be rejected.

Sources:

Olsen, Nicole L. “Caffeine Consumption Habits and Perceptions among University of New Hampshire Students.”. Honors Theses Paper (2013): 27. Mar. 2013.Web. 5 June 2017.