Introduction:
Group #1 designed a survey to learn about the consumption of energy drinks and caffeinated beverages. The population that we sampled were adults aged 2150 years of age. We attempted to obtain a random sample for the survey. In our discussion group, members stated they randomly chose people they worked with, neighbors, people they met at a store, or walking on a street, for example. These methods do not meet the true definition of a random sample, so I will have to call it a convenience sample.
We asked the following four questions:
1) Do you drink some kind of energy or caffeinated beverage? Y/N
2) Which one of the below beverages do you consume the most?
a. Coffee  b. Tea – c. Energy drink  d. Pop/soda  e.Other
3) On an average day, how many of these types of beverages do you consume?
4) On average, how many days a week do you consume these types of beverages? (17)
Looking at a CategoricalVariable:
The pie chart below represents data from the following question: “ Do you drink some kind of energy or caffeinated beverage?”
The pie chart above clearly depicts that 90%, 144 out of 160 respondents, answered “yes” to the question. 16 of the 160 surveyed (10%) responded, “no."
One sample proportion summary confidence interval:p : Proportion of successes Method: StandardWald 95% confidence interval results:

Confidence Level Interpretation: The Confidence Interval (CI) is a range of values used to estimate the true value of a population parameter. The above Summary Results utilize a 95% CI for the yes/no question of our survey. Our group can say that we are 95% confident that the true value of our population proportion falls between 0.854 and 0.947. In other words, if we randomly selected many different samples of 160 and created a CI for each, 95% of them would contain the population parameter p.
Looking at a Numerical Variable
The responses to question #3 “On an average day, how many of these types of beverages do you consume?” are shown in the histogram and summary statistics below.
Summary statistics:

Confidence Level Interpretation for the population mean µ: Based on the chart in our text, I used the tinterval to calculate the CI. Sigma σ is unknown. Our histogram shows a slightly rightskewed graph, but our n > 30 (n=160). These factors satisfy the requirements of using a tdistribution.
One sample T confidence interval:μ : Mean of variable 95% confidence interval results:

The above Summary Results use a 95% Confidence Interval regarding the population mean µ. Our group can say with 95% confidence that the actual value of µ falls between the limits of 2.130 and 2.620. Another way of saying this is if we randomly select many samples of 160 adults, 2150 years of age, and calculated the CI of each sample, 95% of them would contain the actual population mean.
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