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Owner: agrosskc
Created: Jun 11, 2016
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Coffee Drinkers
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I. Introduction

My group and I designed a survery pertaining to coffee drinkers. We were able to work together as a group to gather answers we recieved, and organzie them into STATcrunch. We questioned Americans, the age is unknown. We did not do a random survey, as we individually gathered our information and brought it back to the group. I used a questionnare approach, where I handed out a piece of paper with the questions on the, and the person could answer at his/her leisure and return the results. We have a sample with voluntary reponses, do to not every person asked answered the questions.

The questions were the following:

How many cups of coffee consumed in one week?

How many cups of coffee purchased outside the home?

Do you prefer Milk/Cream or black?

Do you prefer Hot, Frozen, or Iced?


Looking at a categorical variable

The responses to the question " Do you prefer Hot, Frozen, or Iced?" are show in the pie graph below

Result 1: Pie Chart With Data   [Info]
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One can quickly tell that people prefer to drink hot coffee- as frozen is the least popular choice. Hot was 69.94%, and frozen was 7.36%. Iced was in between in the mid 20's.

In order to examine what people like in their hot, frozen or iced coffee, we can examine the bar plot below.

Result 2: Bar Plot With Data   [Info]
Right click to copy

With the results above, one can conclude that the majority of hot coffee drinkers like it black. Frozen drinkers do not like to add either milk/cream to their beverage. For iced coffee drinkers, the ratio of black to milk/cream is almost the same.


Looking at a numerical variable

The responses to the question, " How many cups of coffee are consumed in one week"? are show in the histogram, boxplot and summary statistics below.

Result 3: Histogram   [Info]
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Result 4: Boxplot   [Info]
Right click to copy

Result 5: Summary Stats   [Info]
Summary statistics:
ColumnnMeanVarianceStd. dev.Std. err.MedianRangeMinMaxQ1Q3


The histogram shows a left-skewed distribution with the majority of coffee consumed between 1-25 cups of coffee a week.

On the boxployt- it is shown that the boxes end very low, meaning the majority of people have less than 30 cups of coffee a week. Since the Q1 is 6 and Q3 is 17, the IQR is 11.

The mean is 14.2 with a median of 10.5. The median is a close respresntation of the IQR which is the measure of variability, is 11.

The standard deviation is the square root of the average squared deviation from the mean. The average squared deviation from the mean is also known as the variance. In this study, the standard deviation is 15.322 and the variance is 234.775. If you take the square root of the variance you should get your standard deviation, in this case is comes out to be 15.322. It is a good approximation of the standard deviation.

There are a few outliers in the data set, with the range being so wide. I do not believe they are errors, as some people drink more coffee than others.


IV. Looking for a relationship between two numerical variables

To determine whether or not there is a relationship between "How many cups of coffee a person drinks in a week"?, and " How many cups of coffee purchased outside the home?" we look at the scatter plot of a paired data.


Result 6: Scatter Plot   [Info]
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The scattor plot reveals a positive trend with a great deal of scatter. Most people drank coffee equally inside as they did outside their home. Two outliers I noticed were 64 people drank coffee inside the home, while the same people also bought 21 cups of coffee outside the home. Another was 115 cups were drank in the week, but only 20 outside the home.


The correlation coefficeint for the paired data is 0.41666095, as shown below.

Result 7: Correlation   [Info]
Correlation between var1 and var2 is:


Since the absolute value of r is more than .196, we can conclude that there is statistically significant correlation between the number of cups of coffee drank in a week, and the number of cups of coffee purchased outside the home. The scatter plot does reveal some lone splots, as one would suggest the correlation is not a strong one.


Data set 1. coffee drinkers- AG   [Info]
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<A href="">Coffee Drinkers</A>

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