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Owner: nursecandy
Created: Feb 24, 2019
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Tags: Racquet, Group, 3
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Football Survey
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I.             Introduction

A.        The survey my group and I designed was based around the upcoming Super Bowl.  We wanted to see how many hours of TV and sports were watched per week. Out of those hours of sports, did they watch football?  Lastly we wanted to know who they thought would win the Super Bowl. 

We did not have a true random sample as we polled our friends, family, and coworkers using Facebook, email, and asking in person.  Not everyone responded but of those that did, they were voluntary.

B.        We asked the following Questions.

1.     In a typical week, how many hours of TV do you watch?

2.     In a typical week, how many hours of sports do you watch?

3.     Do you watch football?

4.     Who do you think will win the Super Bowl?


New England Patriots, LA Rams, I don’t know



II.  Looking at Categorical Variable

A.     Who do you think will win the Super Bowl?  New England Patriots, LA Rams, I don’t know.


1.       The Pie Chart shows that just over half of the population, 57.24% believed the NE Patriots would win. The remaining percentage is 42.76% with “I don’t know” having 23.03% of that total. 


Result 1: Pie Chart With Data   [Info]
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2.  In the Bar Graft you can see that there is a difference in responses according to if they do or do not watch football.   Overwhelmingly the people who watched football picked the NE Patriots to win over the LA Rams or even the category of I don’t know.  The group that does not watch football has a small difference between those that believe the NE Patriots will win and I don’t know.  I believe it shows that those who watch football have more confidence in choosing a winner as opposed to those who don’t watch football. 


Result 2: Bar Plot With Data "Do you watch football?" grouped by "Who do you think will Win Sup   [Info]
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III.             Looking at a Numerical Variable 


A.  In a typical week, how many hours of sports do you watch?



Result 3: Histogram of "Hours of Sports Watched per Week"   [Info]
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Result 4: Boxplot "Hours of Sports Watched per Week"   [Info]
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Result 5: Summary Stats "Hours of Sports Watched per Week"   [Info]

Summary statistics:

ColumnnMeanVarianceStd. dev.MedianRangeMinMaxQ1Q3
Hours of Sports Watched1523.026315814.5555943.815179522002005







A.  The center of a skewed data set is best described by the median; it is not affected by high outlying values.  Here the median is 2 hours of sports watched per week, while the mean is about 3.03 hours.  The mean is greater than the median, which is typical in a right-skewed distribution.

B.  There was a lot of variability in the responses to the question, with a range of 20 hours, and a standard deviation of 3.8 hours.  The IQR, which gives the range of the middle half of the data, is 5 hours.  The range/4 = 5 and the standard deviation is 3.8. 

C.  The usual minimum value is 3.91 and the usual maximum value is 19.1 using a standard deviation of 2 from the mean of 3.03 and the Range Rule of Thumb.   The outliers make the Range Rule of Thumb not accurate in this data set. 


D.  The boxplot reveals several outliers, which are 13, 14,15,16, and 20 hours of sports watched per week. While these values represent, in my opinion, an excess of sports watched per week, they are plausible for those hard-core sports fans and I do not consider them to be in error.   

IV.             Looking for a Relationship between Two Numerical Variables. 

A.     To determine if there is a relationship between the responses to the questions “How many hours of TV do you typically watch per week” and “How many hour of Sports do you typically watch per week” we look at the scatter plat of the paired data.


The scatter plot reveals most of the data clumped together with other scattered data points in the graft. There appears to be no correlation between the two variables. 


Result 6: Scatter Plot "Hours of Sports vs Hours of TV Watched per Week"   [Info]
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Result 7: Correlation "Hours of TV and Sports Watched per Week"   [Info]
Correlation between Hours TV Watched and Hours of Sports Watched is:





B.  The value of r is 0.316 and in the extended table of Correlation Critical Values, for n=152, alpha=0.5 the critical value is 0.159.  Our r of 0.316 is greater than 0.159 it shows a very weak correlation.  The scatter plot reveals no correlation.  I believe there is no statistical significance. 




HTML link:
<A href="">Football Survey</A>

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