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Created: Feb 21, 2019
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Angela Woodall- "Analysis of Football Survey" M3A2
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Analysis of Football Survey



The purpose of the survey was to identify the television viewing habits of American Adults. There was a specific focus on  sports, with an emphasis on the 2019 Super Bowl, in which the New England Patriots were competing against the Los Angeles Rams. The sample group was comprised of 152 friends, co-workers, and family. The participants were people known to the surveyors who were easily reached and afforded the group the greatest opportunity of collecting relevant data.  For data collection, the surveyors used one of the main types of non-probability sampling, convenience sampling.  Random sampling was not the sampling method in the data collection process.

We asked the following four questions

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

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

  3. Do you watch football?

  4. Who do you think will win the 2019 Super Bowl?

Looking at a Categorical Variable

The pie chart below illustrates the responses to the question “Who do you think will win the 2019 Super Bowl?” based on response choices of: “Yes”, “No” or “I don’t know”.

Result 1: Pie Chart of who the survey participants thought would win Super Bowl 2019

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Eighty-seven of the 152 people surveyed, 57.24%, believed the New England Patriots would win the 2019 Super Bowl, while only 30, 19.74% of the people surveyed believed the LA Rams actually would take the title.  Thirty-five, 23.03% of the people surveyed, did not know who would win.

To identify if there was a correlation amongst those surveyed who actually watch football to who they thought would take the title of the 2019 Super Bowl, we can examine the bar plot below.

Result 2: Bar Plot of the numbers of hours of sports participants who watch TV watched

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The bar plot illustrates that the frequency of the participants in the survey who watch football were approximately 0.175 more likely to name the team they thought would win the Super Bowl.  The bar plot also illustrates that participants who watch football, and those who stated that they did not watch football, thought that the New England Patriots would prevail.  The number of participants, who watched football and had a response of “I don’t know” to the question, was almost equal to that of participants who did not watch football and thought the LA Rams would prevail.

Looking at a Numerical Variable

The responses to the question “In a typical week, how many hours of TV do you watch?” are shown in the histogram, boxplot, and summary statistics below.

Result 3: Histogram of number of hours of TV watched per week


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Result 4: Boxplot of number of hours of TV watched per week

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Result 5: Summary stats of number of hours of TV watched per week

ColumnnMeanVarianceStd. dev.MedianRangeMinMaxQ1Q3
Hours TV Watched 152 14.309211 108.70508 10.426173 12 60 0 60 7 20

The histogram shows a right skewed distribution.  The majority of the 152 participants watch between 10 and 15 hours of television.  The number of participants in the 0-5 hour range, 15-20 and 20-25 were very close, and each around 20 participants.  Approximately only one participant watched 45-50 hours and 60 hours.

The center of a skewed data set can be best described by the median.  Here the median is 12 hours of TV watched per week, while the mean is about 14.3 hours.  The midrange is 30 hours.  We can note that the mean is greater than the median, which is typical and evident of the right skewed distribution histogram.

There is a lot of variability in the responses to this question, with a range of 60 hours, and a standard deviation of 10.4 hours.  There is a range of 60 hours and a standard deviation of 10.427. Quartile 3=20, Quartile 1=7, with a median of 12. The Interquartile range (IQR) = 20-7=13 and the range/4 = 15, therefore outliers, seven, were limited but do represent legitimate data values. 

The boxplot reveals that there were 152 values; 7 Outliers; IQR 13; Upper limit of 36; Q3 of 20; Median of 12; Q1 of 7; and a Lower Limit of 0. 

Looking for a Relationship between Two Numerical Variables

To determine the relationship between the responses to the questions “In a typical week, how many hours of TV do you watch?” and “In a typical week, how many hours of sports programs do you watch?” we look at the scattered plot of the paired data.

Result 6: Scatter Plot of number of hours of TV watched per week vs number of hours of sports programs watched per week

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The scatter plot reveals that for the group of participants that watch between 10-15 hours of TV a week, the majority of the group spent approximately half of their TV time watching sports.

The scatter plot reveals a slight positive trend with a great deal of scatter.  Two outliers stand out in the scatter plot: 40 hours of TV watched with 20 hours of sports programs watched and 60 hours of TV watched with 5 hours of TV watched.  Outliers can dramatically affect the correlation coefficient.

The correlation coefficient for the paired data is 0.31562198, as shown below.  Since the value of r is greater than .196, we can statistically conclude that that the number of participants who watch television, do spend at least half of that time watching sports. 

Result 7: Correlation of hours of TV watched per week/hours of sports programs watched per week

Correlation between Hours TV Watched and Hours of Sports Watched: 0.31562198




















































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