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Created: Jun 22, 2019
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Descriptive Statistics for Survey: Data to be obtained from Adults Commuting To Work
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Our group created the following survey about adults commuting to work:

Survey: Data to be obtained from Adults Commuting To Work

1) How long does it take you to commute to work? 
___ minutes

2) What is the distance that you travel to work? 
___ miles

3) Do you pay for fuel? 
Select one: Y N

4) What type of transportation do you most often use to commute to work? Select one:
Motor Vehicle (Car, Truck or SUV)
Public Transportation (Ride-Share, Train, Subway, Railway, or Bus)

The following spreadsheet reflects the 188 responses our group obtained from family, friends, and aquaintances.  Angel (AM) contributed data set 1-20, Haley (HS) contributed data set 21-82, Louise (LW) contributed data set 83-102, Kathleen (KM) contributed data set 103-122, Yvette (YS) contributed data set 123-167, and Dolores (DT) contributed data set 168-188.  From the collected data, it is observed that the longest amount of time one travels to work is 120 minutes.  The longest distance one travels to work is 130 miles.  177 adults pay for their own fuel, with the common type of transportation being Motor Vehicle.

Data set 1. Survey: Data To Be Obtained From Adults Commuting   [Info]
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To satisfy the following descriptive statistics, Let:

C1=Do you pay for fuel?

C2=What type of transportation do you most often use to commute to work?

N1=How long does it take you to commute to work?

N2=What is the distance that you travel to work?


Result 1: Type Of Transportation Most Often Used   [Info]
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The pie chart of C2 illustrates that 95.21% of adults rely upon a Motor Vehicle as their most often used transportation.  Were our group to examine more than one way an adult could arrive at their place of employment, there likely would be an overlap into other types of transportation, resulting in a pie chart being too complex of an illustration (i.e., a person who takes a Motor Vehicle can also Walk, use Public Transportation, or use a Bicycle, and vice versa for all transportation methods).  Additionally, as Professor Romero pointed out to us, if we were to include the breakdown of Motor Vehicle type (Car, Truck, or SUV), the illustration would have to account for each of three separate components.  The Motor Vehicle slice of the pie then would be broken up even further, and not be as clear to decipher.  If one must compare Car, Truck, or SUV, a separate chart is necessary.


Result 2: Paying For Fuel Grouped By Transportation   [Info]
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This bar chart of the relative frequency of C1 grouped by C2 illustrates that when asked if they pay for their own fuel, over 0.8 of the individuals surveyed answered "Yes".  From common knowledge, most Motor Vehicles run on gasoline, so it was not surprising to observe the direct correlation between payment and transportation type.


I chose to examine N1, Minutes, in my Histogram, Boxplot, and Summary Statistics.

Result 3: Histogram Of Minutes   [Info]
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Result 4: Histogram Of Minutes (Non Relative Frequencies)   [Info]
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Result 5: Boxplot Of Minutes   [Info]
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Result 6: Summary Statistics For Minutes Column   [Info]

Summary statistics:

ColumnMedianRangeMinMaxQ1Q3nMeanVarianceStd. dev.

I ran both a Relative Frequency (result 3) and a Non Relative Frequency (result 4) calculation for the Histogram.  The results show that while total minutes traveled for all 188 responses vary, there is an approximate normal distribution which skews toward the right.  Boxplot results present an even line of outliers, showing the maximum outlier being the longest amount of time obtained in the survey-120 minutes.  Summary Statistics also reflect the varied diferrences of the shortest amount of time traveled (2 minutes) to the longest amount of time traveled (120 minutes).


Finally, I compared N1 to N2 in the following Scatterplot and Correlation Coefficent:

Result 7: Scatter Plot Of Minutes vs. Miles   [Info]
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Result 8: Correlation Coefficient for Paired Data (Minutes, Miles)   [Info]
Correlation between Minutes and Miles is:

Comparing mintues to miles addresses the fact that as a person spends more time traveling to their place of employment, milage will increase.  Both the Scatterplot and Correlation Coefficent show this in an even distribution.





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