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Created: Jun 16, 2019
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Adults Commuting To Work: Louise Wood (Fuentes)
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Data to be Obtained from Adults Commuting to Work

 

The purpose of this survey was to gain more information about how people’s commute to work.  This survey can be offered at a future date and/or on a routine basis in order identify trends in how people are commuting.  For the purposes of this survey we are defining commuting exclusively as using a method of transportation to move from the place someone lives’ to travel to their place of work.  Survey participants were asked to exclude themselves from the survey if they did not meet our sample criteria.  Simple criteria requested that participants be eighteen years old or older and require commuting.  These sample participants were obtained via Facebook requests and one team member found survey participants by using direct questions and recoding answers from peers and fellow employees at his place of work.  

 

Below are the questions asked by this commuting survey: Data to be Obtained from Adults Commuting To Work

 

1) How long does it take you to commute to work?  ___ minutes (N1)

 

2) What is the distance that you travel to work? ___ miles (N2)

 

3) Do you pay for fuel?  Select one: Y N (C1)

 

4) What type of transportation do you most often use to commute to work? (C2)

-      Select one:

-      Walk

-      Bicycle 

-      Motor Vehicle (Car, Truck or SUV)

-      Public Transportation (Ride-Share, Train, Subway, Railway, or Bus)

 

Categorical Variables: The next section of this report will dive deeper into analyzing of the categorical questions offered by the survey.  

 

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

-      Select one:

-      Walk

-      Bicycle 

-      Motor Vehicle (Car, Truck or SUV)

-      Public Transportation (Ride-Share, Train, Subway, Railway, or Bus)

 

Result 1: Pie Chart for Question C2.   [Info]
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The participants sampled in this survey identified that commuting via motor vehicle was the most popular method of commuting to work by a significant margin and made up over 95% of the responses.  This group of motor vehicles included cars, trucks, and SUV’s. The least popular method of commuting to work was via bicycle. Walking to work and using public transportation to get to work were noted in the middle. No questions were asked for participants to use other sources of commuting or for participants to describe using combinations of these commuting methods (i.e. Public Transport and Bicycle).

 

The below bar plot confirms these descriptions by grouping the answers by the C1 Yes or No question. The bar plot also offers the same insight, that motor vehicles is the most popular response by the survey sample. 

 

Result 2: Bar plot for question C1 grouped by C2. Show relative frequencies.   [Info]
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Numeric Variables: In the next section, the questions which offered numeric variables will be discussed. The first question, noted as N1, is below and requests that survey participants detail the number of minutes required for them to travel to work. 

 

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

 

Result 3: Histogram of either N1   [Info]
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Result 4: Boxplot of the same column for which you created a histogram N1. Use fences to identify outliers.   [Info]
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The histogram above identifies that most commuters participate in a commute that is less that fifty minutes, with the most popular commutes from this sample landing very close to the twenty-minute mark. The distribution of the histogram peaks near the twenty-minute mark, and decreases quickly as the length of the commute increases (which a small dip at the fifty minute mark.).  This information is also reinforced by the box plot above.  Which also show the majority of commuters take less that fifty minutes to commute.  Additionally, both displays of information identify one extreme commuter that requires one hundred and twenty minutes for their daily work commute.  This information is consistent across both displays of information. 

 

Result 5: Summary statistics for the same column as above: n, mean, variance, standard deviation, median, rang   [Info]

Summary statistics:


ColumnnMeanVarianceStd. dev.MedianRangeMinMaxQ1Q3IQR
Minutes N118821.808511252.4551115.88883620118212011.52513.5

 

The summary data offered above details aggregate numeric descriptions of the sample data that was collected.  The histogram showed a popular commute time was close to twenty minutes, and the calculated mean is 21.8 minutes and the median is twenty minutes, both of which align with the information in the earlier displays of information. Additional new information is that one survey participant claimed their commute was only 2 minutes, which can be identified by the “Min” field above.  The same MAX number is identified at one hundred twenty, as we identified in the histogram and box plot before as an outlier.  This does not appear to an error, it only appears that one person has a very long commute. The IQR of the data set shows the first quartile at 11.5; the second quartile is at the median which is 20. Finally the third quartile is at 25. Since the interquartile range is Q3 minus Q1, 13.5 for the IQR value is affirmed by both the STATCRUNCH data and the formulas listed here. The interquartile range is 13.5.  This is close to the standard deviation and could be used as an acceptable approximation if needed. 

 

Relationships between variables: The numeric variables of the duration of the commute and the miles of the respective commute are listed below.


Result 6: Scatterplot of N1 vs. N2   [Info]
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The scatter plot above shows that the data points are increasing up and to the right.  As discussed before the data point at one hundred and twenty appears to the MAX send of sample set of commuters.  For the first time we see an outlier that is a “true” outlier which exists at the 45-minute mark and about 180 miles.  This appears to not be true.  It is not likely that this commuter is travelling 180 miles over the course of forty-five minutes during their daily commute.  This data point should be thrown out. Additionally, miles and minutes appear to have a positively correlated relationship.  When one increases in value, so does the other.  This reflects reality since it takes more time to drive more miles. 

 

Result 7: Correlation coefficient for the paired data (N1, N2)   [Info]
Correlation between Minutes N1 and Miles N2 is:
0.76206292


The correlation coefficient also confirms this positive correlation since the value is a positive value. It is statistically significant since r is great than 0.196. 

 

HTML link:
<A href="https://www.statcrunch.com/5.0/viewreport.php?reportid=88033">Adults Commuting To Work: Louise Wood (Fuentes) </A>

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