Workers in Los Angeles averagely take short time to reach to workplace than in New York.
Descriptive statistics like mode, IQR, skewness etc do care about how the data is more leaning on one side than other. That is why these types of measures are helpful here.
Given data includes:
For Los Angeles: 32, 22, 10, 4, 2 (skewed left)
For New York: (18, 14, 10, 12 ,7) (very slightly skewed left)
Part a.
Comparing the distributions of travel times for both of the regions:
Histogram of Los Angeles is more skewed to the left. (values like 32, 20 lies on the left, and 5, 2 on the right tail)
Histogram of New York, in comparison to the histogram of Los Angeles is not that skewed and lies approximately leveled but is slight skewed to left.
This hows that time taken by worker to travel in Los Angeles is averagely very short and count of workers taking longer time falls dramatically.
On the other hand, that above thing doesn't happen for workers in New York.
Part b.
Central measure "Mode" can be used here. The reason of choosing mode is that it focuses on frequency and gives weights to outliers too. Mean, on the other hand takes average of all values thus might forget the data of skewness.
The 3rd moment of measure of skewness can be used to measure the degree of skewness in both the data.
Inter Quartile Range is also useful since it will tell quartile to quartile measures which can help not loose the data of skewness.
So, the important thing that should be included in the measure we use to describe this data is to include the information of skewness of the graph.
Descriptive statistics like mean, standard deviation etc forget about skewness.
Descriptive statistics like mode, IQR, skewness etc do care about how the data is more leaning on one side than other. That is why these types of measures are helpful here.
Learn more here:
https://brainly.com/question/3907939