COURSE CODE: URP1251
COURSE TITLE: STATISTICS FOR PLANNERS |
SUBMISSION DATE: 09/05/2012
DISCUSSION OF BOX PLOT GRAPH WITH APPROPRIATE EXAMPLES
The box plot goes back to John Tukey, which published in 1977 this efficient method to display robust statistics. Box plots, the term of graphical presentation of data. So it is one of the main parts of statistics because Statistics is the science of collecting, organizing, presenting, analyzing and interpreting data to assist in making more effective decisions.
The box plot is a graphical representation of data that shows a data set’s lowest value, highest value, median value, and the size of the first and third quartile. The box plot is useful in analyzing small data sets that do not lend themselves easily to histograms. Because of the small size of a box plot, it is easy to display and compare several box plots in a small space. A box plot is a good alternative or complement to a histogram and is usually better for showing several simultaneous comparisons.
How to use it:
Collect and arrange data. Collect the data and arrange it into an ordered set from lowest value to highest.
Calculate the depth of the median. d(M) = n+1
2 Where d = depth; the number of observations to count from the beginning of the
ordered data set
M = median
n = number of observations in the set of data If the ordered data set contains an odd number of values, the formula will identify which of the values will be the median. If the ordered data set contains an even number of values, the median will be midway between two of the values.
Calculate the depth of the first quartile. d(Q1) = (1)n + 2
Where d = depth; the number of observations to count from the beginning of the
ordered data set
(Q1) = the first quartile
n = number of observations in the set of data
The first quartile will be the value of the data item identified by this formula.
Calculate the depth of the third quartile. d(Q3) = (3)n + 2
Where d = depth; the number of observations to count from the beginning of the ordered data set
(Q3) = the third quartile
n = number of observations in the set of data The third quartile will be the value of the data item identified by this formula.
Calculate the interquartile range (IQR). This range is the difference between the first and third quartile vales. (Q3 - Q1)
Calculate the upper adjacent limit. This is the largest data value that is less than or equal to the third quartile plus 1.5 X IQR. Q3 + [(Q3 - Q1) X 1.5]
Calculate the lower adjacent limit. This value will be the smallest data value that is greater than or equal...
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