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# GrC 320 Histograms

A brief presentation for Graphic Communication 320 on Histograms.
by

## Alyssa Pelletier

on 26 April 2010

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#### Transcript of GrC 320 Histograms

HISTOGRAMS A representation of
a frequency distribution by means of rectangles whose widths represent class intervals and whose areas are proportional to the corresponding frequencies Allow us to aggregate and sort variable data into (usually) equal sized bins to allow quick evaluation of large data sets 2. Determine the bin width, "H"
{H = R / K}

In this case, H = 8 / 9 = .88 Determine the starting boundary
{Min Value - 1/2 H}

In this case, 1 - .44 = .66 Make sure each bin is mutually exclusive & each data point fits into only one. Construct a histogram using the frequency table. This means that
Bin 1 is from . 66 to 1.54
Bin 2 is from 1.55 to 2.42
Bin 3 is from 2.43 to 3.31
etc...
3.
Interpreting
Histograms 1. Count the number of data points.
In this case, there are 25. Start with an unorganized set of numbers.
1, 9, 2, 8, 6, 8, 3, 7, 3, 7,
3, 5, 4, 6, 5, 2, 4, 6, 4, 6,
5, 5, 7, 5, 4 Determine the range "R"for the data set.
{R = Max Value - Min Value.}

In this case, R = 9 - 1 = 8 Divide the data set into a certain number of bins, "K"

In this case, we have less
than 50 data points, so we should use
5 -7 bins. However, our data is nice, so we're going to use 9. Creating Histograms
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