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Chapter 3

# SCMA 301 Chapter Notes - Chapter 3: Bar Chart, Unimodality, Squared Deviations From The Mean

Department
Supply Chain Management and Analytics
Course Code
SCMA 301
Professor
custer
Chapter
3

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Chapter 3: Displaying and Describing Quantitative Data
Chapter 3: Displaying and Describing Quantitative Data
Displaying Quantitative Variable
For quantitative variables, there are no categories
oWe slice up all the possible values into bins and then count the number of cases that fall
into each bin
oThe bins, together with these counts, give the distribution of the quantitative variable
and provide the building blocks for the display of the distribution, called histogram
Histograms
oHistogram plots the bin counts as the heights of bar (increments in the vertical axis)
oCounts the number of cases that fall into each bin, and displays that count as the height
of the corresponding bar
oThere are no gaps between the bars of a histogram unless there are actual gaps in the
data
Gaps
Gaps indicate a region where there are no values
Can be important features of the distribution so watch out for them and
point them out
oVertical axis of a histogram shows the number of cases falling in each bin
oRelative frequency histogram
A report of the percentage of cases in each bin
Shape of the two histograms is the same
Only the vertical axis and labels are different
Faithful to the area principle by displaying the PERCENTAGE of cases in each bin
Bar Chart
oputs gaps between bars to separate the categories
Stem and Leaf Displays
oThey’re like histograms, but they also show the individual values
oQuantitative Data Collection
the data must be values of a quantitative variable whose units are known
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Chapter 3: Displaying and Describing Quantitative Data
Shape
When describing a distribution, pay attention to
oShape
oCenter
Mode
oDoes the histogram have a single, central bump (or peak) or several, separated bumps?
oSingle, most frequent value, but we rarely use the term that way
oOften use modes to describe the shape of the distribution
oUnimodal
a distribution whose histogram has one main hump
oBimodal
Distributions whose histograms have two humps
oMultimodal
Those with three or more
oUniform
A distribution whose histogram doesn’t appear to have any mode and in which
all the bars are approximately the same height
oSymmetry
If the halves on either side of the center look, at least approximately, like mirror
images
oTails
The (usually) thinner ends of a distribution
Skewed
If one tail stretches out farther than the other, the distribution is said to
be skewed to the side of the longer tail
oOutliers
Do any of the value appear to stick out?
Point out any stragglers or outliers that stand off away from the body of the
distribution
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find more resources at oneclass.com