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

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Supply Chain Management and AnalyticsCourse Code

SCMA 301Professor

custerChapter

3This

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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

instead of count

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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Business Stat

Chapter 3: Displaying and Describing Quantitative Data

Shape

When describing a distribution, pay attention to

oShape

oCenter

oSpread

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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