Quantitative variables
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Quantitative Variables. Recall that quantitative variables have units, and are measured on a continuous scale… Examples: income (in $), height (in inches), website popularity (by number if hits). Quantitative Variables. Mathematical operations on quantitative variables makes sense …

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Quantitative Variables

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Quantitative variables

Quantitative Variables

  • Recall that quantitative variables have units, and are measured on a continuous scale…

  • Examples: income (in $), height (in inches), website popularity (by number if hits)


Quantitative variables1

Quantitative Variables

  • Mathematical operations on quantitative variables makes sense …

  • Adding, subtracting, taking the arithmetic average etc…


Visualizing quantitative variables

Visualizing quantitative variables

  • Histogram – note that the bars touch each other – the values at the bottom are continuous!


Visualizing quantitative variables1

Visualizing quantitative variables

  • Dot plot


So why visualize

So why visualize?

  • To see the features of the data

    • Shape

    • Center

    • Spread


Constructing a histogram

Constructing a Histogram


Step 1 choose the classes

Step 1 – Choose the Classes


Step 2 count

Step 2 – Count


Step 3 draw the histogram

Step 3 – Draw the Histogram


Identifying identifiers

Identifying Identifiers

  • Identifier variables are categorical variables with exactly one individual in each category.

    • Examples: Social Security Number, ISBN, FedEx Tracking Number

  • Don’t be tempted to analyze identifier variables.

  • Be careful not to consider all variables with one case per category, like year, as identifier variables.

    • The Why will help you decide how to treat identifier variables.


Shape modality and symmetry

Shape - Modality and Symmetry


Humps and bumps

Humps and Bumps

  • Does the histogram have a single, central hump or several separated bumps?

    • Humps in a histogram are called modes.

    • A histogram with one main peak is dubbed unimodal; histograms with two peaks are bimodal; histograms with three or more peaks are called multimodal.


Humps and bumps cont

A bimodal histogram has two apparent peaks:

Humps and Bumps (cont.)


Humps and bumps cont1

A histogram that doesn’t appear to have any mode and in which all the bars are approximately the same height is called uniform:

Humps and Bumps (cont.)


Symmetry

Symmetry

  • Is the histogram symmetric?

    • If you can fold the histogram along a vertical line through the middle and have the edges match pretty closely, the histogram is symmetric.


Symmetry cont

Symmetry (cont.)

  • The (usually) thinner ends of a distribution are called the tails. If one tail stretches out farther than the other, the histogram is said to be skewed to the side of the longer tail.

  • In the figure below, the histogram on the left is said to be skewed left, while the histogram on the right is said to be skewed right.


Anything unusual

Anything Unusual?

  • Do any unusual features stick out?

    • Sometimes it’s the unusual features that tell us something interesting or exciting about the data.

    • You should always mention any stragglers, or outliers, that stand off away from the body of the distribution.

    • Are there any gaps in the distribution? If so, we might have data from more than one group.


Anything unusual cont

Anything Unusual? (cont.)

  • The following histogram has outliers—there are three cities in the leftmost bar:


Shape outliers

Shape - Outliers

Do any unusual features stick out?

We will discuss these in more detail when we introduce box plots.


Why do we care about shape

Why do we care about shape?

  • When quantitative variables are skewed, we describe the center and spread using different measures than if the variable is symmetric.


The center of the distribution median

The center of the distribution - median

  • The “most typical value” in the data usually refers to some measure of the “center” of the distribution

  • The median is the point that divides the histogram into two equal pieces


Calculating the median

Calculating the median

  • First, order all values from smallest to largest

  • Let n = sample size

  • If n is odd, the median is located at the (n+1)/2 position

  • If n is even, the median is the average of the two middle points


Calculating the median1

Calculating the median

  • Example 1 : Earthquakes in N.Z.

  • 2010 EQ magnitudes in N.Z.: 3.2,3.2,3.3,3.4,3.5,3.5,3.6,3.6, 3.7, 3.8,3.9,3.9,6.4

  • Since n is odd:

    • Median is located at the

      (n+1)/2 = (13+1)/2 = 7th position

    • Median is 3.6


Calculating the median2

Calculating the median

  • Example 2 : Earthquakes in Samoa

  • 2010 Earthquake magnitudes in Samoa: 1.1,3.5,4.4,4.6,5.1,6.0

  • Since n is even:

    • Median is the average of

      • (n/2) = (6/2) = 3rd value (4.4)

      • (n/2)+1 = (6/2)+1 = 4th value (4.6)

    • Median is (4.4+4.6)/2 = 4.5


Median interpretation

Median - Interpretation

  • Example 1: The typical earthquake size in Fiji in 2010 was 3.6 on the Richter scale

  • How useful is this?


Spread

Spread

  • If all earthquakes in Fiji were 3.6, then the Median would be sufficient information

  • But they are not, so we need to see how spread out are the earthquakes around 3.6


Spread range

Spread - Range

  • Range = max value - min value

  • For the Fiji example:

    • Range = 6.4-3.2 = 3.2

  • This is not useful…why?


Spread iqr

Spread-IQR

  • Inter-quartile range

  • IQR = Q3 - Q1

  • Q1 = Median of 1st half

  • Q3 = Median of 2nd half

  • One single number that captures “how spread out the data is”


Spread iqr1

Spread-IQR

  • NZ Earthquake example cont:

  • 2010 EQ magnitudes in N.Z. (divided): 1st half: 3.2,3.2,3.3,3.4,3.5,3.5,3.6,

    2nd half: 3.6, 3.6, 3.7,3.8,3.9,3.9,6.4

  • Q1 = (n+1)/2 = (7+1)/2 = 4 -> 3.4

  • Q3 = (n+1)/2 = (7+1)/2 = 4 -> 3.8

  • IQR = 3.8-3.4 = 0.4

  • When n is odd, include median in both lists…don’t when n is even


Quantitative variables

IQR

  • Almost always a reasonable summary of the spread of a distribution

  • Shows how spread out the middle 50% of the data is

  • One problem is that it ignores a lot of individual variation


5 number summary

5-Number Summary

  • Minimum

  • Q1

  • Median

  • Q3

  • Maximum


The five number summary

The five-number summary of a distribution reports its median, quartiles, and extremes (maximum and minimum).

Example: The five-number summary for the ages at death for rock concert goers who died from being crushed is

The Five-Number Summary


Categorical or quantitative

Categorical or Quantitative?


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