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# Data Vizualization (Showcase Workshop)

## Quantitative Data

> Quantitative data can be:

• counted
• measured

It's ... NUMERICAL!

> Examples of quantitative data include:

• the weight of VW buses
• the wingspan of eagles
• the number of cars passing through an intersection each hour

> What's CONTINUOUS quantitative data?

• there's an infinite number of possible values for each outcome
• when you take measurements, it's usually continuous data you're finding!

But...but...

• You're right! - that doesn't mean the range of values you'd measure would be infinite.

> Examples of CONTINUOUS quantitative data include:

• the surface water temperature of the Lincoln Park swimming pool (64.1, 62.8, 63.5, 65.0 ºF)
• the hopping height of your pet jackrabbit (1.86, 2.41, 2.09, 2.11 m)
• the weight of CMU football players

> So while there's likely an upper limit to the weight of a CMU football player...there's an infinite # of exact hopping heights that could be measured.

> What's DISCRETE quantitative data?

• data with a limited # of possible outcome values
• when you are counting sometime to collect data, it's often discrete!

> Examples of DISCRETE quantitative data include...

• # of passengers in a car
• # of phone calls an office receives each day
• # of puppies in a litter
• # of credit hours registered for per student per semester > Interval

• Intervals between values are known
• Intervals between values are equal
• Example: temperature
• Interval from 50°F to 60°F is known
• Interval from 50°F to 60°F and 80°F to 90°F is equal
• Where might I see INTERVAL data: usually within data collected in environmental science

> Ratio

• A special case of INTERVAL data
• Interval data that starts at 0
• Where will I see RATIO data: environmental science - experimental research
• Example: rainfall
• starts at 0 inches (can't have negative rainfall!)
• difference between 2 and 4 inches is known
• difference between 2 and 4 inches = difference between 10 and 12 inches
• Example: whale mass
• starts at 0 (though no whale will weigh 0, they couldn't weigh LESS than 0!)

> Ordinal

• data has a relevant order
• magnitude between values is unknown or unequal
• When will I see ORDINAL data: survey data with ranked responses
• Example: a satisfaction scale
• scale from 1 to 5 (with 5 as highest satisfaction)
• 5 = better than 3
• 4 = better than 2
• difference between 5 & 3? unknown
• difference between 4 & 2? unknown
• There's an order but the interval differences aren't necessarily meaningful

## Where'd You Find this Good Info? 