Types Of Motorcycles In A Show Room Are An Example Of Which Type Of Data?

Types of Data in Statistics - Nominal, Ordinal, Interval, and Ratio Data Types Explained with Examples

If you're studying for a statistics examination and demand to review your data types this article will give you a brief overview with some simple examples.

Because permit'due south confront it: not many people study information types for fun or in their real everyday lives.

So let's dive in.

Quantitative vs Qualitative information - what's the divergence?

In short: quantitative means you can count information technology and information technology's numerical (think quantity - something you tin count). Qualitative means you can't, and it's not numerical (think quality - categorical data instead).

Boom! Unproblematic, right?

There's one more distinction we should get straight earlier moving on to the actual information types, and it has to do with quantitative (numbers) data: discrete vs. continuous data.

Discrete data involves whole numbers (integers - similar 1, 356, or ix) that tin can't be divided based on the nature of what they are.

Like the number of people in a class, the number of fingers on your hands, or the number of children someone has. You can't have i.9 children in a family (despite what the census might say).

Continuous data, on the other mitt, is the reverse. Information technology can be divided up every bit much equally yous want, and measured to many decimal places.

Similar the weight of a machine (can exist calculated to many decimal places), temperature (32.543 degrees, and so on), or the speed of an airplane.

At present for the fun stuff.

Qualitative data types

Nominal data

Nominal data are used to label variables without any quantitative value. Common examples include male/female (albeit somewhat outdated), hair color, nationalities, names of people, and and so on.

In plainly English: basically, they're labels (and nominal comes from "name" to help y'all remember). You lot accept brown pilus (or brownish eyes). You are American. Your proper name is Jane.

Examples:

What colour pilus practise you have?

Brown
Blonde
Black
Rainbow unicorn

What's your nationality?

American
High german
Kenyan
Japanese

Find that these variables don't overlap. For the purposes of statistics, anyway, y'all can't take both brown and rainbow unicorn-colored pilus. And they're simply really related by the main category of which they're a part.

rainbow-hair — A statistical bibelot...(source). Perhaps center color would've been a amend case. Excluding heterochromia. But tin can't win hither.

Ordinal data

The key with ordinal data is to call up that ordinal sounds like order - and information technology's the guild of the variables which matters. Not and so much the differences between those values.

Ordinal scales are often used for measures of satisfaction, happiness, and so on. Have you e'er taken one of those surveys, similar this?

"How likely are yous to recommend our services to your friends?"

Very likely
Probable
Neutral
Unlikely
Very unlikely

Run into, we don't really know what the difference is between very unlikely and unlikely - or if it's the same amount of likeliness (or, unlikeliness) as between likely and very probable. But that's ok. Nosotros just know that likely is more than neutral and unlikely is more than than very unlikely. It'south all in the order.

Quantitative data types

Interval Data

Interval data is fun (and useful) considering it'south concerned with both the order and difference between your variables. This allows you to measure standard deviation and central tendency.

Anybody's favorite example of interval data is temperatures in degrees celsius. 20 degrees C is warmer than ten, and the divergence between 20 degrees and 10 degrees is 10 degrees. The difference betwixt ten and 0 is also ten degrees.

If you need assist remembering what interval scales are, only remember about the pregnant of interval: the space between. So not only exercise you care about the order of variables, merely also nigh the values in between them.

There is a little trouble with intervals, however: there'due south no "true zero." A truthful zero has no value - at that place is none of that matter - but 0 degrees C definitely has a value: it's quite dank. You can likewise have negative numbers.

If yous don't accept a true zero, y'all can't calculate ratios. This means improver and subtraction work, but division and multiplication don't.

Ratio information

Give thanks goodness in that location'southward ratio data. It solves all our bug.

Ratio data tells u.s. most the gild of variables, the differences between them, and they have that absolute zilch. Which allows all sorts of calculations and inferences to be performed and drawn.

Ratio data is very similar interval data, except zippo means none. For ratio data, it is non possible to accept negative values.

For example, height is ratio data. It is non possible to have negative height. If an object'southward elevation is zero, so there is no object. This is different than something like temperature. Both 0 degrees and -5 degrees are completely valid and meaningful temperatures.

Now that yous have a basic handle on these data types y'all should be a scrap more than ready to tackle that stats exam.

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