- Is color ordinal or nominal?
- What are 2 examples of quantitative data?
- What type of variable is height?
- What type of data is height?
- Is age nominal or ordinal?
- Is gender ordinal or nominal?
- Is weight nominal or ordinal?
- What are 3 types of variables?
- What are the 5 types of variables?
- Is height an ordinal variable?
- Is ordinal qualitative or quantitative?
- Is height an independent variable?
- Is gender scale nominal or ordinal?
- What are 4 types of data?

## Is color ordinal or nominal?

When measuring using a nominal scale, one simply names or categorizes responses.

Gender, handedness, favorite color, and religion are examples of variables measured on a nominal scale.

The essential point about nominal scales is that they do not imply any ordering among the responses..

## What are 2 examples of quantitative data?

There are two general types of data. Quantitative data is information about quantities; that is, information that can be measured and written down with numbers. Some examples of quantitative data are your height, your shoe size, and the length of your fingernails.

## What type of variable is height?

Continuous variables For example, the height of a student is a continuous variable because a student may be 1.6321748755… metres tall. However, when the height of a person is measured, it is usually measured to the nearest centimetre.

## What type of data is height?

Quantitative data is numerical. It’s used to define information that can be counted. Some examples of quantitative data include distance, speed, height, length and weight.

## Is age nominal or ordinal?

Age can be both nominal and ordinal data depending on the question types. I.e “How old are you” is a used to collect nominal data while “Are you the first born or What position are you in your family” is used to collect ordinal data. Age becomes ordinal data when there’s some sort of order to it.

## Is gender ordinal or nominal?

There are two types of categorical variable, nominal and ordinal. A nominal variable has no intrinsic ordering to its categories. For example, gender is a categorical variable having two categories (male and female) with no intrinsic ordering to the categories. An ordinal variable has a clear ordering.

## Is weight nominal or ordinal?

When working with ratio variables, but not interval variables, the ratio of two measurements has a meaningful interpretation. For example, because weight is a ratio variable, a weight of 4 grams is twice as heavy as a weight of 2 grams.

## What are 3 types of variables?

A variable is any factor, trait, or condition that can exist in differing amounts or types. An experiment usually has three kinds of variables: independent, dependent, and controlled.

## What are the 5 types of variables?

There are six common variable types:DEPENDENT VARIABLES.INDEPENDENT VARIABLES.INTERVENING VARIABLES.MODERATOR VARIABLES.CONTROL VARIABLES.EXTRANEOUS VARIABLES.

## Is height an ordinal variable?

An ordinal variable, is one where the order matters but not the difference between values. … When the variable equals 0.0, there is none of that variable. Variables like height, weight, enzyme activity are ratio variables.

## Is ordinal qualitative or quantitative?

Ordinal. On the other hand, a qualitative ordinal variable is a qualitative variable with an order implied in the levels.

## Is height an independent variable?

In this study, time is the independent variable and height is the dependent variable. Answer 5: When you make a graph of something, the independent variable is on the X-axis, the horizontal line, and the dependent variable is on the Y-axis, the vertical line.

## Is gender scale nominal or ordinal?

There are four basic levels: nominal, ordinal, interval, and ratio. A variable measured on a “nominal” scale is a variable that does not really have any evaluative distinction. One value is really not any greater than another. A good example of a nominal variable is sex (or gender).

## What are 4 types of data?

In statistics, there are four data measurement scales: nominal, ordinal, interval and ratio. These are simply ways to sub-categorize different types of data (here’s an overview of statistical data types) .