A series of free Statistics Lectures with lessons, examples & solutions in videos.

This is the second page of the series of free video lessons, “Statistics Lectures”. These lectures introduce the concepts of variables, independent and dependent variables and discuss measurement scales.

**Related Pages**

1: Descriptive vs. Inferential Statistics

3: Frequency Distributions & Cumulative Frequency

4: Central Tendency

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Descriptive statistics summarizes a sample, giving us some ideas about the mean and spread of the data, etc. Inferential statistics use a sample or samples to describe what the population is like; in other words, infering the characteristics of the population from the sample/s.

A variable is a property that can take on many values.

Two kinds of variable:

- Quantitative Variable
- Qualitative/Categorical Variables.

Quantitative variables are measured numerically. With measurements of quantitative variables you can do things like add, subtract, multiply and divide, and get a meaningful result.

There are two kinds of quantitative variables:

- Discrete variables
- Continuous Variables.

A Discrete Variable is a quantitative variable with a finite number of values.

A Continuous Variable is a quantitative variable with an infinite number of values.

Qualitative/Categorical variables allow for classification based on some characteristic.

An independent variable is any variable that is being manipulated.

A dependent variable is any variable that is being measured.

There are four data types of measured variables:

- Nominal
- Ordinal
- Interval
- Ratio

Nominal data (also known as qualitative/categorical data) is data that is split into categories.

Ordinal data is data where order matters, but distance between values does not.

Interval data is data where order matters, and distances between values are equal and meaningful, and
a natural zero is not present.
Ratio data is data where order matters, distances between values are equal and meaningful, and a
natural zero is present.

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