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Introduction
Check yourself

## Instruction

To really understand our dataset, we need to know what type of variables we have. As you learned in the Introduction, there are two kinds of variables:

• Categorical values describe data by characteristics or qualities. Ordinal categorical variables can easily be ranked by their inherent values. Nominal categorical variables have no built-in ranking value – they are just different ways to classify things.
• Numerical variables describe things in terms of numbers – usually by measuring or counting something. Interval numerical variables have no fixed zero point, and the intervals between the values are what is important, e.g. the difference between 1st place and 5th place. Ratio numerical variables have a fixed zero point that represents “no value”, and they can be meaningfully divided.

Now that you know the different types of variables, we can move on to the sample data. Remember, we'll be working with the country, pattern, and consumption variables from the alcohol_consumption dataset.

Here's a quick reminder of these variables:

• country - the name of the country,
• pattern - the level of danger related to that type of alcohol consumption,
• consumption - How many liters of pure alcohol are consumed per adult.