The fourth variable is gender, it is a categorical variable, and it contains

the information on a respondent's gender, whether it is male or female,

so if the respondent is female, so it is coded "1" and if the respondent is male so the variable is coded "2".

Then, the fourth variable is education, okay, and it has 3 levels.

The indication, the variable education here, include information on respondent's level of education.

The variable is coded "1", if the respondent's level of education is low,

is coded "2", if the level of education of the respondent is medium

and is code "3", if the level of education of the respondent is high.

The same for the income,

corresponds to the income level of the household, where the respondent is living

and it has three levels and also it is coded as "1", if the level of income is low,

"2", if the level of income is medium

and "3" if the level of income is high.

So, let's move to see, I mean what kind of techniques we can use to describe categorical and quantitative variables.

Starting with a categorical variable.

A categorical variable can be described using non-graphical and graphical tools.

The non-graphical tool consists in displaying the frequency or, and

sometimes together, the percentage of each level of the categorical variable in a table.

For example in table 2, the variable 'education' is described in terms

of frequency and percentage of each one of the three educational levels.

As you can see, in the table 20% of respondents have a low level of education,

30% of them have a medium level of education and 50% of the respondents,

that participated in the choice experiment, have a high level of education.

Another way to describe the different levels of a categorical variable, is to use a histogram

or any other sort of a graphical technique. I think you are familiar with this kind of representation.

For example, the histogram displayed in Figure1,

represents the distribution of the variable 'level of education'.

For quantitative variables now: for quantitative variables the mean and the standard deviation,

are the most commonly used statistical tools, to describe the quantitative variable.

The mean of a set of observation is computed

by adding their values and dividing the sum by the number of observation.

The standard deviation, measures the spread of the distribution of a quantitative variable,

by looking at how far the observations are from their mean.

For example in Table 3, the variable consumers 'willingness to pay' for strawberries

and the level of consumption of organic food are described,

by providing information about the total number of observation, the mean and the standard deviation

of each variable as well as the minimum and the maximum values of each one of the two variables.

Also a quantity variable can be described using a graphical technique, like a histogram,