Descriptive Statistics is the part of Statistics in charge of representing, analysing and summarizing the information contained in the sample.
After the sampling process, this is the next step in every statistical study and usually consists of:

Explanation of the most important concepts in Statistics and Probability with examples.

In the last chapter we saw how to describe the distribution of a single variable in a sample. However, in most cases, studies require to describe several variables that are often related.

Descriptive Statistics provides methods to describe the variables measured in the sample and their relations, but it does not allow to draw any conclusion about the population.
Now it is time to take the leap from the sample to the population and the bridge for that is Probability Theory.

Random variables The process of drawing a sample randomly is a random experiment and any variable measured in the sample is a random variable because the values taken by the variable in the individuals of the sample are a matter of chance.

Probability distribution of a continuous random variable Continuous random variables, unlike discrete random variables, can take any value in a real interval. Thus the range of a continuous random variables is infinite and uncountable.

Degrees: Physiotherapy
Date: June 6, 2022
Question 1 The patients of a physiotherapy clinic were asked to assess their satisfaction in a scale from 0 to 10. The assessments are summarized in the table below.

Degrees: Physiotherapy
Date: May 6, 2022
Question 1 A basketball player scores 12 points per game on average.
What is the probability that the player scores more than 4 points in a quarter?

Degrees: Physiotherapy
Date: March 11, 2022
Question 1 The table below shows the number of credits obtained by the students of the first year of the physiotherapy grade.
$$48, 52, 60, 60, 24, 48, 48, 36, 39, 54, 54, 60, 12, 46$$

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