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 19, 2020
Descriptive Statistics and Regression Question 1 To see if the confinement due to COVID-19 has influenced the performance of a course, the number of failed subjects of each student in the current course and in the previous year course has been counted, obtaining the table below.

Degrees: Physiotherapy
Date: May 25, 2020
Descriptive Statistics and Regression Question 1 In a course there are 150 students, of which 50 are working students and the other 100 non-working students.

Degrees: Pharmacy, Biotechnology
Date: December 16, 2019
Question 1 The table below summarizes the time (in minutes) required to remove anesthesia after a surgery in a sample of 50 patients.

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