# 31 lezioni di probabilità e statistica

Combinations and dispositions, probability space, conditional probability, independent events, total probability formula, Bayes formula. Notions of random variables (discrete and absolutely continuous), of mean and variance, standardization. Covariance and linear correlation of two variables, independent variables. The binomial, Poisson and normal distributions and the t, chi square and F distributions. Population, samples, data functions and relative degrees of freedom. Confidence intervals by average and by proportion. First and second species errors, tests on a proportion, on an average, on the difference of two averages, on several proportions, on the ratio of variances; furthermore, the calculation of the value "p" by means of R. Square line, linear correlation coefficient, linear determination coefficient. Linear dependence test in terms of F ratio and in terms of Student's t (Pearson test). Using R implementation of the Kolmogorov-Smirnov test on residual normality, as well as confidence intervals of the angular coefficient and correlation.

### Attendance and Credentials

Attendance
FREE!
Attendance Certificate
FREE!

Category

Computer and Data Sciences

Training hours

20

Level

Beginner

Course Mode

Tutored

Language

Italiano

Duration

8 weeks

Type

Online

Course Status

Self Pacement

Enrollments Start

Feb 3, 2020

Course Opens

Mar 2, 2020

Tutoring Starts

Mar 2, 2020

Tutoring Stops

May 20, 2020

Self Paced

May 21, 2020

Course Closes

Not Set
• Know the basics of probability, the binomial and Poisson distributions, the normal, t, chi squared and F distributions.
• Trace from one or two samples to the population parameters by means of confidence intervals and parametric tests, under a given significance level. In particular, test a proportion, an average, a difference in averages, several proportions, a ratio of variances.
• To be able to treat simple linear regression in the aspects of linear dependence tests, confidence intervals, normality test for the residues, linear correlation.
The prerequisites: knowledge of analytical geometry and graphs of elementary functions such as powers, roots, exponentials and logarithms; knowledge of the derivatives and primitives of elementary functions, with the calculation of their definite integrals.

Sheldon Ross, Probabilità e statistica per l’Ingegneria e le Scienze, Milano  2008.

The course is structured in 31 video lessons, also available in pdf format that the student is invited to study in full. In each lesson we require a certain mathematical attention, in the sense of clearly distinguishing between definitions, propositions or theorems, possible proofs, always with at least one example or application carried out in full. After the text of each lesson, the text of some exercises is proposed in pdf only, which the student is invited to perform before moving on to the next lesson. Preference is given to the use of numerical tables for statistical distributions, which are also attached in pdf. The use of the R software is reduced to the essential.
In order to obtain the attendance certificate and the open badge, the student will have to pass the two questionnaires in the course.