Probabilistic and Statistic Methods
A.Y. 2022/2023
Learning objectives
Knowledge of descriptive statistics. Position and variability indices. Acquisition of the principles and the techniques of regression and correlation between variables. Knowledge of inferential statistics. Analysis of Variance.
Expected learning outcomes
Students will be able to independently perform simple exploratory and inferential analyses using a software for data analysis. They will also have learned the concepts behind the main artificial intelligence techniques for the development of classification and regression models; therefore they will be able to interact constructively with the professional figure of data scientist in order to conduct more sophisticated analyses.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
The lessons will be carried out "in presence" and also through the Teams platform, in "synchronous" mode.
Course syllabus
1 - The language of statistics. 2 - The representation of data Organization of data and graphical representations. 3 - Data descriptors Measures of central tendency (mean, median mode) Measures of dispersion or variation. 4 - Bivariate analysis of data Bivariate analysis with qualitative and quantitative data. 5 - The probability The laws of probability. 6 - Random variables and probability distributions Random variables The binomial distribution The normal distribution. 7 - Sampling distributions and confidence intervals The most common estimators Desirable properties of an estimator Distribution of sample mean, The Central Limit Theorem Confidence intervals for the mean. 8 - The hypothesis testing: fundamentals The hypothesis test Stages of a hypothesis test Two-tailed tests Test for the average. 9 - Still about inference, Test on a single population hypothesis on the average hypothesis on a single proportion. 10 - Comparing two populations Test of hypothesis on the difference between the means of two populations - With independent samples - With dependent samples. 11 - Regression Analysis The simple linear regression model The inference in the case of linear regression model Confidence intervals and prediction intervals Correlation analysis. 12 - Analysis of Variance Principles of analysis of variance Fisher F distribution test for Anova.
Prerequisites for admission
The students should have sufficient knowledge of the mathematical language
Teaching methods
If pandemic conditions permit, classes will be held in attendance. Half theory classes and half tutorial classes are scheduled.
Teaching Resources
1) "Introduzione alla Statistica", di M. K. Pelosi e T. M. Sandifer, ed. McGraw-Hill, 2009
2) Material on the teachers' Ariel websites
2) Material on the teachers' Ariel websites
Assessment methods and Criteria
The exam consists in a written test.
The test is based on open and multiple choice questions . Moreover some exercises are included.
Test evaluation is based on the correctness of responses.
The final score is expressed as n/30.
Results are published on the Ariel site of teacher
The test is based on open and multiple choice questions . Moreover some exercises are included.
Test evaluation is based on the correctness of responses.
The final score is expressed as n/30.
Results are published on the Ariel site of teacher
Educational website(s)
Professor(s)
Reception:
on appointment
Via Celoria 2, Milan, Italy, 3rd floor (or by Skype/Teams/Zoom)