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B006815 - PROBABILITY THEORY AND STATISTICS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Teaching Methods
Type of Assessment
Course program
Academic Year 2012-13
Course year
Second year - Second Semester
Belonging Department
Statistics, IT and its applications "G. Parenti" (DISIA)
Course Type
Single education field course
Scientific Area
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES
Credits
6
Teaching Hours
48
Teaching Term
04/03/2013 ⇒ 14/06/2013
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Teaching Language
Italian
Course Content
Descriptive statistics. Introduction to probability theory. Conditional probability and independence. Discrete and continuous random variables. Introduction to statistical inference: point estimation, hypothesis testing and confidence intervals. Linear regression model. Analysis of variance. Introduction to the logistic model. Introduction to the software R.
Suggested readings (Search our library's catalogue)
Iacus S. (2010) Statistica. McGraw-Hill, Milano. ISBN 978-88-386-6575-2
Erto P. (2008) Probabilità e statistica per le scienze e l'ingegneria. McGraw-Hill, Milano. ISBN 978-88-386-6413-7
Erto P. (2008) Probabilità e statistica per le scienze e l'ingegneria. McGraw-Hill, Milano. ISBN 978-88-386-6413-7
Learning Objectives
The course is aimed to introduce students to key concepts underlying statistical reasoning. Students will learn how to organize and analyze a real set of data. At the end of the course students will have understand basic theoretical concepts and have sufficient familiarity with the basic techniques of data processing. They will be able to critically understand the characteristics, potentials and limits of models and statistical methods, presented during the course.
Teaching Methods
Lectures and labs.
Type of Assessment
Written and oral examination.
Course program
General ideas of statistical population, sampling and surveys. Variables and statistical units. Classification, tabulation and interpretation of data. Pictorial representation of data.Measures of central tendency and
their appropriate use; mode, median and mean. Measures of dispersion and their appropriate use: variance.
Probability events and their algebra, postulates. Bayes' Theorem.
Discrete and continuous random variables. Statistical inference. Point Estimation.
Confidence intervals for the means and proportions.
Theory of statistical tests: the formulation of hypothesis acceptance and rejection regions; p-value.
Association and dependency among variables: independence and correlation. Simple and multiple linear regression. Logistic regression model.
Introduction to the use of the software R is for descriptive and inferential statistics.
their appropriate use; mode, median and mean. Measures of dispersion and their appropriate use: variance.
Probability events and their algebra, postulates. Bayes' Theorem.
Discrete and continuous random variables. Statistical inference. Point Estimation.
Confidence intervals for the means and proportions.
Theory of statistical tests: the formulation of hypothesis acceptance and rejection regions; p-value.
Association and dependency among variables: independence and correlation. Simple and multiple linear regression. Logistic regression model.
Introduction to the use of the software R is for descriptive and inferential statistics.