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B027609 - PROBABILITY AND STATISTICS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2018-19
Coorte 2017 - 3-years First Cycle Degree (DM 270/04) in CIVIL, BUILDING AND ENVIRONMENTAL ENGINEERING
Course year
Second year - Second Semester
Belonging Department
Civil and Environmental Engineering (DICEA)
Course Type
Single education field course
Scientific Area
SECS-S/02 - STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH
Credits
6
Teaching Hours
54
Teaching Term
25/02/2019 ⇒ 07/06/2019
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Mutuality
Course teached as:
B028607 - STATISTICA PER L'INGEGNERIA
3-years First Cycle Degree (DM 270/04) in CIVIL, BUILDING AND ENVIRONMENTAL ENGINEERING
Curriculum AMBIENTE
B028607 - STATISTICA PER L'INGEGNERIA
3-years First Cycle Degree (DM 270/04) in CIVIL, BUILDING AND ENVIRONMENTAL ENGINEERING
Curriculum AMBIENTE
Teaching Language
ITALIAN
Course Content
Introduction to Statistics and Data Analysis. Elements of Probability. Random variables. Parameter estimation. Hypothesis testing. Linear regression.
Suggested readings (Search our library's catalogue)
Probabilità e Statistica per l’ingegneria e le scienze, S. M. Ross, Maggioli Editore, 2015 (terza edizione)
Further readings
Analisi statistica dei dati per l'ingegneria, R. E. Walpole at al., Pearson, 2016
Statistica per l'Ingegneria e le Scienze, W. Navidi, McGraw-Hill, 2008
Further readings
Analisi statistica dei dati per l'ingegneria, R. E. Walpole at al., Pearson, 2016
Statistica per l'Ingegneria e le Scienze, W. Navidi, McGraw-Hill, 2008
Learning Objectives
This course will provide students with the understanding of the principal tools to collect, summarize and analyze data in Engineering problems.
Prerequisites
Differential and integral calculus.
Teaching Methods
Lectures and classroom exercises.
Type of Assessment
Written Examination.
Course program
Descriptive Statistics: frequency distributions, graphical representations, measures of central tendency and measures of variability. Elements of Probability: sample space and events, the algebra of events, axioms of Probability, conditional probability.
Random Variables: marginal distributions, joint distributions and conditional distributions, expectation and variance, discrete random variables (Bernoulli,Binomia, Poisson, Hypergeometric), continuous random variables (Uniform, Normal, Exponential, Gamma, Weibull, distributions arising from the Normal one).
Sampling Distributions and Central Limit Theorem. Parameter Estimation: Maximum Likelihood Estimators, Confidence Intervals for the mean, the variance, and the difference of means.
Hypothesis testing: test for the mean, the variance, testing the equality of means, goodness of fit tests.
Linear Regression: parameter estimation and model assessment.
Random Variables: marginal distributions, joint distributions and conditional distributions, expectation and variance, discrete random variables (Bernoulli,Binomia, Poisson, Hypergeometric), continuous random variables (Uniform, Normal, Exponential, Gamma, Weibull, distributions arising from the Normal one).
Sampling Distributions and Central Limit Theorem. Parameter Estimation: Maximum Likelihood Estimators, Confidence Intervals for the mean, the variance, and the difference of means.
Hypothesis testing: test for the mean, the variance, testing the equality of means, goodness of fit tests.
Linear Regression: parameter estimation and model assessment.