DATA ANALYSIS
cod. 07520

Academic year 2019/20
2° year of course - Second semester
Professor
Academic discipline
Telecomunicazioni (ING-INF/03)
Field
Ingegneria delle telecomunicazioni
Type of training activity
Characterising
72 hours
of face-to-face activities
9 credits
hub: PARMA
course unit
in ITALIAN

Learning objectives

Understanding and capability to communicate the foundation of probability theory, and of descriptive and inferential statistics. Knowledge and application of technical tools regarding regression models and statistical testing.

Prerequisites

Mathematical analysis

Course unit content

Descriptive statistics. Inferential statistics. Elements of probability theory. Discrete and continuous random variables. Central limit theorem. Parameter estimation. Confidence levels. Statistical hypothesis testing. Regression.

Full programme

Data organization e description, mean, median, mode, histograms, variance and standard deviation.
Normal model and correlation.
Sample space and events, probability axioms, binomial coefficient, conditional probability, Bayes' formula, independent events.
(approx. 6 hours)

Continuous and discrete random variables, joint and conditional distributions, expected value, covariance, moment generating function.
Random variable models: Bernoulli, Poisson, hypergeometric, binomial, uniform, normal, exponential, gamma, chi-square, t, F.
(approx. 22 hours)

Sample meand, central limit theorem, sample variance.
Maximum likelihood estimators, confidence intervals, bayesian estimators.
Hypothesis testing.
Significance levels, mean and variance testing, t-test, hypothesis tests in Bernoulli populations and on the mean of Poisson distributions.
(approx. 28 hours)

Regression parameters estimation, inferential statistics, estimators distributions, transforming to linearity, weighted least squares.
(approx. 16 hours)

Bibliography

Sheldon M. Ross
Introduction to probability and statistics for engineers and scientists
Elsevier, fifth edition, 2014

Teaching methods

Classroom lectures and exercises.
Homework exercises and (possible) projects.
Software use (Matlab) for problem resolution.

Assessment methods and criteria

Written exam with possible supplementary oral.

Other information

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