Description

Theory
2

Theory/Practice
2

Instructors

Rui Rocha

Contents

CP1-INTRODUCTION TO R SOFTWARE (2h)
 

CP2-DESCRIPTIVE STATISTICS (10h)
Data synthesis.
Sample features.
Bivariate dada analysis (linear regression)
 

CP3-PROBABILITY REVISIONS (3h)
Elementary probabilities.
Union of sets and additive rules.
Conditional probability. Independency. Multiplicative rules. Bayes theorem.
 

CP4-RANDOM VARIABLES (17h)
Definition of random variables.
Discrete and continuous random variables.
Mean, variance and other parameters of random variables.
Discrete and continuous probability distributions.
 

CP5-SAMPLING (6h)
Random sample. Statistics.
Sampling distributions.
 

CP6-ESTIMATION (10h)
Basic notions.
Point estimation.
Confidence intervals for parameters of normal and other populations.
 

CP7-PARAMETRIC HYPOTHESIS TESTS (12h)
Basic notions.
Hypothesis tests for parameters of normal and other populations.

Learning Outcomes

General objectives:
Give the students the concepts and techniques necessary to make a rigorous analysis and data interpretation, to make estimates with known uncertainty and to make well-founded decisions.
Specific objectives:
OB1 - Statistic data analysis with the R language.
OB2 - Organization, syntheses and analysis of sample data.
OB3 - Calculus of elementary probabilities.
OB4 - Utilization of random variables and computation of descriptive measures.
OB5 - Utilization of statistical distributions and identification of the conditions for their application.
OB6 - Parameter estimation.
OB7 - Decision based on data sample analysis.