Probability and Statistics I

General characterization

Prerequisites

The students should be provided with basic knowledge about calculus (mathematical analysis: geometric and arithmetic progressions, sumations, series, derivation and integration)

Bibliography

Coelho, C. A. (2008). Tópicos em Probabilidades e Estatística, Vol. I, Vol. II (Cap.^s 6,7).

Mood, A. M., Graybill, F. A. e Boes, D. C. (1974). Introduction to the Theory of Statistics, 3ª ed., J. Wiley & Sons, New York.

Montgomery, D. C. e Runger, G. C. (1998). Applied Statistics and Probability for Engineers, 2ª ed., J. Wiley & Sons, New York.

Ross, S. M. (1999). Introduction to Probability and Statistics for Engineers and Scientists. J Wiley & Sons, New York.

Murteira, B. J. F. (1990). Probabilidades e Estatística, Vol I, 2ª ed., McGraw-Hill Portugal, Lisboa.

Rohatgi, V. K. (1976). An Introduction to Probability Theory and Mathematical Statistics. J. Wiley & Sons, New York.

Teaching method

• 2 weekly Lecture-Lab Classes of 2 hours each, where the concepts are introduced and analysed and the main results are derived and proven. Illustrative examples are also shown and and problems pertaining the concepts and results shown are solved with the active participation of the students..

Evaluation method

1. Pre-Requisites

In order to be able to obtain the final approval for the course, either by Tests or Exam, the students will need to obtain a rate of attendance of at least 2/3 to Labs.

 2. Evaluation

• The recommended form of evaluation consists in 2 Tests:
• 1st Test - weight: 50% (0-20 scale)
• 2nd Test - weight: 50% (0-20 scale) - with a minimum grade of 7
• Students will be approved in case they obtain from tests a final grade of 9.5 or more (on a 0-20 scale).
• Students who obtained a final grade from tests less than 9.5 (on a 0-20 scale), may have access to a final Exam.
• Students with a final grade of more than 17 (on a 0-20 scale) have to go through an oral examination, or their final grade will be equal to 17.

Subject matter

1 – Combinatorics (short review) 

2 – Elementary Probability Theory

• Random experiment and Outcome space
• Event and Event Space
• The concept of Probability. Probabilities Properties
• Conditional Probability and independence of events
  • Some useful and iteresting results on Conditional Probabilities
  • Conditional Independence and (Marginal) Independence
  • Odds and Odds ratio
  • Illustrative example of the reason of the definition of the Independence of 3 or more events
  • Examples of application (of the notion of conditional probability, Bayes formula and Bayes and Total Probability Theorems)
  • The Borel-Cantelli Lemmas

3 – Random variables and Probability Distributions

• Definition of random variable. Examples
• Probability Distribution functions. Properties. Quantiles
• The Survival and Risk functions – two alternative ways of representing the distribution of a r.v.
  • Development and study of a Risk function
  • Survival and Risk functions for discrete r.v.s
• Expected value. Properties. Moments. Some important inequalities involving moments
• Moment generating functions and characteristic functions
• The distribution of Y = g(X)

4 – Joint and conditional distributions of random variables

• Joint distribution of two or more random variables
• Joint and marginal moments
• The joint moment generating function
• Conditional distributions and independence
  • Conditional moments
  • The conditional expected value
  • Some additional notes on the conditional expected value
  • Independence of r.v.s
  • Consequences of independence
  • Other conditional distributions
  • Truncated distributions as conditional distributions
• Joint distributions of r.v.s of different types
• The distribution of (Y1, Y2) = g(X1,X2)
• The distributions of Sum, Difference, Product and Ratio of two r.v.s
• Mixtures

5 – Discrete random variables

• The Uniform distribution
• The Geometric distribution
• The Negative Binomial distribution
• The Bernoulli distribution
• The Binomial distribution
• The Hipergeometric distribution
• The Poisson distribution

6 – Continuous random variables

• The Exponential distribution
• The Normal distribution
• The chi-square distribution
• The T distribution
• The F distribution
• The Gamma distribution

7 – Brief reference to multivariate distributions

• The Multinomial distribution
• The Multivariate Normal distribution

Programs

Programs where the course is taught: