Description

Theory
1

Theory/Practice
3

Instructors

Manel Cruz

Contents

Chapter 1 - Review of Probability: (1week)
Probability rules, Conditional probability, independence.
Multiplication and total probability rules. Bayes Theorem.
 

Chapter 2 - Distributions of probability. (7 weeks)
Random Variable (R.V.). Types of R.V.
Probability distributions and probability functions. Mean and variance.
Discrete distributions (Binomial and Poisson)
Continuous distributions (uniform, exponential and normal).
Additive and Central Limit Theorem
 

Chapter 3 - Estimation. (3 weeks)
Population and sample.
Random sample.
Sample mean distribution and related items.
Sample proportion.
Confidence intervals. Significance.
Confidence intervals for means, proportions and its variants.
 

Chapter 4 - Hypothesis Testing. (3 weeks)
Statistical hypothesis.
Type I and type II errors.
Tests on a population mean.
Tests on a population proportion.
Qui-square tests
 

Chapter 5 - Regression statistical analysis. (2 weeks)

Learning Outcomes

OB1: Evaluate probabilities and apply probability theory concepts to solve problems under uncertainty.

OB2: Identify and apply appropriate probabilistic models to analyse random phenomena.

OB3: Explain the concept of random variables and distinguish between random and deterministic variables.

OB4: Estimate parameters and formulate and test statistical hypotheses.

OB5: Apply statistical inference methods to support decision-making and data analysis.

OB6: Apply linear regression techniques for modelling and forecasting purposes.