Probability and Statistics

In theoretical lessons the expository method is used during the explanation of the theoretical subjects.
Examples of real data are presented and exercises are resolved as concept’s application. In tutorial classes
students should practicing the concepts with the resolution of exercises in groups and individually and they use excel and statistical software (SPSS) in data analysis, statistical testing and regression methods.
In addition, students will be supervised, through the clarification of theoretical doubts and exercises resolution.

Objectives: Teaching tools to explore data, especially clinical data, and random models often used by practicing engineers. To establish an appropriate regression model to a real data set.
Generic skills: Application of knowledge and understanding. Critical thinking. Interpretation of results. Self-learning. Ability to work in groups, developing interpersonal relationships.
Specific skills: To learn exploring data with graphs and numerical summaries. To formalize problems involving the result of random experiments and identify the convenient probabilistic models.
To know how to use data from an experiment to make inferences about population parameters. To learn the “Least Squares Method” to obtain the linear regression model? understanding the meaning of the regression parameters and coefficient of determination. To be able to make statistical inferences about these parameters.
To use statistical software (SPSS) in statistical analysis.

Introduction.
Data Types. Methods of Collecting and Presenting Data. Samples, Populations and Randomness.
Probability
Fundamental concepts. Probability of an event. Applying the Probability Rules. Conditional Probability and independent events.
Random variables and Probability Distributions
Random variables and Probability Distributions. Probability distribution of a discrete random variable and probability distribution of a continuous random variable.
Some special probability distributions: binomial, Poisson, uniform and exponential. The Normal distribution. Sampling distributions.
Statistical Inference
Point and interval estimates of population parameters. Confidence Intervals. Hypothesis Testing.
Linear Regression
Straight-Line Models. Regression Concepts and Assumptions. Correlation. Tests and Confidence Intervals on Regression Parameters.
Using statistical software (IBM SPSS) in Statistics Analysis.

NAO