Statistical Methods

In theoretical-practical lessons the expository and inquisitive method is used for the theoretical subjects. Examples of real data are presented and exercises are solved as concept’s application. In laboratorial classes students should practice the concepts with the resolution of exercises in groups and individually using either excel or statistical software, such as SPSS or R, in statistical analysis. In addition, students will be supervised, through the clarification of theoretical doubts and exercises resolution.

Evaluation:
Assessment can be either continuous or by a final exam. Continuous assessment consists of one intermediate test (50%) and a final test (50%). Successful continuous assessment requires a minimum of 4/10 points in each test, and a final of 10/20. Alternatively, or in the case the student does not succeed the continuous evaluation, the evaluation is made through a final examination (100%).

To learn, understand and know how to use basic probability and statistical tools that allow students to explore data and analyse random models often used by practicing industrial engineers.
Generic skills: Application of knowledge and understanding. Critical thinking. Interpretation of the results. Accomplishment and decision making. Communication. Self-learning. Ability to work in team, developing interpersonal relationships.
Specific skills: To learn how to explore data with graphs and numerical summaries; the meaning of correlation and how to bivariate regression, understanding the meaning of the regression coefficients. To formalize problems involving the result of random experiments and identify the appropriate probabilistic model’s parameters. To know how to use data from an experiment to make inferences about population parameters. To know how to use statistical software (SPSS or R) in descriptive statistics, correlation and regression, confidence intervals and hypothesis tests.

Probability Theory
Probability and Common Probability Distributions.
Probability of an event. Applying the Probability Rules. Conditional Probability and independent events.
Random variables
Probability Distributions. Probability distribution of a discrete random variable and probability distribution of a continuous random variable. Measures of central tendency and measures of dispersion. Some special probability distributions.
Statistical Inference
Sampling distribution. Point and interval estimates of population parameters. Confidence Intervals. Hypothesis tests.
Descriptive Statistics. Regression
Methods for collecting and presenting data. Tables of frequencies and graphical representation. Measures of location and dispersion. Simple linear regression model. Estimation of the parameters of the regression model by the method of least squares. Inference based on least squares estimators.

NAO