Applied Statistics

Recommended Prerequisites

Knowledge and  mastery of the subjects taught in Mathematics in secondary educationand in Mathematics and Statistics of the same course.  

Teaching Methods

Detailed lectures (using some audio-visual devices) introducing and explaining concepts, principles and theories. In the theoretical-practical classes, students will solve problems with the guidance of the teacher.

The statistical software SPSS is used in the theoretical classes and in two practical classes, given in a computer room, to illustrate the main concepts of the syllabus.

The evaluation consists of a final exam or, alternatively, two intermediate tests. For the tests’ admission the presence of 75% of all classes is required.

Learning Outcomes

To provide basic mathematical knowledge that prepares the students to model standard behaviors of random phenomena, which occur in Science and in Engineering contexts.  To contribute to the acquisition of skills that enable describing, analyzing and interpreting real situations through random mathematical models.

Acquiring skills in synthesis and analysis, oral and written communication, problems solving, critic reflection, autonomous learning and practical application of theoretical knowledge. 

Work Placement(s)

No

Syllabus

1. Random variables and probability distributions. Real random variables discrete and continuous. Moments and parameters. Discrete and continuous distributions.

2. Estimation. Introduction to statistical inference. Point estimation: estimators and sampling distributions, methods to obtain estimates. Confidence intervals: generalities, confidence intervals for a population mean, for a gaussian population variance and for a proportion. Confidence intervals for comparison of means, or proportions, of independent or dependent populations

 3. Tests of Significance. Generalities, level of significance and power. Tests for a population mean, for a gaussian population variance and for a proportion. Tests for comparison of means, or proportions, of independent or dependent populations. Qui-Square test for the goodness of fit.

4. Simple linear regression model. Estimation, confidence intervals and tests for the regression parameters. Prediction intervals.

Head Lecturer(s)

Maria da Graça Santos Temido Neves Mendes

Assessment Methods

Continuous assessment
Frequency: 100.0%

Final assessment
Exam: 100.0%

Bibliography

Murteira, B., C. S. Ribeiro, J. A. Silva, C. Pimenta - Introdução à Estatística, 2001, McGraw-Hill, Lisboa.

Guimarães, R., Sarsfield Cabral, J., Estatística, 1997, McGraw-Hill, Lisboa.

Moore, D., McCabe, G., Introduction to the practice of statistics, Freeman, New York, 2006.

Devore, J.L., Probability and statistics for engineering and the sciences, Duxbury, 2000.

Andrews, L.C., R.L. Phillips – Mathematical Techniques for engineers and scientists, 2003, Spie Press, Washington.

Ross, S. - Introduction to Probability and Statistics for engineers and scientists, 1987, Wiley.