Applied Statistics for Engineers

Recommended Prerequisites

Mathematical Analysis I, Mathematical Analysis II, Statistichal analysis of Data.

Teaching Methods

The teaching is provided in theoretical and practical sessions. The lectures are expository and include the presentation of examples that motivate and enable to understand the notions exposed.

Some of the practical classes will take place in computer lab so that students can acquire the basic skills associated with statistical analysis using a specific software.

It could be developed a computational design modeling of  real or simulated stochastic systems  in order to develop critical skills and interpretation of results.

Learning Outcomes

This course comes as a complement to the curricular unit of "Statistical analysis of Data". Together these two courses are designed to allow students to gain an integrated view of the basic statistic concepts and techniques frequently used in the field of engineering. At the end of the course students should be able to use the methods of statistical analysis critically and independently in the preparation of decisions. It is also expected that students are able to perform regression analysis and apply parametric and non-parametric tests to the modeling of stochastic systems,  perform analysis of variance, design simple experiments and use statistical software in solving the mentioned problems.

Work Placement(s)

No

Syllabus

1. Non-parametric tests: Goodness of fit tests,  independence tests , homogeneity tests.

Paired samples : tests of signals, Wilcoxon test.

2. Simple linear regression model: estimation, adequacy and inference.

Multiple linear regression and nonlinear regression.

3. Analysis of Variance: models with one or two factors with fixed, variable or mixed effects.

4. Design of Experiments: Principles and some suitable experimental designs (Taguchi method).

5. Introduction to reliability and survival analysis : function failure rate and stochastic aging; reliability of independent component systems .

Head Lecturer(s)

Manuel António Facas Vicente

Assessment Methods

Assessment
There are 2 types of grading: during the semester or by final examination. Grading during the semester may involve problem solving or the development of a project (weighting from 0 to 40%), taking tests (with 0-30% total weight) or midterm exams (with 50-100% weight). Grading by final examination includes taking a written exam (weighting 50 to 100%): 100.0%

Bibliography

• Murteira, B., C. S. Ribeiro, J. A. Silva, C. Pimenta (2010) - Introdução à Estatística, 3ª ed., Escolar Editora, Lisboa.

• Cobb, G.W. (1998) Introduction to design and analysis of experiments, Springer.

• Devore, J.L. (2011) - Probability and statistics for engineering and the sciences, 8ª ed., Brooks/Cole.

• Draper, N.R., Smith, H. (1998) - Applied Regression Analysis, 3^rd ed. Wiley.

• Harold, R. (1990) Analysis of variance in Experimental Design, Springer.

• Maroco, J. (2007) - Estatística com utilização do SPSS, 3ª ed., Edições Sílabo.

• Montgomery, D.C. (2005) Design and Analysis of Experiments, 6th ed., Wiley

• Montgomery, D.C., G.C. Runger (2007) - Applied Statistics and Probability for Engineers, 4ª ed., 2007, Wiley.

• Ross, S. (2009) Introduction to Probability and Statistics for Engineers and Scientists, 4^th ed., Elsevier, Academic Press.

• Roy, R.k. (1990) A primer on the Taguchi method, Van Nostrand Reinhold, NY.

• Sprent, P., Smeeton, N.C. (2007) Applied non-parametric statistical methods, 4^th ed.,  Chapman & Hall, CRC.