Statistics for Engineers 2016
Lecturer:
Prof. Luigi Salmaso, DTG, Universitā di
Padova: luigi.salmaso@unipd.it
Timetable:
Total: 42 hours
27
November 4 hours 9-13.30 (L. Salmaso)
11
December 4 hours 9-13.30 (L. Salmaso)
19
January 6 hours 9-13 and 14-16 (L. Salmaso)
20
January 6 hours 9-13 and 14-16 (R. Fontana)
28
January 4 hours 9-13.30 (L. Salmaso)
11
February 6 hours 9-13 and 14-16 (L. Salmaso)
12
February 6 hours 9-13 and 14-16 (F. Pesarin)
19
February 6 hours 9-13 and 14-16 (l. Salmaso)
ROOM: aula informatica (computer lab) B of Polo Meccanico in via Venezia -
Padova.
Specific Topics:
The course is an
introduction to statistical methods most frequently used for experimentation in
Engineering.
Lectures are planned
both in the classroom and in computer lab also for an
introduction to the use of the following statistical software:
·
R (www.r-project.org)
General Topics:
1. Elements of univariate statistical methods:
Elements of descriptive statistics: frequency, indices
of synthesis (position, variability and shape) and graphical representations (histogram,
boxplot, scatterplot).
Elements of probability theory: discrete and
continuous probability distributions.
Elements of statistical inference: sampling
distributions, point and interval estimation, hypothesis testing, One-way ANOVA, Multi-Way ANOVA, Factorial
Designs.
Main Reference: Stark, P.B., 1997. SticiGui: Statistics Tools
for Internet and Classroom Instruction with a Graphical User Interface <http://www.stat.berkeley.edu/~stark/SticiGui>
2. Statistical
Modelling: Experiments
and observational studies, regression, residuals versus error terms, matrix
algebra, standard errors, generalized least squares, normal theory of
regression, the F-test, path models,
inferring causation from regression, response schedules, types of variables,
maximum likelihood, probit and logit models, latent
variables, the bootstrap for estimating bias and variance.
Main References:
1.
Montgomery DC, Design and Analysis of Experiments,
2010, Wiley.
2.
Lattin J,
Carroll JD, Green PE, Analyzing Multivariate Data, 2003,Duxbury
Applied Series.
3.
Johnson RA, Wichern DW, Applied Multivariate
Statistical Analysis,1998, Prentice Hall; 4th edition.
4.
Hollander and Wolfe, Nonparametric Statistical Methods,
2nd edition, 1999, Wiley Series in Probability and Statistics.
5.
Shumway RH, Stoffer
DS, Time Series Analysis and Its Applications (With R Examples), 2nd Edition, 1998, Springer Texts in Statistics, NewYork.
6. Adhoc material by Lecturer.
Examination:
Attendance is
required for at least 2/3 of the lecture hours.
Final evaluation
will be based on the discussion of a case study within
the individual PhD project.