19475 - Quantitative Methods for Decision Analysis

Course Unit Page

Academic Year 2018/2019

Learning outcomes

Student is expected to get a basic knowledge and precise framework about statistics and statistical techniques concerning the analysis of data bases. In particular the student is expected to learn: - probability ‘s tools - measures of variance - index numbers.

Course contents

Descriptive statistics

Probability

Discrete and continuous probability distributions

Mean, median, mode, central value

Frequency Analysis

Bar graphs, histograms, scatter plots, box plots

Standard deviation, variance

Kurtosis, skewness

Correlation and covariance

Hypothesis tests

Basics of linear regressions, p-values, residuals

Readings/Bibliography

Doane, Seward / Applied Statistics in Business and Economics, Mc Graw Hill, 2018 (6th edition)

Teaching methods

Theoretical lectures in class, practical applications in laboratory with the statistical software R

Assessment methods

Written exam, consisting in a test with closed theory questions and an application to be run at the PC with the statistical software R

Teaching tools

Blackboard, slides, PC

Office hours

See the website of Francesco Bergamaschi