Methods for Statistical Data Analysis

Measures of location and variability. Visual techniques for presenting discrete and continuous data. Sampling distributions and the central limit theorem. Confidence Intervals (CI) for the parameters of one or two independent populations. Asymptotic CI for the mean, proportion (one sample) and the difference in means, proportions (two samples). Testing statistical hypotheses for parameters using CI. Special topics in CI and relative tests. Basic elements in testing statistical hypotheses. Likelihood Ratio Test (LRT). Asymptotic LRT, chi-square goodness of fit test (test of independence) and Kolmogorov-Smirnov (KS) test. Tests for normality. Order statistics and CI for the median and quantiles. Sign test for the median. Methods for comparing the distributions of two samples. One-way Analysis of Variance (ANOVA) for independent and dependent samples and relative tests. Basic principles of experimental design. Simple linear regression. Correlation coefficients and tests. Modelling two-dimensional variables: the bivariate normal distribution and the theory of copulas. Applications are presented using the language R.

Division: Statistics, Probability and Operational Research
Recommended Literature:

Program of Studies:
Semester: A
ECTS: 7.5
Hours per week (Lec/Tut/L): 3/0/0
Code: MCDA101
Erasmus students: No