Applied Statistics

Learning Outcomes: 

The goal of the course is to familiarize students with the principles of applied statistics using examples from the environmental engineering area. Students will be able to realize statistical analysis of data, compare averages via ANOVA or via non-parametric tests, depict data using the correct graphs and tables, check normality, transform data, make linear regressions and study correlations and their significance. Emphasis will be given on variance analysis (t-test, ANOVA with Tukey test) techniques for comparing means when conducting experiments or making field measurements. Basic non-parametric tests and the chi-squared test will be taught. All above will be performed via simple spreadheets (Excel) or free-ware software available in the Web (e.g. https://www.socscistatistics.com/, https://www.gigacalculator.com/calculators/statistics/, free phone Apps) with mandatory laboratories in the computer center of the school.

General Skills:
The following specific skills will be gained after attending the course:

To analyze data using spreadsheets and other free-ware statistical software.
To perform normality check, transform data and to finally choose between parametric or non-parametric statistical tests.
To compare means and make randomized block design analyses.
To properly make graphs of results, interpret data through simple graphs.
To analyze social science data with categorical / qualitative variables.

Course Content:
1. Introduction: Qualitative, quantitative measurements, Significant digits, Means, Standard deviations, Standard Errors, Variances.
2. Distributions – Normal data: Repeated measurement distributions, Errors, Residuals, Normality Checks (Anderson-Darling).
3. Data transformations to normalize data
4. Repeatability: Confidence intervals, t test (independent t, paired t-test)
5. Graphical representations of data – Making and choosing proper graphs
6. Randomized block designs and their analysis
7. ANOVA test (I): Comparisons of means, single ANOVA
8. ANOVA Test (II): Tukey Test, Dunnett Test, multiple factor ANOVAs
9. Variables and linear regression: Regression analysis and correlation.
10. Linear regression, Correlations, R2, Pearson correlation coefficient, Other regressions, Spearman’s correlation. Curve fitting
11. Social statistics: Non-parametric statistical controls (Mann Whitney, Kruskal-Wallis, Mood’s median) and correlation of categorical variables
12. Chi-squared test
13. In class-exercices – Revisiting basic principles

Suggested Bibliography: 

1. Πειραματικός Σχεδιασμός και Στατιστική Ανάλυση, Δ. Κομίλης, Εκδ. Ζυγός, 2012 (book in Greek)
2. Statistics for Environmental Engineers, Berthouex P, Brown, L, 2nd Ed., CRC Press, 2002
3. Στατιστική: Θεωρία και Πράξη, Γ. Χάλκος, ΔίΣιγμα Εκδόσεις, 2020 (book in Greek)
4. Design and Analysis of Experiments, Montgomery D., 6th edit., Wiley, 2004
5. University notes and exercises uploaded in e-class