# Computing

## Statistical Data Analysis

- Class 45
- Practice 12
- Independent work 93

### Course title

Statistical Data Analysis

### Lecture type

Elective

### Course code

183454

### Semester

5

### ECTS

5

### Lecturers and associates

- Full Professor PhD Bojana Dalbelo Bašić
- Associate Professor PhD Zvonko Kostanjčar
- Associate Professor PhD Jan Šnajder
- Associate Professor PhD Igor Velčić

### Course objectives

Presentation of statistical data; population and sample; sampling methods; population parameters.

Objective of multivariate statistical analysis; Data, objects, variables and scales (Stevens's classification); Classification of multivariate techniques; Summarizing, describing and graphical representation of multivariate data.

Measures of central tendency (mean, median, mode); measures of dispersion (standard deviation, variance, quantile, and IQR); Statistical inferences; correlation coefficient; linear correlation.

Hypothesis testing; type of errors; parametric hypothesis testing; sampling distributions; Tests for means (one and two sample tests - matched pairs and independent); t-test.

Tests for variances (one and two sample tests); Chi-square test; F-test.

Difference of means; variance of two normal populations; comparing of two population proportions; Tests for population proportions (one and two sample tests); U-test.

Interpretation of the tests results; Sample size; Interpretation of p-value; Example of applications: statistical quality control and control charts.

Midterm exam.

Advantage, disadvantage and use of nonparametric statistical procedures; Nonparametric tests for single sample; Nonparametric tests for two independent and two related samples; Nonparametric tests for three and more independent and related samples.

Data manipulation prior to multivariate analysis (missing data, outlier detection, transformations of data, standardization, normality, linearity, homoscedascity, homoegenity); Data appropriate for multivariate analysis: data, correlation, variance-covariance, sum-of-squares and cross-products matices, residuals; distances (statistical and Mahalanobis); Sample geometry and Random sampling.

Analysis of variance (ANOVA/MANOVA) and design of experiment.

Simple linear regression; Multiple linear regression.

Applied correlation and regression analysis, interpretation and relation to ANOVA.

Bayesian versus Frequentist inference.

Final exam.

### Required reading

(.), Željko Pauše: Uvod u matematičku statistiku, Školska knjiga, 1992,

(.), Mirta Benšić, Nenad Šuvak: Primijenjena statistika, Sveučilište J. J. Štrosmajera, 2013,

(.), 1) David M. Diez, Crhisopher D. Barr, Mine CerinkayaRundel: OpenIntro Statistics, OpenIntro, 2015.,

(.), 2) L. Fahrmeir, T. Kneib, S. Lang, B. Marx: Regression: Models, Methods and Applications, Springer, 2013.,

(.), 3) G. James, Daniela Witten,Tre vor Hastie, Robert Tibshirani: An Introduction to Statistical Learning with Applications in R, Springer, 2013.,

#### Online education during epidemiological measures

- Study program duration
- 6 semesters (3 years)
- Semester duration
- 15 weeks of active teaching + 5 examination weeks
- Total number of ECTS points
- 180
- Title
- Bacc.ing.comp (Bachelor of Science in Computing)

**Academic calendar**

#### Minimal learning outcomes

- Define main notions in the statistical data analysis
- Explain mathematical backgrounds of main statistical procedures
- Apply procedure of data preparation and visualization
- Apply statistical test on real data
- Analyze the relation between statistical variables by applying regression analysis and correltion analyis
- Justify the adequacy of statistical inference for given data
- Interpret the results of statistical data analysis and explain their practical meaning