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# Quantitative Methods

## Quantitative Methods

• Class 40
• Practice 10
• Independent work 130
Total 180

### Course title

Quantitative Methods

Obligatory

6

### The course aims

Train students for independent application of quantitative analysis and modeling by acquiring theoretical and practical knowledge of quantitative methods. Students will learn to identify certain types of problems and choose appropriate methods of analysis and modeling. Through examples and exercises, students will develop the art of modeling of real business problems.

### Content

Lecture topics:
L1: Introduction to quantitative methods.
L2: Model types.
L3: Modeling process.
L4: Defining problem, objective and scope of research.
L5: Collecting data: primary and secondary sources.
L6: Measuring and scaling (exercise).
L7: Primary measurement scales.
L8: Comparative scaling.
L9: Specific scales.
L10: Design of questionnaires (exercise).
L11: Relationships between variables.
L12: Sampling techniques.
L13: Distributions and confidence intervals.
L14: Descriptive statistics. Measure of averages, dispersion and roundness.
L15: Non-parametric tests. Chi-square test. K-S. Wilcoxon.
L16: Correlation. Scatter diagram. Correlation coefficient. Introduction to regression.
L17: Linear regression.
L18: Multiple regressions. Model quality analysis.
L19: Factor analysis.
L20: Clustering.

Topics for seminar classes:
S1: Basics of modeling.
S2: Impact diagrams.
S3: Decision trees.
S4: Marketing problem modeling.
S5: Statistical tools.
S6: Sampling in R.
S7: Sampling.
S8: Descriptive statistics.
S9: Non-parametric tests.
S10: Correlation analysis.
S11: Linear regression.
S12: Factor analysis.
S13: Probability and probability distribution.
S14: Decision-making under uncertainty.
S15: Time series analysis and forecasting.
S16: Assessment of confidence intervals.
S17: Hypothesis testing.
S18: Sensitivity analysis.
S19: Simulation models.
S20: Normal and binomial distribution.

### Literature

Jacobs, F. Robert (2019). Quantitative Methods (custom made)

### Supplementary literature

Albright, S. Ch., Winston, W. (2015). Business Analytics: Data Analysis and Decision Making, 5th Edition. CENGAGE Learning.
Jacobs, F. Robert, Chase, Richard (2017). Operations and Supply Chain Management, 15th Edition. McGraw-Hill Education.

#### Minimum learning outcomes

• Vrednovati osnovne koncepte kvantitativnog modeliranja, prikupljanja podataka, mjernih skala te uzorkovanja u smislu primjenjivosti i korisnosti u poslovnim analizama.
• Izabrati, izmjeriti i interpretirati osnovne mjere centralne tendencije i disperzije povijesnih podataka.
• Izabrati, izmjeriti i interpretirati osnovne aspekte korelacijske i regresijske analize.
• Procijeniti i mjeriti izbore između dviju ili više alternativa s obzirom na njihove troškove i/ili profitabilnosti.
• Izabrati i interpretirati osnovne aspekte matematičkih programskih modela.

#### Preferred learning outcomes

• Izabrati vrste modela, opće tehnike modeliranja i adekvatan skup statističkih alata za analizu zadanih problema u poslovanju.
• Kritički interpretirati odgovarajuće mjere deskriptivne statistike. Modelirati povijesne podatke korištenjem pivot-tabela.
• Kritički interpretirati korelacijske i regresijske analize, procjene parametara, standardiziranih koeficijenata, signifikantnosti, prediktivnosti budućeg.
• Modelirati i interpretirati točke pokrića i izbora između alternativa.
• Modelirati, rješavati i interpretirati napredne verzije transportnih modela, uključujući ograničene kapacitete, fiksne troškove i druga ograničenja.