Quantitative Methods

Quantitative Methods

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

Course title

Quantitative Methods

Lecture type




Lecturers and Associates

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.


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.


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

  • Evaluate basic concepts of quantitative modeling, data collection, measurements and scaling in terms of their applicability and usefulness in business analysis.
  • Choose, measure and interpret basic measures of central tendency and dispersion for historical data.
  • Choose, measure and interpret basic aspects of correlation and regression analysis.
  • Evaluate and measure the trade-offs between two or more alternatives relative to their cost and/or profitability.
  • Choose and interpret basic aspects the mathematical programming models.

Preferred learning outcomes

  • Choose model types and general modeling techniques for business problems. Choose adequate measuring scales and sampling techniques.
  • Critically interpret relevant measures of descriptive statistics. Model historical data using PivotTables.
  • Critically interpret moving average, exponential smoothing, correlation and regression analyses, parameter estimation, standardized coefficients, significance, and predictability of the future.
  • Model, and interpret the break-even point(s) and trade-offs between alternatives.
  • Model, solve and interpret enhanced versions of the transportation model, including capacity limited, fixed costs, other constraints.