LaboratoryQuantitative Analysis and Statistical Methods
This course gives delegates a detailed and current grounding in quantitative analysis, from the underlying mathematical and statistical concepts through to the modelling techniques used to draw defensible conclusions from data. It covers the difference between qualitative and quantitative analysis, types of data and measures of central tendency, data collection and sampling methods, graphical representation, probability concepts and distributions, and hypothesis testing including population parameters and margin of error. Delegates work through paired and unpaired t tests, one way and two way ANOVA, simple and multiple linear regression with their assumptions and diagnostics, correlation, time series and non linear models. The programme closes on supervised and unsupervised learning, factor and cluster analysis, and how these methods are applied in financial analysis, marketing analytics and operational research. Delivery is roughly 30 percent lectures, 20 percent workshops and delegate presentations, 30 percent hands on exercises and case studies, and 20 percent software and video.
Course objectives
- Differentiate qualitative from quantitative analysis and apply the underlying mathematical and statistical concepts
- Carry out data collection and sampling, and represent results graphically
- Explain basic probability concepts and probability distributions
- Apply null and alternative hypothesis testing and estimate population parameters and margin of error
- Select and interpret paired and unpaired t tests, including their assumptions
- Apply one way and two way ANOVA and interpret the F statistic and p values
- Build simple and multiple linear regression models, run diagnostic tests and handle multicollinearity
- Apply correlation analysis, time series analysis and non linear regression models
- Differentiate supervised from unsupervised learning and identify common algorithms and quantitative software
- Apply factor and cluster analysis in financial analysis, marketing analytics and operational research
Who should attend
- Analytical chemists, laboratory technicians and laboratory scientists
- Pharmaceutical scientists, biotechnologists and forensic scientists
- Pathologists, clinical laboratory scientists and medical researchers
- Researchers, data scientists and data analysts
- Business, finance and policy analysts
- Marketing and market research professionals
Course outline
- 01Qualitative versus quantitative analysis and the underlying mathematical and statistical concepts
- 02Types of data, measures of central tendency and graphical representation
- 03Data collection techniques and sampling methods
- 04Basic probability concepts and probability distributions
- 05Null and alternative hypothesis testing, population parameters and margin of error
- 06Paired and unpaired t tests, their assumptions and interpretation
- 07One way and two way ANOVA, the F statistic and p values
- 08Simple linear regression: the regression equation, coefficients, assumptions and diagnostic tests
- 09Multiple linear regression: adjusted R squared, model fit and handling multicollinearity
- 10Correlation analysis, time series analysis and non linear regression models
- 11Supervised and unsupervised learning, common algorithms and quantitative software
- 12Factor analysis, cluster analysis, financial quantitative analysis, marketing analytics and operational research
Scheduled sessions
| Dates | Venue | Format | Price | Register |
|---|---|---|---|---|
| 6 to 7 November 2026 | Gaborone, Botswana | Classroom | R11,995 per delegate | Register Now |
| 6 to 7 February 2027 | Dar es Salaam, Tanzania | Classroom | R11,995 per delegate | Register Now |
| 18 to 19 March 2027 | Durban, South Africa | Classroom | R11,995 per delegate | Register Now |
| 19 to 20 April 2027 | Dubai, UAE | Classroom | R11,995 per delegate | Register Now |