Foundations of OR: Statistical Methods in OR: Descriptive Stats, Sampling and Regression
A practical course applying sampling and regression analysis to ‘real world’ case study data and interpreting the results. Learn when and how to use these techniques to address ‘real world’ problems such as quality control.
Description
- Enhance your analytical skills using sampling and regression statistical techniques.
- Improve your data analysis with a better understanding of how to interpret the results of applying statistics to ‘real world’ data.
- Increase your confidence in using the Normal Distribution to describe a dataset, and to interpret sample results.
- Build your knowledge towards more advanced statistical methods.
- Understand when and how to utilise these techniques with other analytical methods such as forecasting and simulation.
Topics
- Normal Distribution sampling
- Correlation analysis
- Regression analysis
Related courses
- Foundations of OR: Statistical Methods in OR: Multivariate Models
- Foundations of OR: Statistical Methods in OR: Forecasting
- Foundations of OR: Simulation
Learning objectives
- Apply the Normal Distribution to ‘real world’ problem situations and interpret the results.
- Describe some of the key characteristics of a dataset that may affect the results.
- Apply correlation and regression analysis to ‘real world’ problem situations and interpret the results.
- Address common questions that arise in practice when working with sample data.
- Understand the assumptions behind basic sampling theory.
- Learn when to use these different statistical techniques.
Course format
- Powerpoint presentation to introduce the techniques
- Case studies based on ‘real world’ problems
- Use the analysis tool pack in Excel to apply the techniques
- Group discussion/work to interpret the results
- Bring questions from your own work to embed your learning
- Supporting resource pack available to use following the course
Audience
Ideal for anyone starting their career in operational research or wanting to improve their confidence in working with statistics.