Back to: Statistics
- Regression Techniques
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Regression is a powerful technique that will enhance statistical thinking and appreciation.
An appropriate understanding of regression ensures that it is correctly used. The purpose of analysis; the types of variables under investigation; the statistical assumptions of the variables; and the manner in which the data is collected, will all need to be considered throughout the course.
Who is it for?
- The course is designed for those who have a working knowledge of basic descriptive statistics (mean, median, standard deviation); hypothesis testing and p-values
- Knowledge of particular statistical software is assumed
- Delegates who have attended the Introductory Course in Data Management will have the necessary prerequisites
What you wil learn:
- Basic fundamentals of regression, including simple linear regression, multiple line regression and non-linear regression
Benefits of attending:
- Understand basic regression techniqes
- Use the different models - simple linear regression, multiple linear regression and non linear regression
- Verify the models with ANOVA
- Use statistical packages to apply the models to data and interpret the output