- Building a regression model with stats packages has become straightforward.
- However, interpreting the software output and building a good model are no simple tasks.
- Learn the essentials of model-building, goodness-of-fit tools, model validation & common pitfalls.
Simple Linear Regression (SLR)
- Objectives
- Terminology
- What is a model and specification?
- Principle of least squares estimation
- Interpretation of model coefficients
- Difference between correlation and regression
- Statistical testing of model coefficients: intercept and slope
- Condition of use and diagnostic tools
- Prediction in regression analysis
- Extrapolation: Use & Piftalls
- Objectives
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- Aspects common to SLR
- Interpretation of model coefficients
- Model-building steps
- Good and not so good measures of model performance
- Checking model adequacy
- Specific issues in MLR: Variable selection, multicollinearity and use of special terms
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- Nonlinear Regression (NLR)
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- Applications of nonlinear regression
- Overview of other ways to handle multicollinearity
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- Steps in model construction
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- Robustness of regression to deviations from conditions of application
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- Participants should know the essential tools in statistics - descriptive statistics, both numerical and graphical, as well as hypothesis testing and confidence intervals.
- Potential participants should either have attended the training session Fundamental Tools in Statistics or should possess a similar background.
Upon completion of this module, participants will be able to:
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- Understand the context of use of simple and multiple linear regression
- Construct simple/multiple regression models
- Assess the goodness-of-fit of the model to the data
- Identify common issues in regression, diagnose problems and fix them
- Interpret statistical software output
Recommended Duration: 1 day(s)
Course Materials:
TBD
Case Studies:
N/A