Can you use statistical assessments in model specification?
You can use statistical assessments during the model specification process. Various metrics and algorithms can help you determine which independent variables to include in your regression equation. I review some standard approaches to model selection, but please click the links to read my more detailed posts about them.
What should be included in the model specification process?
For more information about this process, read 5 Steps for Conducting Scientific Studies with Statistical Analyses. Specification should not be based only on statistical measures. In fact, the foundation of your model selection process should depend largely on theoretical concerns.
How to choose the correct type of regression analysis?
There are numerous types of regression models that you can use. This choice often depends on the kind of data you have for the dependent variable and the type of model that provides the best fit. In this post, I cover the more common types of regression analyses and how to decide which one is right for your data.
What happens when you omit a variable from a regression model?
In fact, when you omit important variables from the model, the estimates for the variables that you include can be biased. This condition is known as omitted variable bias. If you can’t include a confounder, consider including a proxy variable to avoid this bias. The sample you collect can be unusual, either by luck or methodology.
Is the specification of a regression model based on statistics?
Specification should not be based only on statistical measures. In fact, the foundation of your model selection process should depend largely on theoretical concerns. Be sure to determine whether your statistical results match theory and, if necessary, make adjustments.
How does model selection depend on sample size?
In general, model selection depends on the nature of the data, the sample size, and the intended application of the results. The approach must be adapted to the problem at hand.
How is model selection based on inference and prediction?
Model Selection The final model selection relies on two separate but very closely knit concepts: inference and prediction. If the investigator is interested in building a prediction model, then the final model selection is based around the ideas of reducing the prediction error, classification error rate, or the deviance of the partial likelihood.