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What model should I use for my data?
If the data lies on a straight line, or seems to lie approximately along a straight line, a linear model may be best. If the data is non-linear, we often consider an exponential or logarithmic model, though other models, such as quadratic models, may also be considered.
What are the different statistical models?
There are three main types of statistical models: parametric, nonparametric, and semiparametric:
- Parametric: a family of probability distributions that has a finite number of parameters.
- Nonparametric: models in which the number and nature of the parameters are flexible and not fixed in advance.
What kind of model do you want to be?
You can share it with your friends 🙂 What Kind of Model Should You Be? Being a model is about a lot more than just being pretty. Are you cut out for it?
Which is the best machine learning model to use?
I’ll tell you which machine learning model to use according to the nature of your problem, and I’ll try to explain some concepts. First, if you have a classification problem “which is predicting the class of a given input”.
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.
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.