What are the most important things to consider when identifying a model to fit a set...

What are the most important things to consider when identifying a model to fit a set of data? Be sure to list as many characteristics as you can, but don't be afraid to share any thoughts, ideas, or uncertainties you may have about a particular model. If you're unsure about something, just ask some clarifying questions to help you get a better idea of what the model's key features are. Your post must have a minimum of 5 sentences.

Expert Solution

1. Visually compare the graph of the data to the graph of the model. (Look for a pattern from the graph.)

2. Calculate a correlation coefficient, r (for some models).
The correlation coefficient measures the strength and the direction of a linear relationship between two variables. A value of | r | near one may indicate a "good fit".

3. Calculate a coefficient of determination, r² (R²).
The coefficient of determination represents the percent of the data that is the closest to the line of best fit.

4. Examine the residuals.
Examine the scatter plot of the residuals, which depicts the measure of the signed distances between the actual data values and the outputs predicted by the model. A good linear model has residuals that are near zero and are randomly distributed.

5. Think about your answer.
Is your choice realistic? Don't use a model that will lead to predicted values that are totally unrealistic.

milcah answered 3 years ago

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