15.8. Modeling with Regression http://www.ck12.org
15.8 Modeling with Regression
Here you will use regression on a variety of different types of data to make reasonable predictions.
Linear correlation is the simplest type of relationship between two variables. Your calculator has the power to use a
variety of different function families to find other relationships and create many different types of models. How do
you choose which function family is best for a given situation?
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URL: http://www.ck12.org/flx/render/embeddedobject/60152http://www.youtube.com/watch?v=CxEFOozrMSE Khan Academy: Linear, Quadratic, and Exponential Models
Guidance
Once you understand how to do linear regression with your calculator, you already know the technical mechanics to
perform other regressions in the [STAT] [CALC] menu. The most common regressions correspond to the function
families.
- QuadReg - Quadratic function family
- CubicReg - Cubic function family
- QuarticReg - Quartic function family or 4thdegree polynomial
- LnReg - Natural Log function family
- ExpReg - Exponential function family
- PwrReg - Power function family
- Logistic - Logistic function family
- SinReg - Sinusoidal function family.
When you perform these types of regressions, it will be incredibly important for you to interpret and explain parts
of the graph. Here are some points to keep in mind:
- They-intercept may have a particular meaning that may or may not be reasonable.
- When you use your model to make predictions it is important for you to remember the relevant domain of
your model. If your data is about elementary school students then it might extend to middle and high school
students, but it might not. - The calculator may produce a correlation coefficient for each of these non-linear regressions, but you should be
very careful. Technically, the correlation coefficient is only supposed to be calculated with linear regression,
so the calculator is doing some fancy linearization to produce it. You can learn more about this process in
future statistics courses.