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Peter Flom
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Linear regression can accommodate non-straight-line relationships between IVs and the DV through various transformations of variables, addition of polynomial terms and so on.

That is a model like

$y = b_0 + b_1x_1^2 + b_2x_1 + b_3x_3^5$

is a linear model. But a model such as

$y = b_0 + b_12^{x_1}$$y = b_0 + 2^{b_1x_1}$

is not.

If the data are really nonlinear, then the choice of model depends partly on what you know about the relationships. If you don't know much, a spline regression may work well.

Linear regression can accommodate non-straight-line relationships between IVs and the DV through various transformations of variables, addition of polynomial terms and so on.

That is a model like

$y = b_0 + b_1x_1^2 + b_2x_1 + b_3x_3^5$

is a linear model. But a model such as

$y = b_0 + b_12^{x_1}$

is not.

If the data are really nonlinear, then the choice of model depends partly on what you know about the relationships. If you don't know much, a spline regression may work well.

Linear regression can accommodate non-straight-line relationships between IVs and the DV through various transformations of variables, addition of polynomial terms and so on.

That is a model like

$y = b_0 + b_1x_1^2 + b_2x_1 + b_3x_3^5$

is a linear model. But a model such as

$y = b_0 + 2^{b_1x_1}$

is not.

If the data are really nonlinear, then the choice of model depends partly on what you know about the relationships. If you don't know much, a spline regression may work well.

Source Link
Peter Flom
  • 128.3k
  • 36
  • 184
  • 424

Linear regression can accommodate non-straight-line relationships between IVs and the DV through various transformations of variables, addition of polynomial terms and so on.

That is a model like

$y = b_0 + b_1x_1^2 + b_2x_1 + b_3x_3^5$

is a linear model. But a model such as

$y = b_0 + b_12^{x_1}$

is not.

If the data are really nonlinear, then the choice of model depends partly on what you know about the relationships. If you don't know much, a spline regression may work well.