| 1. | OLS can handle non-linear relationships by introducing the regressor HEIGHT 2.
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| 2. | For instance, the third regressor may be the square of the second regressor.
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| 3. | For instance, the third regressor may be the square of the second regressor.
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| 4. | If it holds then the regressor variables are called " exogenous ".
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| 5. | The constant term in all regression equations is a coefficient multiplied by a regressor equal to one.
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| 6. | This sum is thus equal to the constant term's regressor, the first vector of ones.
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| 7. | The coefficient " ? " 1 corresponding to this regressor is called the " intercept ".
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| 8. | In this case ( assuming that the first regressor is constant ) we have a quadratic model in the second regressor.
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| 9. | In this case ( assuming that the first regressor is constant ) we have a quadratic model in the second regressor.
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| 10. | With more than one regressor, the " R " 2 can be referred to as the coefficient of multiple determination.
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