regressor

Unlike Table 4, for example, age is not a significant regressor.

The most important regressor on this page is clearly earnings at the first interview.

This term was then included as an additional regressor in the second equation of the model.

If it holds then the regressor variables are called exogenous.

The second stage involved estimating equation (1) using as the regressor in addition to X1.

With more than one regressor, the R can be referred to as the coefficient of multiple determination.

This sum is thus equal to the constant term's regressor, the first vector of ones.

What relationship does each regressor have with the dependent variable when all other regressors are held constant?

You can see that R2adj will always be lower than R2 if there is more than one regressor.

The coefficient β corresponding to this regressor is called the intercept.

Mean-independence: the errors are mean-zero for every value of the latent regressor.

Note that the regressor in the auxiliary regression is generated with the command:

For instance, the third regressor may be the square of the second regressor.

The constant term in all regression equations is a coefficient multiplied by a regressor equal to one.

The resulting estimator can be expressed by a simple formula, especially in the case of a single regressor on the right-hand side.

Berkson's errors: the errors are independent from the observed regressor x.

Regressor types seemed to get everywhere.

For example, the two-regressor model with a distributed lag effect for one regressor is written:

OLS can handle non-linear relationships by introducing the regressor .

In addition to the regressors outlined above, consider a case where one lag of the dependent variable is included as a regressor, .

It is well-known that centering of the observed regressor values improves the conditioning of a regression problem.

To date, methods of deriving site index (S) equations assume that stochastic error is only present in the regressor.

Model regressor variables.

If the data matrix X contains only two variables, a constant and a scalar regressor x, then this is called the "simple regression model".

In other words, the value y(i) is the vector inner product of the regressor vector and the parameter vector.