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If a departure from the
Assuming that the data are normally
independence assumption is
distributed, under the simple linear
1 2 detected, we transform the response
model, the estimated variance has
variable to improve upon the
the following sampling distribution:
independence assumption.
It is a good practice to create a
multiple linear regression model
3 4 The assumption of normality:
using linearly dependent set of
predictor variables.
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, Terms in this set (82)
Assuming that the data are normally Chi-squared with n-2 degrees of freedom.
distributed, under the simple linear
model, the estimated variance has
the following sampling distribution:
The fitted values are defined as? The regression line with parameters replaced with
the estimated regression coefficients.
The estimators fo the linear Minimizing the sum of squared differences
regression model are derived by? between the observed and expected values of the
response variable.
The estimators for the regression Unbiased regardless of the distribution of the data.
coefficients are:
The assumption of normality: Is needed for the sampling distribution of the
estimators of the regression coefficients and hence
for inference.
The estimated versus predicted have the same expectation.
regression line for a given x*
The variability in the prediction the variability due to a new measurement and due
comes from to estimation.
Residual analysis can only be used to False
assess uncorrelated errors.
Independence assumption can be False
assess using the normal probability
plot.