Least Square Elimination (LSE) cannot be applied to GLM models.
False - it is applicable but does not use data distribution information fully.
In multiple linear regression with idd and equal variance, the least squares
estimation of regression coefficients are always unbiased.
True - the least squares estimates are BLUE (Best Linear Unbiased Estimates) in
multiple linear regression.
Maximum Likelihood Estimation is not applicable for simple linear regression and
multiple linear regression.
False - In SLR and MLR, the SLE and MLE are the same with normal idd data.
The backward elimination requires a pre-set probability of type II error
False - Type I error
The first degree of freedom in the F distribution for any of the three procedures in
stepwise is always equal to one.
True
MLE is used for the GLMs for handling complicated link function modeling in the X-Y
relationship.
True
In the GLMs the link function cannot be a non linear regression.
False - It can be linear, non linear, or parametric
When the p-value of the slope estimate in the SLR is small the r-squared becomes
smaller too.
False - When P value is small, the model fits become more significant and R
squared become larger.
In GLMs the main reason one does not use LSE to estimate model parameters is the
potential constrained in the parameters.
False - The potential constraint in the parameters of GLMs is handled by the link
function.
The R-squared and adjusted R-squared are not appropriate model comparisons for
non linear regression but are for linear regression models.
TRUE - The underlying assumption of R-squared calculations is that you are fitting a
linear model.
The decision in using ANOVA table for testing whether a model is significant
depends on the normal distribution of the response variable
True
When the data may not be normally distributed, AIC is more appropriate for variable
selection than adjusted R-squared
True
The slope of a linear regression equation is an example of a correlation coefficient.
False - the correlation coefficient is the r value. Will have the same + or - sign as the
slope.
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