An ordinary regression model that treats the response Y as normally distributed is a special case of a GLM, with normal random component and identity link function. 1. With a GLM. Y does not need to have a normal distribution and one can model a function of the mean of Y instead of just the mean itself, but In order to get the maximum likelihood estimates the variance of Y must be constant at all values of predictors. ii. The Pearson residual e_inty_i-muhatiysqrt(muhat_) for a GM has a large-sample standard normal distribution (a)) True False, (w) True; (b)) True 00 True True (co False 0 false, Oll) False; IdFalse 00 True (1) False;

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16-12-2022
Mathematics

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An ordinary regression model that treats the response Y as normally distributed is a special case of a GLM, with normal random component and identity link function. 1. With a GLM. Y does not need to have a normal distribution and one can model a function of the mean of Y instead of just the mean itself, but In order to get the maximum likelihood estimates the variance of Y must be constant at all values of predictors. ii. The Pearson residual e_inty_i-muhatiysqrt(muhat_) for a GM has a large-sample standard normal distribution (a)) True False, (w) True; (b)) True 00 True True (co False 0 false, Oll) False; IdFalse 00 True (1) False;

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debjitbhowal4568 debjitbhowal4568 · Answer 1 · 2022-12-20T10:31:22-05:00

An ordinary Regression model that treats the response Y is (a) True False, (w) True

What is Regression?

A statistical method called regression links a dependent variable to one or more independent (explanatory) variables.

A regression model can demonstrate whether changes in one or more of the explanatory variables are related to changes in the dependent variable.

A) Models for numerical response variable, like ANOVA and linear regression are special cases of GLMs

for these model the following holds

1. Random component has a normal distribution

2. Systematic component α+β₁x₁+β₂x₂+...........βₓxₓ

3. link function = identity (g(µ)=µ)

GLMs can generalise these models with response Y as normally distributed, hence the statement is True

B) With a GLM. Y does not need to have a normal distribution and one can model a function of the mean of Y instead of just the mean itself. but in order to get ML estimates the variance of Y must be small. This small variance of Y is the reason for ML estimator to be the best one. hence the statement is false.

An ordinary Regression model that treats the response Y is (a) True False, (w) True

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