Can we solve a problem using regression as well classification? Justify with an example.

1.) When and Why do we use Proportional model, Binomial model, and Poisson regression model? 2.) How and why are degrees od freedom infleunced by each of these models?

4. Source: Kleinbaum, Kupper, Miller, and Nizam. Applied Regression Analysis and Other Multivariable Methods, Third Edition. Pacific Grove: Duxbury Press, 1998. p 683 (problem 2). A five year follow-up study on 600 disease free subjects was carried out to assess the effect of 0/1 exposure E on the development (or not) of a certain disease. The variables AGE (continuous) and obesity status (OBS), the latter a 0/1 variable were determined at the start of the follow-up and were to be considered as control variables in analyzing the data. (A) State the logit form of a logistic regression model that assesses the effect of the 0/1 exposure variable E controlling for the confounding effects of AGE and OBS and the interaction effects of AGE with E and OBS with E.

Question 3. Regression need answer of part b Consider real-valued variables X and Y. The Y variable is generated, conditional on X, from the fol- lowing process: E~N(0,0²) YaX+e where every e is an independent variable, called a noise term, which is drawn from a Gaussian distri- bution with mean 0, and standard deviation σ. This is a one-feature linear regression model, where a is the only weight parameter. The conditional probability of Y has distribution p(YX, a) ~ N(aX, 0²), so it can be written as p(YX,a) = exp(- (-202 (Y-ax)²) 1 ν2πσ The following questions are all about this model. MLE estimation (a) Assume we have a training dataset of n pairs (X, Y) for i = 1..n, and σ is known. Which ones of the following equations correctly represent the maximum likelihood problem for estimating a? Say yes or no to each one. More than one of them should have the answer "yes." a 1 [Solution: no] arg max > 2πσ 1 [Solution: yes] arg max II a [Solution: no] arg max a [Solution: yes] arg max a 1…