Strayer University Promotion and Pricing Strategies Discussion

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STAT 4620/5620
Assignment 2: Due Monday February 3 2020
1. Describe the estimators for linear regression that are implemented in the
lmRob function (in R). Contrast these with what is implemented by the lm
function. Explain how to proceed with data analysis when you have good
reason to believe that a linear model is reasonable for your data but that
there may well be data recording errors.
2. Explain how the Akaike information criterion (AIC) is computed for a linear
model as well as how it is commonly utilized for model (or variable) selection
purposes.
3. Consider the negative binomial distribution.
(a) Write down its density function.
(b) Plot the density curves from a negative binomial distribution for a range
of values of it’s two parameters.
(c) What distribution do you arrive at if the dispersion parameter is taken
to be 1?
(d) How is the dispersion parameter estimated in the function glm.nb?
4. Residual checking for GLMs is not always as straightforward as for linear
models, and the problems are particularly acute in the case of binary responses. This question explores this issue.
(a) The following code fits a GLM to data simulated from a simple binomial
model and examines the default residual plots.
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