Imagine asking a home buyer to describe their dream house, they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. However, this playground competition's dataset proves there are more that influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Using the "swiss" dataset, conduct a regression of two variables of interest. Interpret the assumptions and results. Post your solutions and R code here.

Complete the following questions in R Markdown and submit as a single .html file (knitted.)

The R datasets library contains data on air quality in New York (airquality). Conduct a hypothesis test to evaluate if ozone levels are a function of month. NOTE: dichotomize month. If that test were significant, what else would be required? Post your hypothesis test and R code with your discussion.

The "swiss" dataset in library (datasets) contains information about socioeconomic indicators and fertility. Provide descriptive statistics and confidence intervals for appropriate variables. Interpret. Submit your R code.

Often, we can model processes using several different probability distributions. For example, we might use the Poisson instead of the binomial, the binomial instead of the geometric, or the normal approximation instead of the binomial. If the assumptions are understood, then the probability results will be nearly identical. Let's assume that a hospital's neurosurgical team performed 30 procedures for in-brain bleeding last year. Five of these procedures resulted in death within 30 days. If the national proportion for death in these cases is .05, then is there evidence to suggest that your hospital's proportion of deaths is more extreme than the national proportion? Model both as a binomial and a Poisson, and provide your R code solutions.