1) Obtaining data samples for a multiple regression problem from the dataset "PSTAT 126 Final Dataset 1" (which will be sent in an email with that subject line), utilize any or all of the various regression model-fit assessment tools available in R that we have discussed in the course to help identify and validate a linear regression model that appears to best fit the given data. These will for example likely include, but may not be limited to, visual plots and/or graphs (for example, residual vs. fitted value plots), results of statistical hypothesis tests, numerical regression model accuracy measures, and linear model summary output reports, as well as any other applicable diagnostic tools that we have considered in the course. You should use the solutions to Problems 1 and 5 in Homework #3 as a general guide as to what types of information your answer could or should contain (and how to go about answering this question), but there may have been course material introduced after Homework #3 was assigned that may also be relevant here. You will likely need to try a number of different variable transformations on the response variable and/or predictor variables, using the diagnostic tools to decide which of the resulting regression models appear consistent with the data and which do not. You should clearly identify one or perhaps two candidate regression models that you believe are most consistent with the given data. Please try to include screenshots of the graphical plots you use as well as quote any relevant R code output results. You can or even should include the R code itself as well if you feel it helps to support your argument. Please explain your reasoning as to why the model(s) you propose may be the right one(s) in plain, natural language; you do not necessarily have to identify the "right" model to get a great deal of partial credit or even perhaps full credit