Reference no: EM133218010

Question: If you view this in a spreadsheet, you will find four columns of a small dataset. The first column shows the number of fawn in a given spring (fawn are baby antelope). The second column shows the population of adult antelope, the third shows the annual precipitation that year, and finally the last column shows how bad the winter was during that year.

You have the option of saving the file to your computer and read it into R, or read the data directly from the web into a dataframe.
You should inspect the data using the str() command to make sure all of the cases have been read in (n=8 years of observations) and that there are four variables.

Create bivariate plots of number of baby fawns versus adult antelope population, precipitation that year, and severity of the winter. Your code should produce three separate plots. Make sure the y-axis and x-axis are labeled. Keeping in mind that the number of fawns is the outcome (or dependent) variable, which axis should it go on in your plots?

Next, create three regression models of increasing complexity using lm(). In the first model, predict the number of fawns from the severity of the winter. In the second model, predict the number of fawns from two variables (one should be the severity of the winter). In the third model, predict the number of fawns from the three other variables. Which model works best? Which of the predictors are statistically significant in each model? If you wanted to create the most parsimonious model (i.e., the one that did the best job with the fewest predictors), what would it contain?