Assignment #2 - Assumptions of regression - 2019

For each of the following four datasets, import the data into R, analyze the data using regression. Then provide the following information in a Microsoft Word Document

  1. Using the statement I described in class, describe the relationship between X and Y - the same statement you did for assignment 1 (be sure to include the p-value). Describe the relationship even if it isn't 'significant' (you fail to reject the null), but be sure to state that the relationship isn't significant if that's the case
  2. What assumption of regression is violated with this data-set? Cut and paste the graph that visually informs you that an assumption has been violated. (Note that only one assumption has truly been violated; if more than one assumption appears to be violated, choose the one that appears to be the strongest violation).

DataSet #1 - Relationship between prey density (X; prey/ha) and predation rate (Y; prey killed per predator per day)

DataSet #2 - Relationship between tree basal area (Y; m2) and stand age (X; years)

DataSet #3 - Relationship between prey density (X; prey/ha) and coyote litter size (Y; pups); Note that litter size is not continuous, but that's not the assumption that has been violated.

DataSet #4 - Relationship between road density (X; roads/km2) and probability of habitat use by wolves (Y; %)

For this final dataset, describe the relationship between X and Y using the standard statements. Also, report what the expected number of farmers kill by tigers is at 20.0 prey/ha with prediction intervals. Finally, generate a graph of the y predictions and the prediction intervals like I showed in class.

DataSet #5 - Relationship between prey density (X; prey/ha) and number of farmers killed by tigers (Y; person/km2).

Truth for Datasets #1-4 - Just in case you're interested in how the data was created