Use the data set of measurements of antelope astragali from Barr (2014).

- Read in the data, saving it to a variable called
`astrag`

. - Make a scatterplot with the natural logarithm of measurement
`B`

on the x-axis and the natural logarithm of measurement`DistRad`

on the y-axis. - Calculate the ordinary least-squares (OLS) regression for
`log(DistRad)`

as a function of`log(B)`

, and print a`summary()`

of this data. - Calculate the reduced major axis regression (RMA) for the same variables.
**Note**the`lmodel2`

package uses “RMA”" to refer to “Ranged Major Axis”, which is different. You want what`lmodel2`

calls Major Axis (MA), which is equivalent to what we call reduced major axis (RMA) in our literature. - Is the OLS slope greater or less than the RMA slope?
- Add both the OLS and the RMA regression lines to the plot created in part B. Make sure the two lines have different colors and/or line types, and that they are labeled so I know which is which.
- use the
`plot()`

function to plot regression diagnostic plots for the OLS regression model. Do the assumptions of linear regression appear to be met? Explain your answer in complete sentences. - Extract the residuals from the OLS regression, and make a histogram of them, with the appropriate labels and title for the plot.
- Calculate the species means for both variables
`B`

and`DistRad`

, and add the species means as a new layer on the scatterplot created in part B.