Use this syntax to read in the data for this problem. $($If you get an error about the package not being

loaded, then uncomment the first line and rerun the code.$)$

Give the sample size: $n=$

Let's generate a plot of the data and overlay the best$-$fit line:

gf$\_$point $($Weight $$ WingLength, data$=$Sparrows$)|>$ gf$\_$lm$()$

Next, we'll generate the linear regression model:

$1m.$out $=1m($Weight $$ WingLength, data$=$Sparrows$)$

$1m.$out

Give the regression model parameter estimates for the line:

intercept: $b_0=$

slope: $b_1=$

Now we will calculate the confidence interval for the slope. This gives us the range in which we would

expect to find the true slope ${}_1.$ For a $95\%$ confidence interval, we will need information from these two

commands:

summary $(1m.$out$)$

$qt(0\mathrm{.}975,df=114)$

Give the lower and upper bounds for the $95\%$ confidence interval for the slope for this model:

To obtain predictions $($the fitted values$)$ at our data points, we can use the command:

fitted $(1m.$ out$)$

These predictions are also called conditional means $($the "average" values for the dependent variable

conditional on a given value for the independent variable$).$ If we want a predicted value $($conditional

mean$)$ at some specific input WingLength, say wingLength $=26\mathrm{.}2,$ we can make a function specifically designed

for such predictions.

weightPredictor $=$ makeFun $.$ out