Add docs for BP test and Boot Residual linear regression.

docs/src/api/frequentist_regression.md

@@ -10,6 +10,18 @@ fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression)
 fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, bootstrap::Boot_Residual, sim_size::Int64 = 1000)
 ```
 
+### With bootstrap
+Fitting linear regression while estimating the standard error using bootstrap statistics.   
+```@docs
+Boot_Residual
+```
+
+### Breusch-Pagan Lagrange Multiplier test for heteroscedasticity
+Breusch-Pagan tests the homoscedasticity assumption of the residual variance in linear regression. 
+```@docs
+BPTest(container::FrequentistRegression, data::DataFrame)
+```
+
 ## Logistic Regression
 ```@docs
 fit(formula::FormulaTerm, data::DataFrame, modelClass::LogisticRegression, Link::Logit)

docs/src/man/guide.md

@@ -50,7 +50,7 @@ sigma(model)
 
 We can also get the predicted response of the model, along with other measures like the vector of Cook's distances using the [`predict`](@ref) and [`cooksdistance`](@ref) functions exported by CRRao. Here's a plot of the vector of Cook's distances.
 
-```@example ols_linear_regression
+```@repl ols_linear_regression
 plot(cooksdistance(model))
 ```