diff --git a/docs/src/basics/plot.md b/docs/src/basics/plot.md
index 0a6a87297f..6016e14220 100644
--- a/docs/src/basics/plot.md
+++ b/docs/src/basics/plot.md
@@ -3,7 +3,7 @@
 ## Standard Plots Using the Plot Recipe
 
 Plotting functionality is provided by recipes to Plots.jl. To
-use plot solutions, simply call the `plot(type)` after importing Plots.jl
+plot solutions, simply call the `plot(type)` after importing Plots.jl
 and the plotter will generate appropriate plots.
 
 ```julia
@@ -15,7 +15,7 @@ plot(sol) # Plots the solution
 Many of the types defined in the DiffEq universe, such as
 `ODESolution`, `ConvergenceSimulation` `WorkPrecision`, etc. have plot recipes
 to handle the default plotting behavior. Plots can be customized using
-[all of the keyword arguments provided by Plots.jl](https://juliaplots.github.io/supported/).
+[all of the keyword arguments provided by Plots.jl](http://docs.juliaplots.org/latest/supported/).
 For example, we can change the plotting backend to the GR package and put a title
 on the plot by doing:
 
@@ -192,7 +192,7 @@ on the available attributes.
 
 ## Plotting Without the Plot Recipe
 
-What if you don't want to use Plots.jl? Odd choice, but that's okay! If differential
+What if you don't want to use Plots.jl? Odd choice, but that's okay! If the differential
 equation was described by a vector of values, then the solution object acts as
 an `AbstractMatrix` `sol[i,j]` for the `i`th variable at timepoint `j`. You can
 use this to plot solutions. For example, in PyPlot, Gadfly, GR, etc., you can
diff --git a/docs/src/features/monte_carlo.md b/docs/src/features/monte_carlo.md
index 8777fc6d7e..df8c373db6 100644
--- a/docs/src/features/monte_carlo.md
+++ b/docs/src/features/monte_carlo.md
@@ -38,10 +38,9 @@ end
 ```
 
 modifies the initial condition for all of the problems by a standard normal
-random number (a different random number per simulation). This can be used
-to perform searches over initial values. Note that the parameter `i` is a unique
-counter over the simulations. Thus if you have an array of initial conditions `u0_arr`,
-you can have the `i`th simulation use the `i`th initial condition via:
+random number (a different random number per simulation). Notice that since
+problem types are immutable, it uses `.=`. Otherwise, one can just create
+a new problem type:
 
 ```julia
 function prob_func(prob,i,repeat)