Small documentation fixes

docs/src/basics/SampledIntegralProblem.md

@@ -9,7 +9,7 @@ for doing that.
 
 Say, by some means we have generated a dataset `x` and `y`:
 
-```example 1
+```@example 1
 using Integrals # hide
 f = x -> x^2
 x = range(0, 1, length=20)
@@ -18,7 +18,7 @@ y = f.(x)
 
 Now, we can integrate this data set as follows:
 
-```example 1
+```@example 1
 problem = SampledIntegralProblem(y, x)
 method = TrapezoidalRule()
 solve(problem, method)
@@ -37,7 +37,7 @@ non-uniform grids.
 
 Example:
 
-```example 2
+```@example 2
 using Integrals # hide
 f = x -> x^7
 x = [0.0; sort(rand(1000)); 1.0]
@@ -53,7 +53,7 @@ If the provided data set `y` is a multidimensional array, the integrals are eval
 of its axes. For performance reasons, the last axis of the array `y` is chosen by default, but this can be modified with the `dim`
 keyword argument to the problem definition.
 
-```example 3
+```@example 3
 using Integrals # hide
 f1 = x -> x^2
 f2 = x -> x^3

src/algorithms.jl

@@ -141,7 +141,7 @@ f = x -> x^2
 x = range(0, 1, length=20)
 y = f.(x)
 problem = SampledIntegralProblem(y, x)
-method = TrapezoidalRul()
+method = TrapezoidalRule()
 solve(problem, method)
 ```
 """