Update OLSexamples.md

docs/src/OLSexamples.md

@@ -12,8 +12,7 @@ julia> f = apply_schema(f, schema(f, df));  # link model to data
 
 julia> lm(f, df)  # run OLS for illustration; not needed for following lines
 StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}, Matrix{Float64}}
-```
-```
+
 y ~ 1 + x
 
 Coefficients:
@@ -23,8 +22,7 @@ Coefficients:
 (Intercept)  0.0296797   0.0283593   1.05    0.2954  -0.025917  0.0852764
 x            1.03483     0.0285833  36.20    <1e-99   0.978798  1.09087
 ─────────────────────────────────────────────────────────────────────────
-```
-```julia
+
 julia> resp, predexog = modelcols(f, df);  # extract response & (exogenous) predictor variables
 
 julia> clustid = df.firm;  # extract clustering variable
@@ -43,16 +41,13 @@ julia> p(test)  # programmatically extract p value
 0.49459493f0
 
 julia> ci(test)  # programmatically extract confidence interval
-```
-```
+
 1×2 Matrix{Float32}:
  0.934961  1.13469
-```
-```julia
+
 julia> plot(plotpoints(test)...)  # plot confidence curve
-```
+
 ## Further examples
-```julia
 using WildBootTests, CSV, DataFrames, StatsModels, GLM, Plots
 
 # use Webb instead of Rademacher weights, 99,999 bootstrap replications instead of 999