Improve wording in rf614 tutorial.

Author: hageboeck

tutorials/roofit/rf614_binned_fit_problems.C

@@ -26,7 +26,7 @@
 // One should in principle be able to use
 // pdf.generateBinned(x, nEvents, RooFit::ExpectedData()).
 // Unfortunately it has a problem: it also has the bin bias that this tutorial
-// demostrates, to if we would use it, the biases would cancel out.
+// demostrates. If we used it, the biases would cancel out.
 std::unique_ptr<RooDataHist> generateBinnedAsimov(RooAbsPdf const &pdf, RooRealVar &x, int nEvents)
 {
    auto dataH = std::make_unique<RooDataHist>("dataH", "dataH", RooArgSet{x});
@@ -82,14 +82,14 @@ void rf614_binned_fit_problems()
    // result and the true parameters comes from binning effects.
    std::unique_ptr<RooAbsData> expoData{generateBinnedAsimov(expo, x, 10000)};
 
-   // If you do the fit the usual was in RooFit, you will get a bias in the
+   // If you do the fit the usual way in RooFit, you will get a biased
    // result. This is because the continuous, normalized pdf is evaluated only
    // at the bin centers.
    std::unique_ptr<RooFitResult> fit1{expo.fitTo(*expoData, Save(), PrintLevel(-1), SumW2Error(false))};
    fit1->Print();
 
    // In the case of an exponential function, the bias that you get by
-   // evaluating the pdf only at the bin centers is a constant scale factor in
+   // evaluating the pdf only at the bin centers is an almost constant scale factor in
    // each bin. Here, we can do a trick to get rid of the bias: we also
    // evaluate the normalization integral for the pdf the same way, i.e.,
    // summing the values of the unnormalized pdf at the bin centers. Like this
@@ -103,7 +103,7 @@ void rf614_binned_fit_problems()
    // Power law example
    // -----------------
 
-   // Let's not look at another example: a power law \f[x^a\f].
+   // Let's now look at another example: a power law \f[x^a\f].
    RooRealVar a{"a", "a", -0.3, -5.0, 5.0};
    RooPower powerlaw{"powerlaw", "powerlaw", x, RooConst(1.0), a};
    std::unique_ptr<RooAbsData> powerlawData{generateBinnedAsimov(powerlaw, x, 10000)};
@@ -116,8 +116,8 @@ void rf614_binned_fit_problems()
    // trick by sampling the integral in the same way doesn't cancel out the
    // bias completely. The average bias is canceled, but there are per-bin
    // biases that remain. Still, this method has some value: it is cheaper than
-   // rigurously correcting the bias by integrating the pdf in each bin. So if
-   // you know your per-bin bias variations are small or performance is an
+   // rigorously correcting the bias by integrating the pdf in each bin. So if
+   // you know your per-bin bias, variations are small or performance is an
    // issue, this approach can be sufficient.
    enableBinIntegrator(powerlaw, x.numBins());
    std::unique_ptr<RooFitResult> fit4{powerlaw.fitTo(*powerlawData, Save(), PrintLevel(-1), SumW2Error(false))};
@@ -184,7 +184,7 @@ void rf614_binned_fit_problems()
    // initial model is not extremely off. Proving this mathematically is left
    // as an excercise to the reader.
 
-   // This counterterms can be enabled in RooFit if you use a binned
+   // These counterterms can be enabled in RooFit if you use a binned
    // RooDataHist to do your fit and pass the Offset("bin") option to
    // RooAbsPdf::fitTo() or RooAbsPdf::createNLL().