notes: fix statistic typos

notes/sections/1.md

@@ -125,8 +125,8 @@ $$
 On each iteration the function is interpolated by a parabola passing though the
 points $x_\text{min}$, $x_e$, $x_\text{max}$ and the minimum is computed as the
 vertex of the parabola. If this point is found to be inside the interval, it is
-taken as a guess for the true minimum; otherwise the method falls back to a g
-olden section (using the ratio $(3 - \sqrt{5})/2 \approx 0.3819660$ proven to be
+taken as a guess for the true minimum; otherwise the method falls back to a
+golden section (using the ratio $(3 - \sqrt{5})/2 \approx 0.3819660$ proven to be
 optimal) of the interval. The value of the function at this new point $x'$ is
 calculated. In any case, if the new point is a better estimate of the minimum,
 namely if $f(x') < f(x_e)$, then the current estimate of the minimum is updated.  
@@ -173,7 +173,7 @@ although the result is quite imprecise.
 
 #### Median
 
-The median is a central tendency statistics that, unlike the mean, is not
+The median is a central tendency statistic that, unlike the mean, is not
 very sensitive to extreme values, albeit less indicative. For this reason
 is well suited as test statistic in a pathological case such as the Landau
 distribution.  

notes/sections/7.md

@@ -389,7 +389,7 @@ Aerial and lateral views of the samples. Projection line in blue and cut in red.
 Using the same parameters of the training set, a number $N_t$ of test samples
 was generated and the points were classified applying both methods. To avoid
 storing large datasets in memory, at each iteration, false positives and
-negatives were recorded using a running statistics method implemented in the
+negatives were recorded using a running statistic method implemented in the
 `gsl_rstat` library. For each sample, the numbers $N_{fn}$ and $N_{fp}$ of
 false negative and false positive were obtained in this way: for every signal
 point $x_s$, the threshold function $f(x_s)$ was computed, then: