typos in regression

doc/src/Regression/Regression.do.txt

@@ -2179,7 +2179,7 @@ the design matrix and the parameters $\beta$.
 ===== Resampling methods: Jackknife and Bootstrap =====
 
 Two famous
-resampling methods are \textit{the independent bootstrap} and \textit{the jackknife}. 
+resampling methods are the _independent bootstrap_ and _the jackknife_. 
 
 The jackknife is a special case of the independent bootstrap. Still, the jackknife was made
 popular prior to the independent bootstrap. And as the popularity of
@@ -2245,15 +2245,15 @@ def stat(data):
 t = jack(X, stat)
 !ec
 
-Consider first the function \texttt{jack()}. This function repeatedly
-estimates the function called \texttt{statistic()} under the resampled
+Consider first the function _jack()_. This function repeatedly
+estimates the function called _statistic()_ under the resampled
 data by systematically leaving out one observation from the data. The
-function \texttt{stat()} is passed as an argument to
-\texttt{jack()}. The array \texttt{t} is eventually returned, which
+function _stat()_ is passed as an argument to
+_jack()_. The array _t_ is eventually returned, which
 contains all the estimates $\widehat{\vec{\theta}}$, and can be
-plotted or analysed in other ways, such as by calling \texttt{std(t)}
-from \texttt{numpy} to estimate the standard error of
-$\widehat{\vec{\theta}}$. The function \texttt{std(t)} is just the
+plotted or analysed in other ways, such as by calling _std(t)_
+from _numpy_ to estimate the standard error of
+$\widehat{\vec{\theta}}$. The function _std(t)_ is just the
 estimator $\widehat{\sigma}^2$.
 
 
@@ -2339,7 +2339,7 @@ def stat(data):
 t = boot(X, stat, 2**9)
 !ec
 
-Consider first the function \texttt{boot()}. In the \texttt{for}-loop, this function repeatedly estimates the function called \texttt{statistic()} under the resampled data \texttt{data[randint(0,n,n)]}. The function \texttt{statistic()} is passed as an argument to \texttt{boot()}. The array \texttt{t} is eventually returned, which contains all the estimates $\widehat{\vec{\theta}}$, and can be plotted or analysed in other ways, such as by calling \texttt{std(t)} from \texttt{numpy} to estimate the standard error of $\widehat{\vec{\theta}}$. The function \texttt{std(t)} is just the estimator $\widehat{\sigma}^2$.
+Consider first the function _boot()_. In the _for_ loop, this function repeatedly estimates the function called _statistic()_ under the resampled data in  _data(randint(0,n,n))_. The function _statistic()_ is passed as an argument to _boot()_. The array _t_ is eventually returned, which contains all the estimates $\widehat{\vec{\theta}}$, and can be plotted or analysed in other ways, such as by calling _std(t)_ from _numpy_ to estimate the standard error of $\widehat{\vec{\theta}}$. The function _std(t)_ is just the estimator $\widehat{\sigma}^2$.
 
 
 
@@ -2353,7 +2353,7 @@ o Define a vector $\vec{x}^*$ containing the values which were drawn from $\vec{
 o Using the vector $\vec{x}^*$ compute $\widehat{\theta}^*$ by evaluating $\widehat \theta$ under the observations $\vec{x}^*$. 
 o Repeat this process $k$ times. 
 
-When you are done, you can draw a histogram of the relative frequency of $\widehat \theta^*$. This is your estimate of the probability distribution $p(t)$. Using this probability distribution you can estimate any statistic thereof. In principle you never draw the histogram of the relative frequency of $\widehat{\theta}^*$. Instead you use the estimators corresponding to the statistic of interest. For example, if you are interested in estimating the variance of $\widehat \theta$, apply the esimator $\widehat \sigma^2$ to the values $\widehat \theta ^*$.
+When you are done, you can draw a histogram of the relative frequency of $\widehat \theta^*$. This is your estimate of the probability distribution $p(t)$. Using this probability distribution you can estimate any statistics thereof. In principle you never draw the histogram of the relative frequency of $\widehat{\theta}^*$. Instead you use the estimators corresponding to the statistic of interest. For example, if you are interested in estimating the variance of $\widehat \theta$, apply the esimator $\widehat \sigma^2$ to the values $\widehat \theta ^*$.
 
 
 
@@ -2452,7 +2452,7 @@ We can show that $V(\overline{X}_i) = V(\overline{X}_j)$ for all $0 \leq i \leq
 We can then wrap up
 !bt
 \begin{align}
-n_{j+1} \overline{X}_{j+1}  &= \sum_{i=1}^{n_{j+1}} (\vec{X}_{j+1})_i \stackrel{ \eqref{eq:blocking} }{=}  \frac{1}{2}\sum_{i=1}^{n_{j}/2} (\vec{X}_{j})_{2i-1} + (\vec{X}_{j})_{2i} \nonumber \\
+n_{j+1} \overline{X}_{j+1}  &= \sum_{i=1}^{n_{j+1}} (\vec{X}_{j+1})_i =  \frac{1}{2}\sum_{i=1}^{n_{j}/2} (\vec{X}_{j})_{2i-1} + (\vec{X}_{j})_{2i} \nonumber \\
 &= \frac{1}{2}\left[ (\vec{X}_j)_1 + (\vec{X}_j)_2 + \cdots + (\vec{X}_j)_{n_j} \right] = \underbrace{\frac{n_j}{2}}_{=n_{j+1}} \overline{X}_j = n_{j+1}\overline{X}_j. 
 \end{align}
 !et