diff --git a/Lectures_my/MC_2016/Lecture1/mchrzasz.log b/Lectures_my/MC_2016/Lecture1/mchrzasz.log
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@@ -1,4 +1,4 @@
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diff --git a/Lectures_my/MC_2016/Lecture1/mchrzasz.pdf b/Lectures_my/MC_2016/Lecture1/mchrzasz.pdf
index 0600d11..68ca850 100644
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diff --git a/Lectures_my/MC_2016/Lecture1/mchrzasz.tex b/Lectures_my/MC_2016/Lecture1/mchrzasz.tex
index 65f55e1..2c7916b 100644
--- a/Lectures_my/MC_2016/Lecture1/mchrzasz.tex
+++ b/Lectures_my/MC_2016/Lecture1/mchrzasz.tex
@@ -529,7 +529,7 @@
      =0~~x,y~uncorrelated\\
      >0~~x,y~correlated\\
      <0~~x,y~anticorrelated\\     
-	    \end{dcases}
+	    \end{dcases}\nonumber
 \end{equation}
 \ARROW If x,y are independent then $cov(x,y) =0$ and $V(x+y) =V(x)+V(y)$\\
 \ARROW If variables are independent then they are uncorrelated. If they are uncorrelated then can still be dependent$^{\dagger}$. 
@@ -549,7 +549,7 @@
 
 The law of large numbers (LLN): let's take $n$ numbers from $\mathcal{U}(a,b)$ and for each of them we calculate the $f(u_i)$.  The LLN:
 \begin{equation}
-\frac{1}{n}\sum_{i=1}^nf(u_i) \xrightarrow{N\to \infty} \dfrac{1}{b-a}\int_a^b f(u)du
+\frac{1}{n}\sum_{i=1}^nf(u_i) \xrightarrow{N\to \infty} \dfrac{1}{b-a}\int_a^b f(u)du \nonumber
 \end{equation}
 \ARROW We say (in statistic terminology) that the left side is asymptotically equivalent to the value of the integration if $n \to \infty$.\\
 \ARROW Assumptions:
@@ -591,7 +591,7 @@
 \end{exampleblock}
 \ARROW E(1.3) Using ROOT draw the Gauss distribution:
 \begin{equation}
-\rho(x;\mu \sigma) = \dfrac{1}{\sqrt{2}\pi } e^{ \dfrac{-(x-\mu)^2}{2\sigma^2}} \nonumber
+\rho(x;\mu, \sigma) = \dfrac{1}{\sigma\sqrt{2\pi} } e^{ \frac{-(x-\mu)^2}{2\sigma^2}} \nonumber
 \end{equation}
 and calculate (for given $\mu$ and $\sigma$ the:
 \begin{align*}
@@ -626,7 +626,7 @@
 \ARROW E(1.4) Calculate the above.\\
 \ARROW From above we get:
 \begin{equation}
-\dfrac{R_n-n/12}{\sqrt{n/12}} \xrightarrow{N\to \infty} N(0,1).
+\dfrac{R_n-n/12}{\sqrt{n/12}} \xrightarrow{N\to \infty} N(0,1).\nonumber
 \end{equation}
 aka we get a Gaussian distribution.
 \ARROW For $n=12$ $\Rightarrow~(R_{12}-6)$ ''practical'' Gauss generator.\\