From 8f4521195ec9e47830b9694582d0f557d0b23878 Mon Sep 17 00:00:00 2001
From: Frank Sauerburger <frank@sauerburger.com>
Date: Tue, 22 Aug 2017 17:04:56 +0200
Subject: [PATCH] Fix typos

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index cdc9c2c..1534850 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,7 @@ The `--user` argument for `pip` installs the python package in your home
 directory, which hides potentially older packages installed with `apt-get`.
 
 ## Windows
-Since I'm not using python on Windows myself, I don't have first-hand experiance
+Since I'm not using python on Windows myself, I don't have first-hand experience
 with it. However I think [Anaconda](https://www.continuum.io/downloads) is a
 good solution for windows users, which provides all required packages. 
 
@@ -166,7 +166,7 @@ The result is [-1.   0.5  2.   5.   6.5]
 ```
 
 Numpy offers many other functionalities which are beyond the scope of this basic
-introduction. It is definetely worth glancing at the
+introduction. It is definitely worth glancing at the
 [documentation](https://docs.scipy.org/doc/numpy/index.html).
 
 # Plotting Functions
@@ -399,7 +399,7 @@ numpy array. The function returns a two dimensional array. The outer array has
 one entry for each line in the text file. The inner array has two entries in our
 case, one for the
 channel and the other one for the event count. We can use `transpose()` to flip
-the matrix, such that the outer array has tswo entries, one with an array of
+the matrix, such that the outer array has two entries, one with an array of
 channel values and the other one with event counts. Since our measured event
 counts stem from radioactive decay, we know that the event counts follow a
 Poisson distribution. Therefore, the uncertainties of the event counts are simply
@@ -614,7 +614,7 @@ laboratories. Here is a list with online resources, which might be useful.
 
  - [Python](https://docs.python.org/3/) documentation
  - [Numpy and Scipy](https://docs.scipy.org/doc/) documentation
- - [Matplotlib](http://matplotlib.org/contents.html) documentaiton
+ - [Matplotlib](http://matplotlib.org/contents.html) documentation
  - [Orthogonal distance regression](https://docs.scipy.org/doc/scipy/reference/odr.html) - least square fit, which considers
    uncertainties in $`x`$ and $`y`$ directions.
  - Histograms in
-- 
GitLab