Makefiles for projects done

projects/Makefile

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+all:
+	for d in `ls -d project_*/`; do \
+		echo "Processing $$d" ; \
+		cd $$d; $(MAKE) zip ; \
+	done
+
+	mv project_*/*zip .
+
+clean:
+	for d in `ls -d project_*/`; do \
+		echo "Cleaning up $$d" ; \
+		cd $$d; $(MAKE) clean ; \
+	done
+
+	rm -f *.zip
+	rm -rf auto

projects/disclaimer.tex

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+  \fbox{\parbox{0.985\linewidth}{ \small Please answer all questions
+      in an electronic file (.txt, .doc are ok, but we prefer .pdf) and
+      submit in ILIAS. If the assignments include programming
+      exercises, hand in a pdf for the questions, the .py files for
+      the programs, and the data in one zip file. 
+ 
+      Use complete and correct sentences unless otherwise
+      noted. Please be succinct. Use your own words. Write down a
+      concise reasoning, not just the result.  We expect you to do
+      exercises on your own, but you are encouraged to discuss the
+      exercises with your fellow students. If you blindly copy your
+      results from others, you miss out on a chance to learn something
+      new. Use all resources available to you, but always make sure
+      that you truly understand why you give the answer you give.
+    }}

projects/project_PCA_natural_images/Makefile

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+latex:
+	pdflatex *.tex
+	pdflatex *.tex
+
+clean:
+	rm -f *.log *.aux *.zip *.out
+	rm -f `basename *.tex .tex`.pdf
+
+zip: latex
+	zip `basename *.tex .tex`.zip *.pdf *.dat *.mat

projects/project_PCA_natural_images/pca_natural_images.tex

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+\documentclass[addpoints,10pt]{exam}
+\usepackage{url}
+\usepackage{color}
+\usepackage{hyperref}
+
+\pagestyle{headandfoot}
+\runningheadrule
+\firstpageheadrule
+
+\firstpageheader{Essential Statistics}{Homework 01 due 10/29/2014 23:59 am}{23. October 2014}
+\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
+\firstpagefooter{}{}{}
+\runningfooter{}{}{}
+\pointsinmargin
+\bracketedpoints
+
+%\printanswers
+\shadedsolutions
+
+
+\begin{document}
+%%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%%
+\sffamily
+\begin{flushright}
+\gradetable[h][questions]
+\end{flushright}
+
+\begin{center}
+  \fbox{\parbox{0.985\linewidth}{ \small Please answer all questions
+      in an electronic file (.txt, .doc are ok, but we prefer .pdf) and
+      submit in ILIAS.
+ 
+      Use complete and correct sentences unless otherwise
+      noted. Please be succinct. Use your own words. Write down a
+      concise reasoning, not just the result.  We expect you to do
+      exercises on your own, but you are encouraged to discuss the
+      exercises with your fellow students. If you blindly copy your
+      results from others, you miss out on a chance to learn something
+      new. Use all resources available to you, but always make sure
+      that you truly understand why you give the answer you give.
+    }}
+\end{center}
+
+%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{questions}
+  \question {\bf Reading assignment: Do not submit answers to this
+    question! } 
+
+  Read chapter 1. up to 2.4 (including) of Samuels/Wittmer/Schaffner.
+
+  Pay special attention to the following questions.
+  \begin{enumerate}
+  \item What types of scientific evidence do the authors list? How
+    strong are these evidences?
+  \item What are the different types of data encountered in
+    statistical analysis?
+  \item What is a population? What is a random sample? What are
+    sampling errors? What are nonsampling errors?
+  \item What is a descriptive statistic?
+  \item What property do robust statistics have?
+  \end{enumerate}
+
+  \question Install python and a suitable editor on your computer. 
+  \begin{parts}
+    \part For installing python, I recommend the anaconda
+    distribution: \url{http://continuum.io/downloads}. It does not
+    matter whether you install python 2.7 or 3.4. I will use python
+    3.4 syntax. 
+    
+    \part As editor I recommend either sublime text (for people new to
+    programming) or pycharm (for people with programming
+    experience). I do not recommend to use a text editor that comes
+    with your operating system (like word pad). Text processing
+    programs like Mircosoft Word or Libre-Office {\bf won't work at
+      all}. Programming needs a little more than just typing text and
+    you will make your life unnecessarily hard by using an editor not
+    suited for it.
+    \part Find out how to run a python program on your operating
+    system and how to install new python packages. Install the
+    packages {\tt pandas} and {\tt seaborn}. 
+  \end{parts}
+  
+  \question To publish scientific results, you will usually need
+  to use statistical methods. Some journals provide you with a brief
+  description of how they expect you to apply statistical methods. One
+  example can be found in the author guidelines of the journal
+  Nature
+
+  \begin{center}
+    \url{http://www.nature.com/neuro/pdf/sm_checklist.pdf}
+  \end{center}
+
+  Please read the ‘checklist’ and answer the following questions:
+  
+  \begin{parts}
+    \part[2] Why is it important that statistical methods are applied
+    correctly?  
+
+    \begin{solution}
+      When not applied correctly, the results of statistical methods
+      might not support your hypothesis and can lead to false
+      conclusions.
+    \end{solution}
+
+    \part[2] Name two common descriptive statistics and what you have
+    to specify for them in nature.
+
+    \begin{solution}
+      \begin{itemize}
+      \item A clearly defined number $n$ of data points should be
+        specified. If the sample is small, plot points instead of
+        using descriptive statistics. Errorbars should be clearly
+        defined.
+      \item measure of center: mean, median
+      \item measure of variability: standard deviation, range
+      \end{itemize}
+    \end{solution}
+
+    \part[3] Name one statistical test that you have heard of or
+    used. If you were to apply any of them, what would you have to
+    specify to follow the Nature guidelines?
+
+    \begin{solution}
+      {\bf Student's T-Test} for testing whether the mean of two
+      populations is the same
+      \begin{itemize}
+      \item a clearly defined $n$ for the test
+      \item a justification for the sample size used
+      \item a clear description of the statistical method: since the
+        t-test is very common, stating that a two independent sample
+        t-test was used should be sufficient.
+      \item Justify that the data meets the definition: the two
+        populations should be normally distributed with the same
+        variance; the data was sampled independently from the two
+        populations being compared.
+      \item Is the variance in the different groups different.
+      \item was it one-sided or two-sided
+      \end{itemize}
+    \end{solution}
+
+    \part[3] Why are you asked to justify each incidence in which
+    you exclude some of the data that you collected? What could be a
+    valid reason to exclude a data point?
+
+    \begin{solution}
+      Excluded data points might make a sample from a population not
+      representative anymore, and can therefore alter the outcome and
+      conclusions of a study. They might be excluded if there is a
+      good reason to believe that they are not part of the population
+      under investigation.
+    \end{solution}
+
+  \end{parts}
+
+  \question {\bf Robust statistics} In 1888, P. Topinard published
+  data on the brain weights of hundreds of French men and women. Here
+  are ten brain weights (in Gramm) of female brains from the dataset
+  \begin{center} [1125, 1027, 1112, 983, 1090, 1247, 1045, 983, 972, 1045]
+  \end{center}
+  
+  Open a new file ``brain\_weight.py'' with you text editor to write
+  the following python program (please hand in the plots and the program). 
+  \begin{parts}
+    \part[2] Create a list that contains the above brain weights.
+    \part[2] Create a new list that contains the following ten means:
+    Each mean is computed from the original list after removing one
+    element (hint use slicing and adding lists for that; we did this
+    in the lecture already). {\bf Warning:} I {\em do not} expect you
+    to use {\tt for}-loops. Only use them if you know them already. If
+    you do use them, be prepared to explain your code to me to get
+    credits for this task.
+    \part[2] Create yet another list that does the same, only for the
+    median. 
+    \part[2] Make a boxplot with the different means and medias (like
+    in the lecture). To show the plot at the end of the program
+    you need to put a {\tt plt.show()} at the end of the program. If
+    you want to save the plot, put the command {\tt
+      plt.gcf().savefig('YOUR\_NAME\_homework01.pdf')} before that. Label
+    the y-axis by using the function {\tt plt.ylabel('FILL IN YOUR LABEL')}
+    \part[2] What can you observe and what does that tell you about
+    the robustness of the statistic?
+  \end{parts}
+  \begin{solution}
+    \begin{verbatim}
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+sns.set_context("paper", font_scale=1.5, rc={"lines.linewidth": 2.5})
+
+w = [1125, 1027, 1112, 983, 1090, 1247, 1045, 983, 972, 1045]
+
+brain_means = [ np.mean(w[1:]), np.mean(w[:1] + w[2:]), np.mean(w[:2] + w[3:]), \
+                np.mean(w[:3] + w[4:]), np.mean(w[:4] + w[5:]), np.mean(w[:5] + w[6:]), \
+                np.mean(w[:6] + w[7:]), np.mean(w[:7] + w[8:]), np.mean(w[:8] + w[9:]),\
+                np.mean(w[:9]) ]
+brain_medians = [ np.median(w[1:]), np.median(w[:1] + w[2:]), np.median(w[:2] + w[3:]), \
+                np.median(w[:3] + w[4:]), np.median(w[:4] + w[5:]), np.median(w[:5] + w[6:]), \
+                np.median(w[:6] + w[7:]), np.median(w[:7] + w[8:]), np.median(w[:8] + w[9:]),\
+                np.median(w[:9]) ]
+
+sns.boxplot([brain_means, brain_medians], names=['means', 'medians'])
+plt.ylabel('brain weight [g]')
+plt.gcf().savefig('fabian_sinz_homework01.pdf')
+plt.show()
+    \end{verbatim}
+  \end{solution}
+  
+  
+\end{questions}
+
+
+
+
+
+\end{document}