Started statistics lecture

statistics/Makefile

@@ -1,21 +0,0 @@
-DOTSOURCES = $(wildcard figs/*.dot)
-
-
-
-all: $(DOTSOURCES:dot=pdf) 
-	pdflatex lecture_statistics*.tex
-	pdflatex lecture_statistics*.tex
-	pdflatex lecture_statistics*.tex
-
-
-figs/prob%.pdf : figs/prob%.dot
-	dot -Tpdf -o $@ $<
-
-figs/test%.pdf : figs/test%.dot
-	dot -Tpdf -o $@ $<
-
-figs/fig%.pdf : figs/fig%.dot
-	dot -Tpdf -o $@ $<
-
-clean:
-	rm -rf *.dvi *.pdf *.aux *.out *.log auto *.nav *.snm *.toc *.vrb

statistics/assignments/Makefile

@@ -1,16 +0,0 @@
-all:
-	for number in 001 002 003 004 005 006 007 007 009 010 011 012 013 014 015 016 017 ; do \
-		echo $$number ; \
-		sed "s/000/$$number/g" day1.tex > tmp.tex; \
-		pdflatex tmp.tex; \
-		mv tmp.pdf day1_$$number.pdf; \
-		cp ../data/example$$number.csv ./ ;\
-		rm tmp.* ; \
-		zip example$$number.zip example$$number.csv  day1_$$number.pdf ; \
-		rm example$$number.csv ;\
-		rm day1_$$number.pdf ; \
-	done
-
-clean:
-	rm *.zip
-	rm -rf auto

statistics/assignments/day1.tex

@@ -1,72 +0,0 @@
-\documentclass[addpoints,10pt]{exam}
-\usepackage{url}
-\usepackage{color}
-\usepackage{hyperref}
-
-\pagestyle{headandfoot}
-\runningheadrule
-\firstpageheadrule
-
-\firstpageheader{Scientific Computing}{afternoon assignment day 01}{10/20/2014}
-%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
-\firstpagefooter{}{}{}
-\runningfooter{}{}{}
-\pointsinmargin
-\bracketedpoints
-
-%\printanswers
-\shadedsolutions
-
-
-\begin{document}
-%%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%%
-\sffamily
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-
-\begin{questions}
-  \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. 
-
-  Assume you collected the following dataset. You can download it from
-  Ilias as {\tt example000.csv}. Here is the description of the dataset:
-
-  \begin{quotation}
-    \tt 
-    \input{../examples/example000.tex}
-  \end{quotation}
-
-  \begin{parts}
-    \part Download the dataset and write a script that loads it into
-    matlab. 
-
-    \part Think about the type of your data (I might ask you that
-    tomorrow).
-
-    \part Produce a plot that displays the data in an appropriate
-    way. Make sure to respect all elements of good plotting we
-    discussed today.
-
-    \part Download the statistical checklist from nature. Produce {\bf
-      one} slide that contains the plot and a concise summary of your
-    data which respects the requirements made by nature (assume you
-    are producing a figure legend for the figure in nature). It is
-    good style to avoid expressions like ``the plot shows'' or
-    similar. 
-
-    \part Upload your code, the data, and the slide as a zip to
-    Ilias. Deadline is 19h00. Structure the zip such that you can
-    present you program in front of the class. Several students will
-    be asked to present their slide and their code tomorrow morning. 
-
-  \end{parts}
-  
-\end{questions}
-
-
-
-
-
-\end{document}

statistics/assignments/day2.tex

@@ -1,46 +0,0 @@
-\documentclass[addpoints,10pt]{exam}
-\usepackage{url}
-\usepackage{color}
-\usepackage{hyperref}
-
-\pagestyle{headandfoot}
-\runningheadrule
-\firstpageheadrule
-
-\firstpageheader{Scientific Computing}{afternoon assignment day 02}{10/21/2014}
-%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
-\firstpagefooter{}{}{}
-\runningfooter{}{}{}
-\pointsinmargin
-\bracketedpoints
-
-%\printanswers
-\shadedsolutions
-
-
-\begin{document}
-%%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%%
-\sffamily
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-
-\begin{questions}
-  \question Download example002 from yesterday (brain weights). 
-  \begin{parts}
-    \part Simulate a null distribution via permutation.
-    \part Determine whether you can reject ``means are equal'' on a
-    5\% significance level using the simulated null distribution.
-    \part Check whether the means are different with a two sample
-    t-test in matlab ({\tt ttest2}).
-    \part Plot the data appropriately and generate a single slide that
-    contains the plot and short discussion of the test that respects
-    the nature statistical checklist (ignore all question whether the
-    assumptions of the test are satisfied).
-    \part Upload the slide and the code to Ilias. Deadline is 19h00.
-  \end{parts}
-\end{questions}
-
-
-
-
-
-\end{document}

statistics/assignments/day3.tex

@@ -1,65 +0,0 @@
-\documentclass[addpoints,10pt]{exam}
-\usepackage{url}
-\usepackage{color}
-\usepackage{hyperref}
-
-\pagestyle{headandfoot}
-\runningheadrule
-\firstpageheadrule
-
-\firstpageheader{Scientific Computing}{afternoon assignment day 02}{10/22/2014}
-%\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
-\firstpagefooter{}{}{}
-\runningfooter{}{}{}
-\pointsinmargin
-\bracketedpoints
-
-%\printanswers
-\shadedsolutions
-
-
-\begin{document}
-%%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%%
-\sffamily
-%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
-
-\begin{questions}
-  \question When the p-value is small, we reject the null
-  hypothesis. For example, if you want to test whether two means are
-  not equal, the null hypothesis is ``means are equal''. If e.g. $p\le
-  0.05$ then we take it as sufficient evidence that the null
-  hypothesis is not true. Therefore, we assume that the means are not
-  equal (which is what you want to show). 
-
-  In this exercise we will look at what kind of p-values we expect if
-  the null hypothesis is true. In our example, this would be the case
-  if the true means of two datasets are actually equal. 
-  \begin{parts}
-    \part Think about how you expect the p-values to behave in that
-    situation. 
-    \part Simulate the situation in which the means are equal by
-    repeating the following at least $1000$ times:
-    \begin{enumerate}
-    \item Generate two arrays {\tt x} and {\tt y} with $10$ normally
-      (Gaussian) distributed random numbers using {\tt randn}. By
-      construction, the true means behind the random number are zero. 
-    \item Perform a two sample t-test ({\tt ttest2}) on {\tt x} and
-      {\tt y}. Store the p-value.
-    \end{enumerate}
-    \part Plot a histogram of the $1000$ p-values. What do you think
-    is the distribution the p-values (i.e. if you repeated this
-    experiment many more times, how would the histogram look like)?
-    \part Given what you find, think about whether the following
-    strategy is statistically valid: You collect $10$ data points from
-    each group and perform a test. If the test is not significant, you
-    collect $10$ more and repeat the test. If the test tells you that
-    there is a significant difference you stop. Otherwise you repeat
-    the procedure until the test is significant.
-  \end{parts}
-\end{questions}
-
-
-
-
-
-\end{document}

statistics/beamercolorthemetuebingen.sty

@@ -1 +0,0 @@
-../latex/beamercolorthemetuebingen.sty

statistics/data/brainWeight.dat

@@ -1,185 +0,0 @@
-1607 0 
-1157 0 
-1248 0 
-1310 0 
-1398 0 
-1237 0 
-1232 0 
-1343 0 
-1380 0 
-1274 0 
-1245 0 
-1286 0 
-1508 0 
-1105 0 
-1123 0 
-1198 0 
-1300 0 
-1249 0 
-1185 0 
-915 0 
-1345 0 
-1107 0 
-1357 0 
-1227 0 
-1205 0 
-1435 0 
-1289 0 
-1093 0 
-1211 0 
-1260 0 
-1193 0 
-1330 0 
-1130 0 
-1357 0 
-1193 0 
-1232 0 
-1321 0 
-1260 0 
-1380 0 
-1230 0 
-1136 0 
-1029 0 
-1223 0 
-1240 0 
-1264 0 
-1020 0 
-1415 0 
-1410 0 
-1275 0 
-1230 0 
-1085 0 
-1048 0 
-1181 0 
-1103 0 
-1165 0 
-1547 0 
-1173 0 
-1660 0 
-1307 0 
-1535 0 
-1315 0 
-1257 0 
-1424 0 
-1309 0 
-1170 0 
-1412 0 
-1270 0 
-1230 0 
-1233 0 
-1561 0 
-1193 0 
-1272 0 
-1355 0 
-1137 0 
-1354 0 
-1110 0 
-1265 0 
-1407 0 
-1227 0 
-1330 0 
-1222 0 
-1305 0 
-1475 0 
-1177 0 
-1337 0 
-1145 0 
-1070 0 
-1305 0 
-1085 0 
-1303 0 
-1390 0 
-1532 0 
-1238 0 
-1233 0 
-1280 0 
-1245 0 
-1459 0 
-1157 0 
-1302 0 
-1385 0 
-1310 0 
-1342 0 
-1303 0 
-1248 0 
-1115 0 
-1365 0 
-1227 0 
-1353 0 
-1125 1 
-1027 1 
-1112 1 
-983 1 
-1090 1 
-1247 1 
-1045 1 
-983 1 
-972 1 
-1045 1 
-937 1 
-1245 1 
-1200 1 
-1270 1 
-1200 1 
-1145 1 
-1090 1 
-1040 1 
-1343 1 
-1010 1 
-1095 1 
-1180 1 
-1168 1 
-1095 1 
-1040 1 
-1235 1 
-1050 1 
-1038 1 
-1046 1 
-1255 1 
-1228 1 
-1000 1 
-1225 1 
-1220 1 
-1085 1 
-1067 1 
-1006 1 
-1138 1 
-1175 1 
-1252 1 
-1037 1 
-958 1 
-1020 1 
-1068 1 
-1107 1 
-1317 1 
-952 1 
-1056 1 
-1203 1 
-1183 1 
-1392 1 
-1130 1 
-1284 1 
-996 1 
-1228 1 
-1087 1 
-1035 1 
-1170 1 
-1064 1 
-1250 1 
-1129 1 
-1088 1 
-1037 1 
-1117 1 
-1095 1 
-1027 1 
-1027 1 
-1190 1 
-1153 1 
-1037 1 
-1120 1 
-1212 1 
-1024 1 
-1135 1 
-1177 1 
-1096 1 
-1114 1 

statistics/data/example001.csv

@@ -1,43 +0,0 @@
-MAO,Diagnosis
-6.8,I
-4.1,I
-7.3,I
-14.2,I
-18.8,I
-9.9,I
-7.4,I
-11.9,I
-5.2,I
-7.8,I
-7.8,I
-8.7,I
-12.7,I
-14.5,I
-10.7,I
-8.4,I
-9.7,I
-10.6,I
-7.8,II
-4.4,II
-11.4,II
-3.1,II
-4.3,II
-10.1,II
-1.5,II
-7.4,II
-5.2,II
-10,II
-3.7,II
-5.5,II
-8.5,II
-7.7,II
-6.8,II
-3.1,II
-6.4,III
-10.8,III
-1.1,III
-2.9,III
-4.5,III
-5.8,III
-9.4,III
-6.8,III

statistics/data/example002.csv

@@ -1,186 +0,0 @@
-Weight,Sex
-1607,m
-1157,m
-1248,m
-1310,m
-1398,m
-1237,m
-1232,m
-1343,m
-1380,m
-1274,m
-1245,m
-1286,m
-1508,m
-1105,m
-1123,m
-1198,m
-1300,m
-1249,m
-1185,m
-915,m
-1345,m
-1107,m
-1357,m
-1227,m
-1205,m
-1435,m
-1289,m
-1093,m
-1211,m
-1260,m
-1193,m
-1330,m
-1130,m
-1357,m
-1193,m
-1232,m
-1321,m
-1260,m
-1380,m
-1230,m
-1136,m
-1029,m
-1223,m
-1240,m
-1264,m
-1020,m
-1415,m
-1410,m
-1275,m
-1230,m
-1085,m
-1048,m
-1181,m
-1103,m
-1165,m
-1547,m
-1173,m
-1660,m
-1307,m
-1535,m
-1315,m
-1257,m
-1424,m
-1309,m
-1170,m
-1412,m
-1270,m
-1230,m
-1233,m
-1561,m
-1193,m
-1272,m
-1355,m
-1137,m
-1354,m
-1110,m
-1265,m
-1407,m
-1227,m
-1330,m
-1222,m
-1305,m
-1475,m
-1177,m
-1337,m
-1145,m
-1070,m
-1305,m
-1085,m
-1303,m
-1390,m
-1532,m
-1238,m
-1233,m
-1280,m
-1245,m
-1459,m
-1157,m
-1302,m
-1385,m
-1310,m
-1342,m
-1303,m
-1248,m
-1115,m
-1365,m
-1227,m
-1353,m
-1125,f
-1027,f
-1112,f
-983,f
-1090,f
-1247,f
-1045,f
-983,f
-972,f
-1045,f
-937,f
-1245,f
-1200,f
-1270,f
-1200,f
-1145,f
-1090,f
-1040,f
-1343,f
-1010,f
-1095,f
-1180,f
-1168,f
-1095,f
-1040,f
-1235,f
-1050,f
-1038,f
-1046,f
-1255,f
-1228,f
-1000,f
-1225,f
-1220,f
-1085,f
-1067,f
-1006,f
-1138,f
-1175,f
-1252,f
-1037,f
-958,f
-1020,f
-1068,f
-1107,f
-1317,f
-952,f
-1056,f
-1203,f
-1183,f
-1392,f
-1130,f
-1284,f
-996,f
-1228,f
-1087,f
-1035,f
-1170,f
-1064,f
-1250,f
-1129,f
-1088,f
-1037,f
-1117,f
-1095,f
-1027,f
-1027,f
-1190,f
-1153,f
-1037,f
-1120,f
-1212,f
-1024,f
-1135,f
-1177,f
-1096,f
-1114,f

statistics/data/example003.csv

@@ -1,52 +0,0 @@
-singtime
-4.3
-24.1
-6.6
-7.3
-4
-2.6
-4
-3.9
-9.4
-6.2
-1.6
-6.5
-0.2
-2.7
-17.4
-5.6
-2
-3.8
-1.2
-0.7
-1.6
-2.3
-3.7
-0.8
-0.5
-4.5
-11.5
-3.5
-0.8
-5.2
-2
-0.7
-1.7
-5
-2.8
-1.5
-3.9
-3.7
-4.5
-1.8
-1.2
-0.7
-0.7
-4.2
-4.7
-2.2
-1.4
-14.1
-8.6
-3.7
-3.5

statistics/data/example004.csv

@@ -1,29 +0,0 @@
-Pulse
-97
-111
-93
-98
-107
-77
-121
-88
-96
-123
-119
-91
-99
-95
-99
-102
-77
-85
-104
-106
-114
-85
-112
-102
-104
-94
-104
-98

statistics/data/example005.csv

@@ -1,37 +0,0 @@
-Branches
-23
-30
-54
-28
-31
-29
-34
-35
-30
-27
-21
-43
-51
-35
-51
-49
-35
-24
-26
-29
-21
-29
-37
-27
-28
-33
-33
-23
-37
-27
-40
-48
-41
-20
-30
-57

statistics/data/example006.csv

@@ -1,32 +0,0 @@
-Glucose
-81
-85
-93
-93
-99
-76
-75
-84
-78
-84
-81
-82
-89
-81
-96
-82
-74
-70
-84
-86
-80
-70
-131
-75
-88
-102
-115
-89
-82
-79
-106

statistics/data/example007.csv

@@ -1,24 +0,0 @@
-NerveCells
-35
-19
-33
-34
-17
-26
-16
-40
-28
-30
-23
-12
-27
-33
-22
-31
-28
-28
-35
-23
-23
-19
-29

statistics/data/example008.csv

@@ -1,21 +0,0 @@
-RateChange,Treatment
-28,Caffeine
-11,Caffeine
--3,Caffeine
-14,Caffeine
--2,Caffeine
--4,Caffeine
-18,Caffeine
-2,Caffeine
-2,Caffeine
-26,Decaf
-1,Decaf
-0,Decaf
--4,Decaf
--4,Decaf
-14,Decaf
-16,Decaf
-8,Decaf
-0,Decaf
-18,Decaf
--10,Decaf

statistics/data/example009.csv

@@ -1,12 +0,0 @@
-NEConcentration,Treatment
-543,Toluene
-523,Toluene
-431,Toluene
-635,Toluene
-564,Toluene
-549,Toluene
-535,Control
-385,Control
-502,Control
-412,Control
-387,Control

statistics/data/example010.csv

@@ -1,13 +0,0 @@
-Dopamine,Group
-3420,toluene
-2314,toluene
-1911,toluene
-2464,toluene
-2781,toluene
-2803,toluene
-1820,control
-1843,control
-1397,control
-1803,control
-2539,control
-1990,control

statistics/data/example011.csv

@@ -1,10 +0,0 @@
-Animal,Site I,Site II
-1,50.6,38
-2,39.2,18.6
-3,35.2,23.2
-4,17,19
-5,11.2,6.6
-6,14.2,16.4
-7,24.2,14.4
-8,37.4,37.6
-9,35.2,24.4

statistics/data/example012.csv

@@ -1,10 +0,0 @@
-Subject,mCPP,Placebo
-1,1.1,0
-2,1.3,-0.3
-3,1,0.6
-4,1.7,0.3
-5,1.4,-0.7
-6,0.1,-0.2
-7,0.5,0.6
-8,1.6,0.9
-9,-0.5,-2

statistics/data/example013.csv

@@ -1,9 +0,0 @@
-Animal,Control,Regenerating
-1,16.3,11.5
-2,4.8,3.6
-3,10.9,12.5
-4,14.2,6.3
-5,16.3,15.2
-6,9.9,8.1
-7,29.2,16.6
-8,22.4,13.1

statistics/data/example014.csv

@@ -1,16 +0,0 @@
-BodyTempDrop,AlcoholDose
-0.2,1.5
-1.9,1.5
--0.1,1.5
-0.5,1.5
-0.8,1.5
-4,3
-3.2,3
-2.3,3
-2.9,3
-3.8,3
-3.3,6
-5.1,6
-5.3,6
-6.7,6
-5.9,6

statistics/data/example015.csv

@@ -1,18 +0,0 @@
-PeakFlow,Height
-733,174
-572,183
-500,176
-738,169
-616,183
-787,186
-866,178
-670,175
-550,172
-660,179
-575,171
-577,184
-783,200
-625,195
-470,176
-642,176
-856,190

statistics/data/example016.csv

@@ -1,19 +0,0 @@
-Patient,Before,After
-1,98,75
-2,100,60
-3,82,25
-4,100,55
-5,93,78
-6,119,102
-7,70,58
-8,78,70
-9,104,90
-10,70,50
-11,60,65
-12,88,45
-13,45,36
-14,159,144
-15,65,27
-16,98,90
-17,66,16
-18,67,53

statistics/data/example017.csv

@@ -1,21 +0,0 @@
-LegStrength,UpperBodyStrength
-55,low
-70,low
-45,low
-246,low
-240,low
-96,low
-225,low
-40,middle
-200,middle
-250,middle
-192,middle
-117,middle
-215,middle
-181,high
-85,high
-416,high
-228,high
-257,high
-316,high
-134,high

statistics/do_regression.m

@@ -1,6 +0,0 @@
-function parameter = do_regression(x, y, start_parameter)
-
-options = optimoptions('fminunc','GradObj','on');
-fun = @exp_funct;
-myfunc = @ (p)(lserr(p, x, y, fun));
-parameter = fminunc(myfunc, start_parameter, options);

statistics/environments.tex

@@ -1 +0,0 @@
-../latex/environments.tex

statistics/examples/example001.tex

@@ -1,6 +0,0 @@
-MAO and Schizophrenia Monoamine oxidase (MAO) is an enzyme that is
-thought to play a role in the regulation of behavior. To see whether
-different categories of schizophrenic patients have different levels
-of MAO activity, researchers collected blood specimens from 42
-patients and measured the MAO activity in the platelets. Values are
-expressed as nmol benzylaldehyde product per 108 platelets per hour.

statistics/examples/example002.tex

@@ -1,3 +0,0 @@
-Brain Weight: In 1888, P. Topinard published data on the brain weights
-of hundreds of French men and women. Brain weights are given in
-gram. 

statistics/examples/example003.tex

@@ -1,4 +0,0 @@
-Cricket Singing Times Male Mormon crickets (Anabrus simplex) sing to attract mates.
-A field researcher measured the duration of 51 unsuccessful songs--that is, the time
-until the singing male gave up and left his perch. The data is given
-in minutes.

statistics/examples/example004.tex

@@ -1,3 +0,0 @@
-Pulse after Exercise: A group of 28 adults did some moderate exercise
-for five minutes and then measured their pulses. Data is given in
-beats/minute.

statistics/examples/example005.tex

@@ -1,5 +0,0 @@
-A dendritic tree is a branched structure that emanates from the body
-of a nerve cell. As part of a study of brain development, 36 nerve
-cells were taken from the brains of newborn guinea pigs. The
-investigators counted the number of dendritic branch segments
-emanating from each nerve cell.

statistics/examples/example006.tex

@@ -1,4 +0,0 @@
-For each of 31 healthy dogs, a veterinarian measured the glucose
-concentration in the anterior chamber of the right eye and also in the
-blood serum. The following data are the anterior chamber glucose
-measurements, expressed as a percentage of the blood glucose.

statistics/examples/example007.tex

@@ -1,5 +0,0 @@
-A veterinary anatomist investigated the spatial arrangement of the
-nerve cells in the intestine of a pony.  He removed a block of tissue
-from the intestinal wall, cut the block into many equal sections, and
-counted the number of nerve cells in each of 23 randomly selected
-sections.

statistics/examples/example008.tex

@@ -1,8 +0,0 @@
-Researchers were interested in the short-term effect that caffeine has
-on heart rate. They enlisted a group of volunteers and measured each
-person's resting heart rate. Then they had each subject drink 6 ounces
-of coffee. Nine of the subjects were given coffee containing caffeine
-and 11 were given decaffeinated coffee. After 10 minutes each person's
-heart rate was measured again. The data in the table contains the
-change in heart rate; a positive number means that heart rate went up
-and a negative number means that heart rate went down.

statistics/examples/example009.tex

@@ -1,9 +0,0 @@
-Toluene and the Brain Abuse of substances containing toluene (for
-example, glue) can produce various neurological symptoms. In an
-investigation of the mechanism of these toxic effects, researchers
-measured the concentrations of various chemicals in the brains of rats
-that had been exposed to a toluene-laden atmosphere, and also in
-unexposed control rats. The concentrations of the brain chemical
-norepinephrine (NE) in the medulla region of the brain, for six
-toluene-exposed rats and five control rats, are given in accompanying
-data file in ng/g.

statistics/examples/example010.tex

@@ -1,3 +0,0 @@
-In a pharmacological study, researchers measured the concentration of
-the brain chemical dopamine in six rats exposed to toluene and six
-control rats. Number are specified in ng/g.

statistics/examples/example011.tex

@@ -1,6 +0,0 @@
-Nerve Cell Density For each of nine horses, a veterinary anatomist
-measured the density of nerve cells at specified sites in the
-intestine. The results for site I (midregion of jejunum) and site II
-(mesenteric region of jejunum) are given in the accompanying dataset.
-Each density value is the average of counts of nerve cells in five
-equal sections of tissue.

statistics/examples/example012.tex

@@ -1,6 +0,0 @@
-Hunger Rating During a weight loss study each of nine subjects was
-given either the active drug m-chlorophenylpiperazine (mCPP) for two
-weeks and then a placebo for another two weeks, or else was given the
-placebo for the first two weeks and then mCPP for the second two
-weeks. As part of the study the subjects were asked to rate how hungry
-they were at the end of each two-week period. 

statistics/examples/example013.tex

@@ -1,10 +0,0 @@
-Certain types of nerve cells have the ability to regenerate a part of
-the cell that has been amputated.  In an early study of this process,
-measurements were made on the nerves in the spinal cord in rhesus
-monkeys. Nerves emanating from the left side of the cord were cut,
-while nerves from the right side were kept intact. During the
-regeneration process, the content of creatine phosphate (CP) was
-measured in the left and the right portion of the spinal cord. The
-following table shows the data for the right (control) side (Y1), and
-for the left (regenerating) side (Y2). The units of measurement are mg
-CP per 100 gm tissue.

statistics/examples/example014.tex

@@ -1,9 +0,0 @@
- In an investigation of the physiological effects of alcohol
-(ethanol), 15 mice were randomly allocated to three treatment groups,
-each to receive a different oral dose of alcohol. The dosage levels
-were 1.5, 3.0, and 6.0 g alcohol/kg body weight. The body temperature
-of each mouse was measured immediately before the alcohol was given
-and again 20 minutes afterward. The accompanying data shows the drop
-(before minus after) in body temperature for each mouse. (The negative
-value - 0.1 refers to a mouse whose temperature rose rather than
-fell.)

statistics/examples/example015.tex

@@ -1,5 +0,0 @@
-The peak flow rate of a person is the fastest rate
-at which the person can expel air after taking a deep breath.
-Peak flow rate is measured in units of liters per minute and
-gives an indication of the person's respiratory health. Flow is given
-in l/min, height in cm. 

statistics/examples/example016.tex

@@ -1,6 +0,0 @@
-An experiment was conducted to study the effect of tamoxifen on
-patients with cervical cancer. One of the measurements made, both
-before and again after tamoxifen was given, was microvessel density
-(MVD). MVD, which is measured as number of vessels per mm$^2$, is a
-measurement that relates to the formation of blood vessels that feed a
-tumor and allow it to grow and spread.

statistics/examples/example017.tex

@@ -1,5 +0,0 @@
-A group of female college students were divided into three groups
-according to upper body strength. Their leg strength was tested by
-measuring how many consecutive times they could leg press 246 pounds
-before exhaustion. (The subjects were allowed only one second of rest
-between consecutive lifts.)

statistics/exp_funct.m

@@ -1,3 +0,0 @@
-function y = exp_funct(A, tau, t)
-
-y = A .* (1 - exp(-1 * (t./tau)));