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openHPI Course "Internetworking 2016"

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Lecture-Feed of Series: Statistische Datenanalyse (WS 2008/09) Feed of Series: Statistische Datenanalyse (WS 2008/09)

Statistische Datenanalyse (WS 2008/09)

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Prof. Dr. Hannelore Liero

Daten werden in allen Bereichen des gesellschaftlichen Lebens erfasst, seien es riesige Datenmengen, die sich in Medizin, Biologie, Bioinformatik, Finanzwesen, Marketing und Soziologie ergeben oder auch weniger umfangreiche Beobachtungen bei naturwissenschaftlichen Experimenten und technischen Qualitätstests. Die Aufgabe der Statistik ist "Learning from data". Dabei geht es darum, Methoden und Konzepte aufzuzeigen, wie man die Informationen, die in diesen Daten stecken, herausfiltert, wie man zugrundeliegende Strukturen erkennt und künftige Tendenzen vorhersagt. Ziel der Vorlesung ist es, ausgehend von ausgewählten Problemstellungen statistische Grundprinzipien zur Analyse von Daten zu vermitteln, um die in Literatur und Softwarepaketen enthaltenen statistischen Verfahren sinnvoll anzuwenden. Daten werden als Werte von zufälligen Größen betrachtet und die Gültigkeit der hergeleiteten Schlüsse wird durch die Angabe von Wahrscheinlich-keiten quantifiziert. Deshalb werden neben Methoden aus der Statistik auch wichtige Grundbegriffe aus der Wahrscheinlichkeitstheorie eingeführt. Folgende Themen werden behandelt:

Testen

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Date: 13.11.2008
Lang.: de
Dur.: 01:31:20
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• Definition of a Confidence Intervall
• Approximative Confidence Intervall for a Probability
• What is a test?
• Possible errors
• Construction of a test procedur
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Date: 18.11.2008
Lang.: de
Dur.: 01:26:10
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• Transition
• Connection: Test and Confidence Interval
• t-Tests
• Realization in R
• Two-sample-t-Test
• Power of the t-Test
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Date: 20.11.2008
Lang.: de
Dur.: 01:30:11
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• Transition
• Table of binomial distribution
• binomial test for two-sided hypothesis
• Goodnes of fit test
• Q-Q-Plots
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Date: 25.11.2008
Lang.: de
Dur.: 01:28:04

Test and distribution for discrete random-values

Prof. Dr. Hannelore Liero

Test and distribution for discrete random-values

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• Formulation of the problem
• Stating the problem
• Testing homogeneity
• Contingency table
• Further Example
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Date: 27.11.2008
Lang.: de
Dur.: 01:29:37
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• Distribution of the number of runs
• Transition
• Kendalls Tau
• run test
• Excel
• 2-random sample T-Test
• Example: Alcohol
• New Segment

Regression

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Date: 02.12.2008
Lang.: de
Dur.: 01:30:45
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• Ermittlung des Median
• Berechnung des Korrelationsassistenten
• Aim
• Approaches for the estimation of f
• Inputs
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Date: 04.12.2008
Lang.: de
Dur.: 01:30:26
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• Repetition 00:13:20
• Notation Regression 00:22:30
• Properties of the l.s.e. 00:17:22
• Simple linear regression 00:06:09
• Standart errors 00:32:07
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Date: 09.12.2008
Lang.: de
Dur.: 01:30:36
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• Linear regression in R 00:14:33
• Coefficient of determination 00:13:24
• The normal regression model 00:20:16
• Example in R 00:18:30
• Prediction intervall for Ynen 00:24:52
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Date: 11.12.2008
Lang.: de
Dur.: 01:31:49
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• Confidence ellipsoids 00:10:14
• F-Test Examples 00:25:55
• Test procedure 00:08:44
• F-Distribution 00:23:21
• Normale Quantile Plot 00:11:21
• Backward procedure 00:16:10
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Date: 16.12.2008
Lang.: de
Dur.: 01:28:49
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• Realization in Excel 00:17:25
• Example Phosphateindex 00:22:10
• Weighted least squares estimator 00:11:21
• Logit model 00:16:56
• Demonstration
• Estimation Procedure 00:10:17
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Date: 18.12.2008
Lang.: de
Dur.: 01:31:42
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• The model 00:07:13
• Linear regression 00:20:32
• Monte Carlo Simulation 00:12:19
• Exercise 3 00:18:14
• Exercise 3 task 2 00:10:55
• Exercise 3 mixing music 00:16:59
• Q-Q-Plot 00:08:01

Nichtparametrische Dichteschätzung

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Date: 06.01.2009
Lang.: de
Dur.: 01:20:01
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• Outline 00:01:50
• Introduction 00:15:35
• Histograms 00:27:48
• Nearest neighbor estimation 00:14:38
• Example 00:12:50
• Properties of kernel estimators 00:08:36
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Date: 08.01.2009
Lang.: de
Dur.: 01:29:04
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• Start 00:02:34
• Mean squared error 00:23:28
• Summarizing 00:06:01
• Test statistic 00:19:50
• Normal reference band 00:09:59
• Estimation 00:13:43
• Different points of view 00:14:52

Nonparamagnetic Regression

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Date: 13.01.2009
Lang.: de
Dur.: 01:30:53
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• Organisational matters 00:03:05
• Local constant regression estimator 00:18:04
• Local linear 00:10:25
• Nonparametric regression 00:23:17
• Comparison 00:09:38
• choice of the bandwidth 00:12:55
• Plug-in Method 00:14:56
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Date: 15.01.2009
Lang.: de
Dur.: 01:30:22

Adaptive Bestimmung des Glättungsparameters

Prof. Dr. Hannelore Liero

Adaptive Bestimmung des Glättungsparameters

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• Repetition 00:08:59
• Cross validation 00:12:27
• Adaptive bandwidth selection in R 00:22:39
• Local linear estimator 00:13:59
• Additive model 00:20:03
• Summarizing Additive model 00:13:25
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Date: 19.01.2009
Lang.: de
Dur.: 01:35:00
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• Outline 00:04:01
• Nonparametric logistic models 00:24:08
• General Nonparametric additive logistic model 00:11:56
• Testing 00:24:32
• Exercice I 00:07:47
• Exercice II 00:22:49
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Date: 22.01.2009
Lang.: de
Dur.: 01:30:39
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• exercises 00:12:57
• Logistic regression : Cancer remission 00:14:02
• Mixture of normal distribution 00:11:57
• For discussion 00:12:24
• What is cluster analysis 00:09:59
• K-means algorithm 00:18:07
• Example: Illustration 00:12:37

Classificationmethod

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Date: 27.01.2009
Lang.: de
Dur.: 01:31:36
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• Clustering Approaches 00:13:04
• Dendrogramm 00:21:05
• Classification Rules 00:09:01
• Linear discriminant analysis LDA 00:20:43
• Probability of misclassification 00:13:54
• Canonical DA and Fisher\'s LDA 00:15:02
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Date: 29.01.2009
Lang.: de
Dur.: 01:31:08
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• Review 00:08:01
• Example 00:22:16
• Quadratic discriminant analysis 00:16:07
• Linear discriminant analysis 00:12:59
• Nonparametric discriminant analysis 00:21:56
• Summary 00:10:59

Test preparation

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Date: 03.02.2009
Lang.: de
Dur.: 01:32:13

Exerciseanalysis of nonparametric regression

Prof. Dr. Hannelore Liero

Exerciseanalysis of nonparametric regression

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• Organisational matters 00:04:07
• Realization of equal-distributed random-variables 00:15:15
• Graphs 00:13:10
• Nonparametric logistic 00:21:44
• Type of task-formulation 00:21:22
• Example Clusteranalysis 00:08:13
• Example discrimination analyse 00:09:45
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Date: 10.02.2009
Lang.: de
Dur.: 01:39:38
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• Start 00:02:51
• Adaptiontest 00:12:57
• Indipendence- and homogenity-test 00:24:43
• Linear model 00:20:03
• Seeding rate 00:18:39
• Logistic regression 00:21:39
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