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1Gliederung der Vorlesung
1) Grundlagen
2) Datendeskription
3) Zusammenhangsanalyse und Regression
4) Wahrscheinlichkeiten, Zufallsgröÿen
a) Zufällige Ereignisse, Wahrscheinlichkeit
b) Bedingte Wahrscheinlichkeit, Unabhängigkeit
c) Zufallsgröÿen, Wahrscheinlichkeitsverteilungen: Kapitel 6.4, 6.5.
i) diskret: Beispiel Binomialverteilung
ii) stetig: Beispiel Normalverteilung
iii) Grenzwertsätze
5) Induktive Statistik
HTW Berlin
, 2
Beispiel stetige Zufallsvariablen: Wert des DAX
Im letzten Kapitel haben wir Zufallsvariablen mit diskreten Werten betrachtet (z.B.
Gewinn, kein Gewinn). Aber was machen wir bei stetigen Zufallsvariablen?
Beispiel: Wert des DAX
DAX Kurs 2008−01−−05−13
12000 12000
10000 10000
8000 8000
6000 6000
4000 4000
Jan 02 Sep 01 Mai 04 Jan 04 Sep 01 Mai 02 Jan 02 Sep 03 Mai 02 Jan 02 Sep 01 Mai 04 Jan 04 Sep 01 Mai 02 Jan 02 Sep 03 Mai 02
2008 2008 2009 2010 2010 2011 2012 2012 2013 2014 2014 2015 2016 2016 2017 2018 2018 2019
HTW Berlin
, 3
Beispiel stetige Zufallsvariablen: Mietpreise
Beispiel: Quadratmeter-Miete einer 4-Zimmer-Wohnung in Pankow:
Histogramm Normal
30 Mittelwert = 8,11
Std.-Abw. = 1,201
N = 72
20
Prozent
10
0
6,00 8,00 10,00 12,00
Quadratmeterpreis
Man könnte für jeden Quadratmeterpreis 8,00 e /m2 , 8,01 e /m2 , ... eine
Wahrscheinlichkeit angeben. Aber macht das Sinn?
HTW Berlin
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