Lineare Einfachregression - Die Methode der kleinsten Quadrate; Prognosen und Residualanalyse
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Um die Abhängigkeit von zwei oder mehreren Variablen zu untersuchen, gibt es in der Statistik mehrere Methoden. Neben der Varianzanalyse und dem Chi-Quadrat-Unabhängigkeitstest gibt es unter anderem die Regressionsanalyse, welche in dieser Seminararbeit behandelt wird. In der Regressionsanalyse wird zwischen der linearen und der nichtlinearen Regression unterschieden. Im Folgenden wird nur auf die lineare Regression eingegangen und dabei auch nur auf die lineare Einfachregression, bei der die Abhängigkeit von zwei Variablen untersucht wird, im Gegensatz zur multiplen linearen Regression, bei der mehr als zwei Variablen betrachtet werden. Die hier beschriebene Methode der kleinsten Quadrate wird in der Regressionsanalyse als Standardverfahren angewandt. Zu gegebenen Messdaten wird eine Gerade gesucht, die möglichst optimal durch die gegebenen Daten verläuft. Die Methode der kleinsten Quadrate wird anhand einer Beispielsituation errechnet. Zur besseren visuellen Veranschaulichung wird dazu eine Regressionsgerade in ein Streudiagramm eingezeichnet. Anschlie end wird die Richtigkeit der Annahme durch die Methode der kleinsten Quadrate anhand ihrer Eigenschaften belegt. Zuletzt werden in der Residualanalyse und mit Hilfe des Bestimmtheitsma es die zuvor getroffenen Annahmen bewiesen.
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