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|Skalenniveaus|![Skalenniveaus](bilder/skalenniveaus.png)|
|Mittelwert|Summe aller Werte dividiert durch die Anzahl von Werten. $$\huge \bar{x} = \frac{\sum_{i=1}^{n} x_i}{n}$$|
|Median|Mittlerer Wert der sortierten Liste. $$\huge \text{Median} = \begin{cases}x_{\left(\frac{n+1}{2}\right)}, & \text{wenn } n \text{ ungerade ist} \\ \\ \frac{x_{\left(\frac{n}{2}\right)} + x_{\left(\frac{n}{2}+1\right)}}{2}, & \text{wenn } n \text{ gerade ist}\end{cases}$$|
|Varianz|$$\displaystyle\huge Varianz = s^2 = \frac{\sum_{i=1}^{n} (x_i - \bar{x})^2 }{n - 1}$$|
|Standardabweichung|$$\displaystyle\huge Standardabbwichung = s = \sqrt{Varianz}$$|
| Mittlere Absolute Abweichung | $$\displaystyle\huge \text{Mittlere Absolute Abweichung} = \frac{\sum_{i=1}^{n} \lvert x_i - \bar{x} \rvert}{n - 1}$$ |
|Varianz|$$\huge Varianz = s^2 = \frac{\sum_{i=1}^{n} (x_i - \bar{x})^2 }{n - 1}$$|
|Standardabweichung|$$\huge Standardabbwichung = s = \sqrt{Varianz}$$|
| Mittlere Absolute Abweichung | $$\huge \text{Mittlere Absolute Abweichung} = \frac{\sum_{i=1}^{n} \lvert x_i - \bar{x} \rvert}{n - 1}$$ |
## Lineare Regression
## Klassifikation
## Unsupervised Learning Clustering