Rechner für die mittlere absolute Abweichung
Berechnen Sie die mittlere absolute Abweichung eines Datensatzes.
Tilgungsplan
| # | Zahlung | Kapital | Zinsen | Restschuld |
|---|---|---|---|---|
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Wachstum über die Zeit
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Einführung
Mean Absolute Deviation Calculator — Compute the mean absolute deviation of a dataset. Enter Dataset (comma-separated) to get an instant, accurate result.
Formel
MAD = Σ|x − mean| / n, the average of the absolute differences between each value and the mean.
Schritt für Schritt
- Enter the Dataset (comma-separated).
- Click Calculate to see your result instantly.
Praxisbeispiel
Example: With Dataset (comma-separated) = 2, 4, 4, 4, 5, 5, 7, 9, the Mean Absolute Deviation Calculator gives Mean: 5, Mad: 1.5.
Häufig Gestellte Fragen
How is MAD different from standard deviation?
MAD averages the absolute deviations, while standard deviation squares them first — MAD is less sensitive to large outliers.
Can MAD be negative?
No, since every deviation is converted to an absolute value before averaging, MAD is always zero or positive.
What inputs does the Mean Absolute Deviation Calculator need?
You'll need to provide: Dataset (comma-separated). All fields use sensible defaults, so you can see a working example immediately and then adjust the values to match your own numbers.
How accurate is the Mean Absolute Deviation Calculator?
The Mean Absolute Deviation Calculator applies the formula exactly as calculated — MAD = Σ|x − mean| / n, the average of the absolute differences between each value and the mean. — so results are precise for the inputs you provide. Accuracy depends on entering correct, realistic input values.
Is the Mean Absolute Deviation Calculator free to use?
Yes, the Mean Absolute Deviation Calculator is completely free, requires no signup, and runs instantly in your browser.
Über Rechner für die mittlere absolute Abweichung
The Mean Absolute Deviation Calculator uses a real, verifiable formula — MAD = Σ|x − mean| / n, the average of the absolute differences between each value and the mean. — so results are accurate every time, not an approximation.