Calcolatore della varianza
Calcola la varianza della popolazione e del campione di un insieme di dati.
Piano di ammortamento
| # | Pagamento | Capitale | Interessi | Saldo |
|---|---|---|---|---|
Visualizzazione dei primi 12 di mesi.
Crescita nel tempo
| Anno | Investito | Valore |
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Introduzione
Variance Calculator — Compute population and sample variance of a dataset. Enter Dataset (comma-separated) to get an instant, accurate result.
Formula
Population variance: σ² = Σ(x−mean)² / N. Sample variance: s² = Σ(x−mean)² / (n−1).
Passo dopo passo
- Enter the Dataset (comma-separated).
- Click Calculate to see your result instantly.
Esempio pratico
Example: With Dataset (comma-separated) = 2, 4, 4, 4, 5, 5, 7, 9, the Variance Calculator gives Population Variance: 4, Sample Variance: 4.5714, Mean: 5.
Domande Frequenti
Which variance should I use?
Use population variance if your data is the entire group of interest; use sample variance if your data is a subset used to estimate a larger population.
Why does sample variance divide by n−1 instead of n?
This is Bessel's correction — dividing by n−1 corrects the downward bias that occurs when estimating variance from a sample.
What inputs does the Variance 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 Variance Calculator?
The Variance Calculator applies the formula exactly as calculated — Population variance: σ² = Σ(x−mean)² / N. Sample variance: s² = Σ(x−mean)² / (n−1). — so results are precise for the inputs you provide. Accuracy depends on entering correct, realistic input values.
Is the Variance Calculator free to use?
Yes, the Variance Calculator is completely free, requires no signup, and runs instantly in your browser.
Informazioni su Calcolatore della varianza
The Variance Calculator uses a real, verifiable formula — Population variance: σ² = Σ(x−mean)² / N. Sample variance: s² = Σ(x−mean)² / (n−1). — so results are accurate every time, not an approximation.