Question
Actualizado en
14 nov 2016
- Ruso
-
Inglés (US)
Pregunta de Inglés (US)
¿Cómo dices esto en Inglés (US)? How do you spell it in words, for example, when you must read similar formula aloud?
¿Cómo dices esto en Inglés (US)? How do you spell it in words, for example, when you must read similar formula aloud?

I read a book in English about statistics and I have to read some pages aloud to my English teacher, but I have no idea how to read mathimatical formulas and operations in English. MSE stands for mean squared error. It's a way to measure how well predictions of our statistical model actually match the observed data.
Respuestas
14 nov 2016
Respuesta destacada
- Árabe
- Inglés (US) Casi fluido
Im not sure but the closest i can say is: MSE equals one over n times sigma (yi minus f times (xi to the power of two))
Ps: the f could stand for frequency or fi which is f1+f2+f3+...fn
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- Portugués (Brasil)
como você soletra isso nas palavras por exemplo....
- Árabe
- Inglés (US) Casi fluido
Im not sure but the closest i can say is: MSE equals one over n times sigma (yi minus f times (xi to the power of two))
Ps: the f could stand for frequency or fi which is f1+f2+f3+...fn
- Ruso
@MoeAyman: Thanks a lot! f(xi) is a function f of the argument xi. And ^ above f means that it's our estimate of f, because we don't know a true f, we can just suppose that f is approximately equal to estimate of f.
Example at the picture: black curve - true f (usually we don't know it), dots - observations (usually it's all we have to make estimate of f), green and blue curves and an orange line are different estimates of f. The best one is blue curve, because it's closest one to true f, but green curve have the smallest MSE, so it's closest to observations (dots). There is a problem between blue and green curves, becase computer can estimate f like green curve, according to the smallest MSE. But it's not true form of function f. This problem is known in statistical learning (or machine learning) as overfitting the data.
Example at the picture: black curve - true f (usually we don't know it), dots - observations (usually it's all we have to make estimate of f), green and blue curves and an orange line are different estimates of f. The best one is blue curve, because it's closest one to true f, but green curve have the smallest MSE, so it's closest to observations (dots). There is a problem between blue and green curves, becase computer can estimate f like green curve, according to the smallest MSE. But it's not true form of function f. This problem is known in statistical learning (or machine learning) as overfitting the data.

- Árabe
- Inglés (US) Casi fluido
@Alena645: aha will i never got that advanced in statistics and only focusing on business statistics, but i hope i helped in some shape or form
- Ruso
- Inglés (US)
- Indonesio
A more formal way to say it:
"Mean squared error is equal to one over n times sigma for i running from 1 to n of y sub i minus f hat of x sub i squared."
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- Ruso
- Inglés (US)
- Indonesio
@Alena645 No problem! You can always choose to say sigma, or just sum or even summation. Some people use sum or summation, though. Nonetheless, saying sigma is still correct.
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