匿名用户

1级

2016-05-01 回答

The following explaination may help U:

Two-sample F-test for equal variances

Syntax

H = vartest2(X,Y)

H = vartest2(X,Y,alpha)

H = vartest2(X,Y,alpha,tail)

[H,P] = vartest2(...)

[H,P,CI] = vartest2(...)

[H,P,CI,STATS] = vartest2(...)

[...] = vartest2(X,Y,alpha,tail,dim)

Description

H = vartest2(X,Y) performs an F test of the hypothesis that two independent samples, in the vectors X and Y, come from normal distributions with the same variance, against the alternative that they come from normal distributions with different variances. The result is H = 0 if the null hypothesis (variances are equal) cannot be rejected at the 5% significance level, or H = 1 if the null hypothesis can be rejected at the 5% level. X and Y can have different lengths. X and Y can also be matrices or n-dimensional arrays.

For matrices, vartest2 performs separate tests along each column, and returns a vector of results. X and Y must have the same number of columns. For n-dimensional arrays, vartest2 works along the first nonsingleton dimension. X and Y must have the same size along all the remaining dimensions.

H = vartest2(X,Y,alpha) performs the test at the significance level (100*alpha)%. alpha must be a scalar.

H = vartest2(X,Y,alpha,tail) performs the test against the alternative hypothesis specified by tail, where tail is one of the following single strings:

'both' — Variance is not Y (two-tailed test). This is the default.

'right' — Variance is greater than Y (right-tailed test).

'left' — Variance is less than Y (left-tailed test).

[H,P] = vartest2(...) returns the p-value, i.e., the probability of observing the given result, or one more extreme, by chance if the null hypothesis is true. Small values of P cast doubt on the validity of the null hypothesis.

[H,P,CI] = vartest2(...) returns a 100*(1-alpha)% confidence interval for the true variance ratio var(X)/var(Y).

[H,P,CI,STATS] = vartest2(...) returns a structure with the following fields:

'fstat' — Value of the test statistic

'df1' — Numerator degrees of freedom of the test

'df2' — Denominator degrees of freedom of the test

[...] = vartest2(X,Y,alpha,tail,dim) works along dimension dim of X. To pass in the default values for alpha or tail use [].

Example

Is the variance significantly different for two model years, and what is a confidence interval for the ratio of these variances?

load carsmall

[H,P,CI] = vartest2(MPG(Model_Year==82),MPG(Model_Year==76))

matlab进行F检验

2014-02-28 07:12阅读：

Kevin3981

像梦一样自由，像大地一样宽容

关注

Two-sample F-test for equal variances

Syntax

H = vartest2(X,Y)

H = vartest2(X,Y,alpha)

H = vartest2(X,Y,alpha,tail)

[H,P] = vartest2(...)

[H,P,CI] = vartest2(...)

[H,P,CI,STATS] = vartest2(...)

[...] = vartest2(X,Y,alpha,tail,dim)

Description

H = vartest2(X,Y) performs an F test of the hypothesis that two

independent samples, in the vectors X and Y, come from normal distributions

with the same variance, against the alternative that they come from

normal distributions with different vari

ances. The result is H = 0 if the null hypothesis (variances are

equal) cannot be rejected at the 5% significance level, or H = 1 if

the null hypothesis can be rejected at the 5% level. X and Y can

have different lengths. X and Y can also be matrices or n-dimensional arrays.

For matrices, vartest2 performs separate tests along each column,

and returns a vector of results. X and Y must have the same

number of columns. For n-dimensional arrays, vartest2 works along the

first nonsingleton dimension. X and Y must have the same size

along all the remaining dimensions.

H = vartest2(X,Y,alpha) performs the test at the significance level

(100*alpha)%. alpha must be a scalar.

H = vartest2(X,Y,alpha,tail) performs the test against the

alternative hypothesis specified by tail, where tail is one of the

following single strings:

'both' — Variance is not Y (two-tailed test). This is the

default.

'right' — Variance is greater than Y (right-tailed test).

'left' — Variance is less than Y (left-tailed test).

[H,P] = vartest2(...) returns the p-value, i.e., the probability of observing the

given result, or one more extreme, by chance if the null hypothesis

is true. Small values of P cast doubt on the validity of the null

hypothesis.

[H,P,CI] = vartest2(...) returns a 100*(1-alpha)% confidence

interval for the true variance ratio var(X)/var(Y).

[H,P,CI,STATS] = vartest2(...) returns a structure with the

following fields:

'fstat' — Value of the test statistic

'df1' — Numerator degrees of freedom of the test

'df2' — Denominator degrees of freedom of the test

[...] = vartest2(X,Y,alpha,tail,dim) works along dimension dim of

X. To pass in the default values for alpha or tail use [].

Example

Is the variance significantly different for two model years, and

what is a confidence interval for the ratio of these

variances?

load carsmall

[H,P,CI] = vartest2(MPG(Model_Year==82),MPG(Model_Year==76))