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  • 2021-04-20 07:44:07

    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))

    更多相关内容
  • matlab开发-FTest

    2019-08-23 23:32:07
    matlab开发-FTest。测试是否有必要通过改进数据错误来增加模型参数。
  • 如何用matlab进行F检验

    千次阅读 2021-04-21 02:59:33
    匿名用户1级2016-05-01 回答The following explaination may help U:Two-sample F-test for equal variancesSyntaxH = vartest2(X,Y)H = vartest2(X,Y,alpha)H = vartest2(X,Y,alpha,tail)[H,P] = vartest2(...)[H,P...

    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))

    展开全文
  • F检验 matlab

    千次阅读 2020-04-15 21:37:31
  • matlab进行F检验

    2021-04-18 13:25:21
    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 variancesSyntaxH = vartest2(X,Y)H = vartest2(X,Y,alpha)H = vartest2(X,Y,alpha,tail)[H,P] = ...

    eae470ab4183c290835f7c3a8b6fdebe.png

    matlab进行F检验

    2014-02-28 07:12阅读:

    75fd2d51e93a0d565e80a07e3a01d74d.png

    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))

    展开全文
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