谁会MATLAB的假设检验啊。。
t检验法：h=ttest(x,m,alpha)；x为正态总体的样本，m为均值μ0，alpha为给定的显著性水平，当部给定时为0.05。
[h,sigma,ci]=ttest(x,m,alpha,tail)；sigma为观测值的概率，当sigma为小概率时则对原假设提出质疑，ci为真正均值μ的1-alpha置信区间。
μ检验法：h=ztest(x,m,sigma,alpha)；x为正态总体的样本，m为均值μ0，alpha为给定的显著性水平，当部给定时为0.05。
[h,sigma,ci,zval]=ztest(x,m,sigma,alpha,tail)；sigma为观测值的概率，当sigma为小概率时则对原假设提出质疑，ci为真正均值μ的1-alpha置信区间，zval为统计量的值。
如何用matlab进行F检验
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))
用matlab检验一组数据是否符合正态分布
集中性正态曲线的高峰位于正中央，即均数所在的位置。对称性正态曲线以均数为中心，左右对称，曲线两端永远不与横轴相交。均匀变动性正态曲线由均数所在处开始，分别向左右两侧逐渐均匀下降。 曲线与横轴间的面积总等于1，相当于概率密度函数的函数从正无穷到负无穷积分的概率为1。即频率的总和为100%。 关于μ对称，并在μ处取最大值，在正（负）无穷远处取值为0，在μ±σ处有拐点，形状呈现中间高两边低，正态分布的概率密度函数曲线呈钟形，因此人们又经常称之为钟形曲线。 扩展资料： 正态分布具有两个参数μ和σ^2的连续型随机变量的分布，第一参数μ是服从正态分布的随机变量的均值，第二个参数σ^2是此随机变量的方差，所以正态分布记作N（μ,σ2）。 μ是正态分布的位置参数，描述正态分布的集中趋势位置。概率规律为取与μ邻近的值的概率大，而取离μ越远的值的概率越小。正态分布以X=μ为对称轴，左右完全对称。正态分布的期望、均数、中位数、众数相同，均等于μ。 σ描述正态分布资料数据分布的离散程度，σ越大，数据分布越分散，σ越小，数据分布越集中。也称为是正态分布的形状参数，σ越大，曲线越扁平，反之，σ越小，曲线越瘦高。 参考资料来源：百度百科- 正态分布 参考资料来源：百度百科-标准正态分布
怎么用matlab验证正态分布并给出正态分布的表达式？
分布的正太性检验： x为你要检验的数据。 load x histfit(x); normplot(x); 从这两个图中可以看出是否近似服从正太分布。 然后估计参数： [muhat,sigmahat,muci,sigmaci]=normfit(x); muhat , sigmahat,muci,sigmaci 分别表示均值、方差、均值的0.95置信区间、方差0.95置信区间。 现在可以用t检验法对其进行检验： 现在在方差未知的情况下，检验均值是否为mahat； [h,sig,ci]=ttest(x,muhat); 其中h为布尔变量，h=0表示不拒绝零假设，说明均值为mahat的假设合理。若h=1则相反； ci表示0.95的置信区间。 sig若比0.5大则不能拒绝零假设，否则相反。