What is the homogeneity test of variance???

If the dispersion degree of the two variances is too different, it means that the dispersion degree of the two groups of data is inconsistent, which is called heterogeneity; For example, two samples with a capacity of 30, one for children and one for adults, take an intelligence test, and then check whether there is a significant difference between adults and children about the results of this test. There are all levels of children, and there are all levels of adults. However, if the selected adults are mentally handicapped and children are geniuses, then the conclusion that there is a significant difference between adults and children is not significant because adults are mentally handicapped and children are geniuses.

If we ensure that there are smart, average, and benzene among adults, and the same is true for children, all levels are available, so that the inference is generally more reasonable. Therefore, if the two samples are about the same degree of dispersion, we assume that their levels are comparable relative to their internal level. When the sample size is relatively small, it is necessary to use the unbiased estimator of variance for comparison, and when the sample size is large, the two variances are directly used to get along, and the result is that the difference of 1 is farther, and the dispersion degree of the two samples is far apart, and there is no way to test the hypothesis without relying on the general, because there is no reference value after testing.

Psychological and educational statistics, right? If yes, look at page 246 to understand.

Homogeneity test for varianceThe interpretation of the results is:

In general, as long as the sig value is greater than the assumption of variance homogeneity, the assumption of variance homogeneity is valid, hence the ANOVA.

The results should be trustworthy; If the SIG value is less than or equal to, the assumption of the homogeneity of the variance is questionable, leading to doubts about the results of the ANOVA.

The commonly used methods for homogeneity testing of variance are:

Hartley's test, Bartlett test, modified Bartlett test. There are three assumptions in ANOVA, one of which is that the overall variance at different levels is equal.

Because of the f-test.

The deviation of the homogeneity of variance is sensitive, so the homogeneity test of variance is necessary. The basic principle is to make some kind of assumption about the characteristics of the population, and then make inferences about whether the hypothesis should be rejected or accepted through statistical reasoning from sampling studies.

1. Variance homogeneity is a classic concept in statistics, and its essential meaning is that for two or more populations that we are going to examine or analyze, the data has the degree of consistency characterized by the degree of dispersion.

2. Generally speaking, it can be understood as the consistency degree of the data distribution of population 1 and the data distribution of population 2. Variance homogeneity is hypothesis testing.

and analysis of variance and many other statistical processes.

3. Homogeneity test of variance.

It is a method of mathematical statistics to check whether the population variance of different samples is the same, and the basic principle is to make some kind of assumption about the characteristics of the population, and then make inferences about whether this hypothesis should be rejected or accepted through statistical reasoning from sampling studies.

Homogeneity test for varianceSignificance is greater than that of the representative variance.

If the dispersion degree of the two variances is too different, it means that the dispersion degree of the two groups of data is inconsistent, which is called heterogeneity; For example, two samples with a capacity of 30, one for children and one for adults, take an intelligence test, and then check whether there is a significant difference between adults and children about the results of this test.

Brief introduction. The homogeneity test of variance is the analysis of the large difference of square fibers.

is a condition for the application of the principle of variance additiveness. The homogeneity test of variance is a test of whether the variance of two samples is the same. The homogeneity test of variance and the difference test of the mean of the two samples are tested in the hypothesis test.

There is no difference in the basic idea. It's just that the distribution of the selected sampling is different. The sampling distribution selected for the variance homogeneity perpendicularity test is f-distribution.

1.Variance homogeneity, also known as variance homogeneity, homoscedasticity, and variance consistency, shows that there is no statistically significant difference between the parties tested at a given level of significance.

2.The variance of the same beat is a classical linear regression.

One of the important assumptions is that the random error term (the distractor) in the population regression function has a constant variance under the condition of an explanatory variable.

3.In econometrics, a set of random variables with homoscedasticity is the least squares method of linear regression.

The residual value of follows a normal distribution with a mean of 0 and a variance of 2.

That is, its distractors must obey a random distribution.

4.The difference of the heterosquare empty pose corresponding to the sum indicates that the distractor term does not satisfy the normal distribution of the mean value of 0 and the variance of 2.

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How to see the results of the homogeneity test of pro-variance is as follows; If the significance is significant here), the f-test, which represents the homogeneity of the variance, is also called the homogeneity test of variance. The f-test is used in the two-sample t-test. Samples are randomly selected from two study populations, and when comparing the two samples, it is necessary to first determine whether the variance of the two populations is the same, i.e., the homogeneity of variance.

If the variance of the two populations is equal, the t-test is used directly, and if not, t can be used'Methods such as tests or variable transformations or rank sum tests. To determine whether the variance of the two populations is equal, the f test can be used. To put it simply, it is to test whether there is a significant difference in the variance of the two samples, which is a prerequisite for choosing a t-test (equal variance two-sample test, heteroskedasticity two-sample test).

The F-test was proposed by the British statistician Fisher, mainly by comparing the variance of the two sets of data S 2 to determine whether there is a significant difference in their precision. As for whether there is a systematic error between the two sets of data, the t-test is performed after the f-test is performed and there is no significant difference in their precision. Hope mine can help you <>

Do you have any other questions?

How to look at the results of the homogeneity test of variance.

Hello. Kiss! Hello, happy to answer your <>

How to see the results of the homogeneity test of pro-variance is as follows; If the significance is significant here), the f-test, which represents the homogeneity of the variance, is also called the homogeneity test of variance. The f-test is used in the two-sample t-test. When comparing the two samples, it is necessary to first determine whether the variance of the two populations is the same, that is, the homogeneity of the variance.

If the variance of the two populations is equal, then the direct round is tested by t, and if it is unequal, t can be used'Methods such as tests or variable transformations or rank sum tests. To determine whether the variance of the two populations is equal, the f test can be used. To put it simply, it is to test whether there is a significant difference in the variance of the two samples, which is a prerequisite for choosing a t-test (equal variance two-sample test, heteroskedasticity two-sample test).

The F-test was proposed by the British statistician Fisher, mainly by comparing the variance of the two sets of data S 2 to determine whether there is a significant difference in their precision. As for whether there is a systematic error between the two sets of data, the t-test is performed after the f-test is performed and there is no significant difference in their precision. Hope mine can help you <>

Do you have any other questions?

Is there a question on education statistics and measurement, can you help with it, paid.

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t-test. process, is the significance of the difference in the mean (mean) between the two samples.

Conduct inspections. However, the t-test needs to know whether the variances of the two populations are equal; The calculation of the t-test value will vary depending on whether the variance is equal or not. In other words, the t-test depends on the equality of variances.

Therefore, while SPSS conducts T-Test for Equality of Means, it should also be carried out in a rough manner's test for equality of variances 。

In levene'The f value in the S Test for Equality of Variances column is , sigFor. 128, indicating the homogeneity test of variance.

There is no significant difference, i.e., equal variances, so the results of the t-test in the following t-test table should be looked at the data in the first row, that is, the results of the t-test in the case of homogeneity.

In the case of the first row (variances=equal) in the t-test for equality of means: t=, df=84, 2-tail sig=000, mean difference=

Since sig=000, that is, the difference between the means of the two samples is significant!

In the end, it depends on which levene'SIG in the S Test for Equality of Variances column, or see the SIG in T-Test for Equality of Means(2-tailed) Huh?

The answer is: both.

Let's start with Levene's test for equality of variances, if there is no significant difference in the homogeneity test of variance, i.e., equal variances, then the results of the following t-test should look at the data in the first row, that is, the results of the t-test in the case of homogeneity.

Conversely, if there is a significant difference in the homogeneity test of variance, i.e., unequal variances, then the results of the subsequent t-test should be shown in the results table of the second row, i.e., the results of the t-test in the case of uneven variance.

You do a t-test, why is there an f-value?

This is because to evaluate whether the variances of two populations are equal, it is necessary to do levene's test for equality of variances, to test the variance of the slag, so there is an f value.

If the significance of the variance homogeneity test is less than that of the variance homogeneity, the variance homogeneity is not satisfied, so the two-by-pair comparison method of variance unevenness can be selected.

In the results of the variance homogeneity test, if p is >, the variance homogeneity is considered, and the t-test looks at the results of the first row; Otherwise, the variance is considered uneven, and the t-test looks at the results of the second row. Generally, a=,p<, that is, p<, can be considered to be different.

If the sample size is large and the data is approximately normally distributed, you can directly use the uneven correction results of the t-test, that is, the t and p values of the second row. If the sample is relatively small, or if the variance is large and the data is heavily non-normally distributed, a nonparametric test should be used.

Invalid assumptions. The basic principle of the significance test is to propose the "invalid hypothesis" and test the selection of the probability (p) level of the "invalid hypothesis" to be true. The so-called "invalid hypothesis" means that when comparing the results of the experimental treatment group with the control group, it is assumed that there is no significant difference between the results of the two groups, that is, the experimental treatment has no effect on the results or is ineffective.

After statistical analysis, if it is found that the difference between the two groups is caused by sampling, the "invalid hypothesis" is valid, and the difference can be considered to be insignificant (i.e., the experimental treatment is invalid). If the difference between the two groups is not due to sampling, the "invalid hypothesis" is not valid and the difference can be considered significant (i.e., the experimental treatment is valid).

The above content refers to: Encyclopedia - Significance Test.