Sum of (squared) bias, a variance and a constant noise term. There is a trade-off between bias and variance – Very flexible models have low bias and high variance – Rigid models have high bias and low variance – Optimal model has has the best balance 10.
- Given Variance: 6.25, Mean: 206, Sample: 90, find the probability that the mean of the sample would differ by more than.3. Calculate the mean for samples for which the sample size and x are given.
- In artificial neural networks, the variance increases and the bias decreases as the number of hidden units increase, although this classical assumption has been the subject of recent debate. Like in GLMs, regularization is typically applied. In k-nearest neighbor models, a high value of k leads to high bias and low variance (see below).
- Sep 22, 2017 The higher the standard deviation the more variability or spread you have in your data. Standard deviation measures how much your entire data set differs from the mean. The larger your standard deviation, the more spread or variation in your data. Small standard deviations mean that most of your data is clustered around the mean. In the following graph, the mean is 84.47, the standard.
This lecture presents some examples of Hypothesis testing, focusing on tests of hypothesis about the variance, that is, on using a sample to perform tests of hypothesis about the variance of an unknown distribution.
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Normal IID samples - Known mean
In this example we make the same assumptions we made in the example of set estimation of the variance entitled Normal IID samples - Known mean. The reader is strongly advised to read that example before reading this one.
The sample
The sample is made of independent draws from a normal distribution having known mean and unknown variance . Specifically, we observe realizations , .., of independent random variables, .., , all having a normal distribution with known mean and unknown variance . The sample is the -dimensional vector , which is a realization of the random vector.
The null hypothesis
We test the null hypothesis that the variance is equal to a specific value :
The alternative hypothesis
We assume that the parameter space is the set of strictly positive real numbers, i.e., . Therefore, the alternative hypothesis is
The test statistic
To construct a test statistic, we use the following point estimator of the variance:
The test statistic isThis test statistic is often called Chi-square statistic (also written as -statistic) and a test of hypothesis based on this statistic is called Chi-square test (also written as -test).
What Does Low Variance Mean Math
The critical region
Let and . We reject the null hypothesis if or if . In other words, the critical region isThus, the critical values of the test are and .
The power function
The power function of the test iswhere is a Chi-square random variable with degrees of freedom and the notation is used to indicate the fact that the probability of rejecting the null hypothesis is computed under the hypothesis that the true variance is equal to .
The power function can be written aswhere we have definedAs demonstrated in the lecture entitled Point estimation of the variance, the estimator has a Gamma distribution with parameters and , given the assumptions on the sample we made above. Multiplying a Gamma random variable with parameters and by one obtains a Chi-square random variable with degrees of freedom. Therefore, the variable has a Chi-square distribution with degrees of freedom.
The size of the test
When evaluated at the point , the power function is equal to the probability of committing a Type I error, i.e., the probability of rejecting the null hypothesis when the null hypothesis is true. This probability is called the size of the test and it is equal to where is a Chi-square random variable with degrees of freedom (this is trivially obtained by substituting with in the formula for the power function found above).
Normal IID samples - Unknown mean
This example is similar to the previous one. The only difference is that we now relax the assumption that the mean of the distribution is known.
The sample
In this example, the sample is made of independent draws from a normal distribution having unknown mean and unknown variance . Specifically, we observe realizations , .., of independent random variables , .., , all having a normal distribution with unknown mean and unknown variance . The sample is the -dimensional vector , which is a realization of the random vector .
The null hypothesis
We test the null hypothesis that the variance is equal to a specific value :
The alternative hypothesis
We assume that the parameter space is the set of strictly positive real numbers, i.e., . Therefore, the alternative hypothesis is
The test statistic
We construct a test statistic by using the sample meanand either the unadjusted sample varianceor the adjusted sample variance
The test statistic isThis test statistic is often called Chi-square statistic (also written as -statistic) and a test of hypothesis based on this statistic is called Chi-square test (also written as -test).
The critical region
Let and . We reject the null hypothesis if or if . In other words, the critical region isThus, the critical values of the test are and .
The power function
The power function of the test iswhere the notation is used to indicate the fact that the probability of rejecting the null hypothesis is computed under the hypothesis that the true variance is equal to and has a Chi-square distribution with degrees of freedom.
The power function can be written aswhere we have definedGiven the assumptions on the sample we made above, the unadjusted sample variance has a Gamma distribution with parameters and (see Point estimation of the variance), so that the random variablehas a Chi-square distribution with degrees of freedom.
The size of the test
The size of the test is equal to where has a Chi-square distribution with degrees of freedom (this is trivially obtained by substituting with in the formula for the power function found above).
Solved exercises
Below you can find some exercises with explained solutions.
Exercise 1
Denote by the distribution function of a Chi-square random variable with degrees of freedom. Suppose you observe independent realizations of a normal random variable. What is the probability, expressed in terms of , that you will commit a Type I error if you run a Chi-square test of the null hypothesis that the variance is equal to , based on the observed realizations, and choosing and as the critical values?
The probability of committing a Type I error is equal to the size of the test:where has a Chi-square distribution with degrees of freedom. ButThus,If you wish, you can utilize some statistical software to compute the values of the distribution function. For example, with the MATLAB commands chi2cdf(65,39) and chi2cdf(15,39) we obtainAs a consequence, the size of the test is
Exercise 2
Make the same assumptions of the previous exercise and denote by the inverse of . Change the critical value in such a way that the size of the test becomes exactly equal to .
Replace with in the formula for the size of the test:You need to set in such a way that . In other words, you need to solvewhich is equivalent toProvided the right-hand side of the equation is positive, this is solved byIf you wish, you can compute numerically. From the previous exercise we know thatTherefore, we need to computeIn MATLAB, this is done with the command chi2inv(0.0444,39), which gives as a result
Exercise 3
Make the same assumptions of Exercise 1 above. If the unadjusted sample variance is equal to 0.9, is the null hypothesis rejected?
In order to carry out the test, we need to compute the test statisticwhere is the sample size, is the value of the variance under the null hypothesis, and is the unadjusted sample variance.
Thus, the value of the test statistic isSince and , we have thatIn other words, the test statistic does not exceed the critical values of the test. As a consequence, the null hypothesis is not rejected.
How to cite
Please cite as:
Taboga, Marco (2017). 'Hypothesis tests about the variance', Lectures on probability theory and mathematical statistics, Third edition. Kindle Direct Publishing. Online appendix. https://www.statlect.com/fundamentals-of-statistics/hypothesis-testing-variance.
Definition: Volume variance is the difference between the total budgeted overhead costs and the actual amount of overhead costs allocated to production processes using the fixed overhead rate as a result of a difference in budgeted and actual production volume. This variance occurs when the actual volume of products produced differs from the budgeted or estimated production schedule.
What Does Volume Variance Mean?
Since most budgets rely on a fixed production amount to determine cost structures, changes in the actual production output will also change the cost structure. For instance, during the budget making process management estimates fixed overhead costs like insurance and taxes. These overhead rates are allocated to the products being produced during the period.
Example
What Does A Low Variance Mean
In most cases the budgeted overhead amount is the same regardless of the production volume as long as the production volume is within the relevant range of production.
At the end of the period, management reconciles the actual business performance with the budgeted or estimated performance.
Management also reconciles the amount of fixed overhead that was allocated to each process with the actual overhead that was incurred for the period. Since the estimates rarely are completely accurate, there is usually a difference between the actual overhead costs incurred during the period and the estimated overhead that was allocated during the period. These differences are considered volume variances because the overhead cost difference occurred as a result of a production volume difference.
Fixed overhead variances also include spending variances. Variable overhead variances include efficiency variances.
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