# AP Statistics Curriculum 2007 NonParam VarIndep

(Difference between revisions)
Jump to: navigation, search
 Revision as of 20:21, 20 October 2008 (view source)IvoDinov (Talk | contribs) (→Also see the SOCR FlignerKilleen Activity)← Older edit Revision as of 02:22, 29 November 2008 (view source)IvoDinov (Talk | contribs) (→Computational Resources: Internet-based SOCR Tools)Newer edit → Line 27: Line 27: ==Computational Resources: Internet-based SOCR Tools== ==Computational Resources: Internet-based SOCR Tools== - Still under development. + * See the [http://www.socr.ucla.edu/htmls/ana/FlignerKilleen_Analysis.html SOCR Fligner-Killeen Analysis applet]. + * See the [[SOCR_EduMaterials_AnalysisActivities_FlignerKilleen | SOCR Fligner-Killeen Activity]]. ==Examples== ==Examples==

## Revision as of 02:22, 29 November 2008

General Advance-Placement (AP) Statistics Curriculum - Variances of Two Independent Samples

## Differences of Variances of Independent Samples

It is frequently necessary to test if k samples have equal variances. Homogeneity of variances is often a reference to equal variances across samples. Some statistical tests, for example the analysis of variance, assume that variances are equal across groups or samples.

## Approach

The (modified) Fligner-Killeen test provides the means for studying the homogeneity of variances of k populations { Xi,j, for $1\leq i \leq n_j$ and $1\leq j \leq k$}. The test jointly ranks the absolute values of $|X_{i,j}-\tilde{X_j}|$ and assigns increasing scores $a_{N,i}=\Phi^{-1}({1 + {i\over N+1} \over 2})$, based on the ranks of all observations, see the Conover, Johnson, and Johnson (1981) reference below.

In this test, $\tilde{X_j}$ is the sample median of the jth population, and Φ(.) is the cumulative distribution function for Normal distribution. The Fligner-Killeen test is sometimes also called the median-centering Fligner-Killeen test.

• Fligner-Killeen test statistics:
$x_o^2 = {\sum_{j=1}^k {n_j(\bar{A_j} -\bar{a})^2} \over V^2}$,
where $\bar{A_j}$ is the mean score for the jth sample, $\bar{a}$ is the overall mean score of all aN,i, and V2 is the sample variance of all scores.

That is:

$N=\sum_{j=1}^k{n_j}$,
$\bar{A_j} = {1\over n_j}\sum_{i=1}^{n_j}{a_{N,m_i}}$, where $a_{N,m_i}$ is the increasing rank score for the ith-observation in the jth-sample,
$\bar{a} = {1\over N}\sum_{i=1}^{N}{a_{N,i}}$,
$V^2 = {1\over N-1}\sum_{i=1}^{N}{(a_{N,i}-\bar{a})^2}$.
• Fligner-Killeen probabilities:

For large sample sizes, the modified Fligner-Killeen test statistic has an asymptotic chi-square distribution with (k-1) degrees of freedom

$x_o^2 \sim \chi_{(k-1)}^2$.
• Note:
Conover, Johnson, and Johnson (1981) carried a simulation comparing different variance homogeneity tests and reported that the modified Fligner-Killeen test is most robust against departures from normality.

## Examples

Suppose we wanted to study whether the variances in certain time period (e.g., 1981 to 2006) of the consumer-price-indices (CPI) of several items were significantly different. We can use the SOCR CPI Dataset to answer this question for the Fuel, Oil, Bananas, Tomatoes, Orange Juice, Beef and Gasoline items.

## See also

SOCR Fligner-Killeen Activity provides more hands-on examples.

## References

• Conover, W. J., Johnson, M.E., and Johnson M. M. (1981), A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics 23, 351-361.

Translate this page: