Testing Differences in Population Variances

1 / 14

Testing Differences in Population Variances - PowerPoint PPT Presentation

Testing Differences in Population Variances. QSCI 381 – Lecture 42 (Larson and Farber, Sect 10.3). Recap – Testing Means. We used different tests when comparing means depending on whether we could assume that the population variances for the two populations were the same.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

PowerPoint Slideshow about ' Testing Differences in Population Variances' - leoma

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Testing Differences in Population Variances

QSCI 381 – Lecture 42

(Larson and Farber, Sect 10.3)

Recap – Testing Means
• We used different tests when comparing means depending on whether we could assume that the population variances for the two populations were the same.
• Today we identify a test which can be used to test for differences between two population variances.
The F-distribution-I
• Let and represent the sample variances of two populations. If both populations are normal and the population variances and are equal, then the sampling distribution of:

is called an .

F-distribution

The F-distribution-II
• Properties of the F-distribution:
• Positively skewed.
• The curve is determined by the degrees of freedom for the numerator and that for the denominator.
• The area under the curve is 1.
• The mean value is approximately 1.
• F-values are always larger than 0.
The Two-Sample F-test for Variances-I
• A two-sample F-test is used to compare two population variances and when a sample is randomly selected from each population. The populations must be independent and normally distributed. The test statistic is:
• where and represent the sample variances with . The degrees of freedom for the numerator is d.f.N=n1-1 and the degrees of freedom for the denominator is d.f.D=n2-1.
The Two-Sample F-test for Variances-II(Finding the rejection region for the test)
• Specify the level of significance .
• Determine the degrees of freedom for the numerator, d.f.N.
• Determine the degrees of freedom for the denominator, d.f.D.
• Determine whether this a one-tailed or a two-tailed test.
• One-tailed – look up the  F-table for d.f.N and d.f.D.
• Two-tailed – look up the /2 F-table for d.f.N and d.f.D.
The Two-Sample F-test for Variances-III(Finding the rejection region for the test)

One-tailed

Two-tailed

F0=2.901

F0=3.576

In EXCEL:

FINV(prob,dfN,dfD)

d.f.N=5; d.f.D=15; =0.05

Example-A-I
• We sample two populations. The sample variances for the two populations are 9.622 (n1=46) and 10.352 (n2=51). Test the claim that the two variances are equal (=0.1).
• H0: ; Ha:
• Determine the critical value and the rejection region.
• This is a two-tailed test.
• We reverse the order of samples 1 and 2 so that . Therefore d.f.N=50; d.f.N=45.
• The critical value is F(0.05,50,45) = 1.626.
Example-A-II
• The test statistic is:
• We fail to reject the null hypothesis
30 fish are sampled from a Marine Reserve and a fished area. Test the claim that the lengths in the Reserve are more variable than those in the fished area (assume =0.05). The data are:Example-B-I
Example-B-II
• H0: ; Ha:
• =0.05; d.f.N=29; d.f.D=29. This is a one-sided test so the rejection region is F>1.861 = FINV(0.05,29,29)
• The test statistic is:
• We reject the null hypothesis (the data provide support for the claim)
Confidence intervals for
• When and are the variances of randomly selected, independent samples from normally distributed populations, a confidence interval for is:

where FL is the left-tailed critical F-value and FR is the right-tailed critical F-value (based on probabilities of /2).

Confidence intervals for(Example-1)
• Find a 95% confidence interval for for example A.
• The lower and upper critical points for the F-distribution are computed:
• FINV(0.975,50,45)=0.564
• FINV(0.025,50,45)=1.788
Confidence intervals for(Example-2)
• The 95% confidence interval is given by: