Variance testing

This PROC TTEST runs a two-sample f-test to compare the LDL change-from-baseline means for active drug and placebo. ODS OUTPUT is used to send the p-values to a data set called pvalue and to send the test of equal mean variances to a data set called variance test. The final pvalue DATA step checks the test for unequal variances. If the test for unequal variances is significant at the alpha =. 05 level, then the mean variances are unequal and the unequal variances p-value is kept. If the test for unequal variances is insignificant, then the equal variances p-value is kept. The final pvalue data set contains the Probt variable, which is the p-value you want. 

Part 2 Analysis of Variance testing for both locations and analytical methods to determine if an overall bias exists for location or analytical method ... 

A set of Pacific open-ocean samples were analysed for iodate-iodine using both the procedure which incorporates pre-oxidation with iodine water and that which does not. Also, in a similar exercise total iodine was determined using both the method that incorporates pre-oxidation with bromine water and the catalytic method using the reaction between Ce(IV) and As(III) [81]. Variance tests showed that differences between either replicates or methods was not significant. 

Snedecor GW, Cochran WG (1967) Variance test for homogeneity of binormal distribution. Statistical methods, 6th edn. Iowa State University Press, Ames, pp 240-241... 

An excellent paper on the subject of variance tests in regression modeling has been written by DEMING and MORGAN [1979],... 

Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (Complete samples). Biometrika 52 591-611 Sidak Z (1967) Rectangular confidence regions for the means of multivariate normal distributions. J Am Statist Assoc 62 626-631... 

White (n=8) wines Rose wines (n=8) Assuming equal variances No assuming equal variances Test of variances ... 

The assessment of the accuracy of fully correlated wavefunctions by means of variance methods requires computation which only varies as N, a lower power of N, the number of electrons, than had been expected. This seems to be dependent on an indirect approach first constructed for a trans correlated method. This means that various different variance tests could be used for the assessment of the accuracy of wavefunctions calculated by the transcorrelated method developed by Handy and Boys. These would require much less equipment in programmes and computer facilities, th the original calculations of such wavefunctions. Supplementary investigations on correlated wave-functions at this level might make possible a whole variety of informative experiments on very exact wavefunctions and energies. 

Figure 5.2 Comparison of the dose-AUC relationship of R-(-)-apomorphine (11), R-(-)-N- -propylnorapomorphine (80) and R-(-)-l l-OH-N- -propylnoraporphine (12). Data represent mean values S.E.M. of 4 animals. Statistical analysis by t-test p<0.05, p<0.01, p<0.001. For comparison with R-(-)-apomorphine (11) 30 nmol/kg equal variance test failed and than Rank Sum Test followed by Mann-Whitney test was performed.

Some statistics concepts such as mean, range, and variance, test of hypothesis, and Type I and Type II errors are introduced in Section 2.1. Various univariate SPM techniques are presented in Section 2.2. The critical assumptions in these techniques include independence and identical distribution [iid) of data. The independence assumption is violated if data are autocorrelated. Section 2.3 illustrates the pitfalls of using such SPM techniques with strongly autocorrelated data and outlines SPM techniques for autocorrelated data. Section 2.4 presents the shortcomings of using univariate SPM techniques for multivariate data. 

Outliers can be tested by three different methods, i.e. Bartlett s homogeneity of variance test, Cochran s one sided outlier test and Hartley s variance ratio test. The ISO (1986) recommends Cochran s test, because (1) the two other tests cannot be applied when one of the variances in a set is zero and (2) the two other tests are very sensitive to the value of the smallest variance. Although in our opinion a zero variance may be easily overcome by setting the variance to a small finite value, the other argument is certainly true for outliers. Cochran s criteria C is given by... 

In ordinary testing the magnitude of the individual bias and random components or deviations are usually not known. Their collective effect influences each measured v, value, and this collective effect is what is normally evaluated in ordinary variance testing. 

From the relative error test to see, the precision is close to the first degree From the average variance test to see, the precision is in the first degree. From the small probability error test to see, the precision is close to the first degree From the grey absolute relevant degree to see, the precision is in the first degree. 

A simple variance test (F test) shows whether the procedural standard deviation Sxo(R) determined in routine analysis differs significantly from the procedural standard deviation Sxo prescribed in the standard. 

This test wilt have robustness properties similar to analysis of variance tests. 

For the six types of combinations, a t-test and a correlation analysis were conducted. Using LDA as a classifier, the third and fourth combinations showed a relatively low p-value (0.09) with a correlation value of 0.77. According to an analysis of variance test with the six types of combinations and 10 subjects, the accuracy trends of the subjects for the six types of combinations showed low statistical significance. However, the distributions of the six combinations for each subject showed very high statistical significance (P = 4.2E-09), indicating that the characteristics of each subject are feasible for application to biometric-method-based EEG [5]. 

Repeat starting from step 1 for two new potential controlled variables and/or different set points. Determine which combination of controlled variables produces the best overall control performance for all the feed variances by finding the pair with corresponding set points that meet the control objectives for all variances tested. 

Some validation protocols use mathematical equations and statistic principles that are relatively complex and that are not discussed here. Computer programs now handle statistical treatment of the data (variance tests, for example). The operator s job essentially consists of entering the experimental results and following a validation protocol established by a specialist in quality procedures. 

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