Scheffe test

Data are presented as mean SEM. Tail flick latencies and paw pressure thresholds were converted to the percentage of maximal possible effect. The area under the time-effect curve was calculated by accumulating the effect measured at discrete time intervals using the trapezoidal integration method. The results were analyzed by ANOVA with repeated measures followed by Scheffe and Dunnett tests. The injury score for each technique and each solution was compared using two-way ANOVA followed by the Scheffe test. The frequency (i.e., the number of rats with lesions) in each group was analyzed by chi-square test. 

The Scheffe test (28) is a method for performing multiple comparisons between group means. Means differing by more chan the value given are significantly different (P < 0.05). As It assumes all possible comparisons are performed, it Is regarded as a conservative test. 

Table 19 ANOVA and Scheffe test for industry by industry...

Scheffe s is another post hoc comparison method for groups of continuous and randomly distributed data. It also normally involves three or more groups (Scheffe, 1959 Harris, 1975). It is widely considered a more powerful significance test than Duncan s. 

The Scheffe procedure is powerful because of it robustness, yet it is very conservative. Type I error (the false positive rate) is held constant at the selected test level for each comparison. 

Scheffe s test). No hepatocellular nodules were observed in mice receiving carbon tetrachloride alone (Dragani et al., 1986). 

Figure 2. Effects of local administration of TTX, alone and in combination with 10 pM imetit, on 100 mM potassium-evoked release of ACh from the cortex of freely moving rats. At 40 (Si) and 140 (S2) min the perfusion medium was changed from 4 to 100 mM KC1 for 10 min after equilibration. TTX was added 20 min, and imetit 10 min before S2 stimulation to the perfusion medium. Both remained throughout the S2 stimulation. Shown are means S.E. of (n) experiments. The presence of significant treatment effects was determined by one-way analysis of variance followed by Scheffe s test. P < 0.001 vs. control.

It has been explained that when testing mixture diagrams, factor space is usually a regular simplex with q-vertices in a q-1 dimension space. In such a case, the task of mathematical theory of experiments consists of determining in the given simplex the minimum possible number of points where the design points will be done and based on which coefficients of the polynomial that adequately describes system behavior will be determined. This problem, for the case when there are no limitations on ratios of individual components, as presented in the previous chapter, was solved by Scheffe in 1958 [5], However, a researcher may in practice often be faced with multicomponent mixtures where definite limitations are imposed on ratios of individual components ... 

Data are expressed as the percentage change in current parameters induced by Ro 15-4513 (3 fiM) and are mean S.E.M. for the indicated numbers (n) of neurons in hippocampal slices isolated from rats in estrus, at day 19 of pregnancy, or at 2 days after delivery. P < 0.05 versus respective control response (one-way ANOVA followed by Scheffe s test). Reproduced with permission from Sanna et al. (2009). 

Figure 11 Average values (n = 6, error bars = SD) of the shift in the peak maximum of the C-H asymmetric stretching absorbance plotted as a function of time during the ethanol liquid treatment protocol. ANOVA followed by Scheffe s F-test revealed a statistically significant difference P < 0.05) between the location of the peak maximum before and after the 30-min ethanol exposure. There was no statistically significant difference between the baseline value and that at 24 hr. (From Ref. 125. Reprinted from Journal of Controlled Release, 16, Bommannan et al. Examination of the effect of ethanol on human stratum comeum in vivo using infrared spectroscopy, pp. 299-304, 1991, with kind permission from Elsevier Science, NL, Sara Burgerhartstraat 25, 1055 KV, Amsterdam, The Netherlands.)...

If you wish to compare the variances of two sets of data that are normally distributed, use the F-test. For comparing more than two samples, it may be sufficient to use the F max-test, on the highest and lowest variances. The Scheff Box (log-ANOVA) test is recommended for testing the significance of differences between several variances. Non-parametric tests exist but are not widely available you may need to transform the data and use a test based on the normal distribution. 

Designs in this case, primarily attributed to Scheffe, are derived very simply. That shown in Fig. 7 for three eomponents is suitable for first-, second-, and partial third-order models. The latter is the central composite design and is quite eommonly used. Test points for eheeking model fit are also shown. 

Fig. 7 Scheffe central composite design for three factors. Open squares are test points.

Statistical Analysis. All data for a particular donor were normalized with respect to the hydrophobic glass at 3 min of platelet-poor plasma exposure for that donor, that is, platelet adhesion at 3 min of platelet-poor plasma exposure to hydro-phobic glass was 100%. A one-way analysis of variance was performed, using each material and time of platelet-poor plasma exposure as a variable. For each material and time, the normalized platelet counts for all the donors were summed, and Scheffe s multiple comparisons were performed. For the difference between the two SBS morphologies, a student s t test was used. Data are presented as the mean of the normalized average from each donor and the pooled standard deviation. 

Equation 9.8 suggests the use of a 2 factorial design to study the effect of the temperature. Equation 9.9 would require a first-order Scheffe design at each temperature (simplex vertices). In fact two independent measurements of solubility were carried out at each point. Also unreplicated test points were set up at the midpoints of the binary mixtures (points 7-12) that would allow use of a more complex model, if necessary. The resulting design is given in table 9.14. 

Typical one-way ANOVA with post hoc tests LSD, Bonferroni, Duncan s, Sidak s, Scheffe, Tukey, Tukey s-b, R-E-G-W-K R-E-G-W-Q, S-N-K. Waller-Duncan Levene s homogeneity of variance... 

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