# Analysis of Variance

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## Analysis of Variance

A process of determining similarities between like groups. Analysis of variance is used in marketing to determine how likely groups with similar demographic backgrounds are to respond to a particular campaign.
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Factorial ANOVA values regarding the main effects of the sound class and experience of the judges, as well as the interaction between sound class and experience of the judges Main effects and interactions Results between effects Sound class F F(1.56)=9.66 P 0.00 Experience of the judges F F(1.56) = 1.82 P 0.17 Interaction between experience F F(1.56)=0.002 of the judges and sound class P 0.96 P = Probability value F = Ratio between the model and its error in the analysis of variance Figure 1.
The factorial ANOVA performed between educational levels showed differences regarding aging stereotypes.
Caption: Figure 3: 5 Hz rPAS did not induce any significant difference between the amount of ratio MU/(M + U) * 100 after the 5 Hz rPAS between dystonic patients and controls in the APB and ADM muscles (factorial ANOVA APB: F = 0.596, P = 0.562 (a); ADM: F = 3.493, P = 0.07 (b)).
Further analyses of these cognitions were conducted by a series of Factorial ANOVAs and post hoc analyses, in the form of independent samples t-tests with adjusted p = 01 (3).
Three types of inferential analytic techniques were employed to test four (4) non-directional hypotheses: the independent sample t-test, 2 x 2 factorial ANOVA and Pearson product moment correlation tests.
Necrosis was monitored by (a) morphological analysis (necrotic cells were characterized by cellular oncosis (black arrows) while apoptotic cells showed membrane blebbing (open arrowhead)) (scale bar: 25 [micro]m (BMDM)) and (b) PI labeling (n = 3 independent experiments with 2 counting regions of 150 cells/region in duplicate; ### P < 0.001 and ## P < 0.01 versus 0 [micro]g/mL; < 0.001 versus [Casp3.sup.+/+]; factorial ANOVA with genotype and treatment as category factors; Dunnett post hoc).
Results of a 3 x 2 mixed factorial ANOVA revealed a significant main effect for competition distance F(1 296) = 110.28 p less than .001 yp = .27 and a significant competition distance by gender interaction effect F(1 296) = 7.80 p less than .006 yp = .03.
Factorial ANOVA results indicated significant interaction effects for five of the seven dependent variables when severity, employment, and gender were considered, and MANOVA results indicated significant interaction for each cluster of dependent variables.
In a 2-factor (sleeves and Tanglefoot) factorial ANOVA, differences in the survival of experimental cohorts to the pupal stage with sleeves (presence vs.
Moreover, a factorial ANOVA confirms this graphical interpretation by showing significant main effects for performance of Person A (F(4,208)=145.65; p<.001) and Person B (F(4,208)=118.52; p<.001), as well as a significant interaction effect (F(16,832)=10.02; p<.001) which is entirely located in the linear-by-quadratic components (F(1,57)=7.63; p=.008 and F(1,57)=61.43; p<.001 respectively).
Table 2 - Factorial ANOVA for main and interaction effects between dependent, and independent variables Source df F Sig.

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