# analysis of variance

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## analysis of variance

n.
A method for assessing the contribution of an independent variable or controllable factor to the observed variation in an experimentally observed dependent variable.

## analysis of variance

n
(Statistics) statistics any of a number of techniques for resolving the observed variance between sets of data into components, esp to determine whether the difference between two samples is explicable as random sampling variation with the same underlying population. Abbreviation: ANOVA
ThesaurusAntonymsRelated WordsSynonymsLegend:
 Noun 1 analysis of variance - a statistical method for making simultaneous comparisons between two or more means; a statistical method that yields values that can be tested to determine whether a significant relation exists between variablesANOVAstatistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parametersmultivariate analysis - a generic term for any statistical technique used to analyze data from more than one variable
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Since the start of 2019, Scisys's Media & Broadcast and Annova Systems units have operated in combination under a single management team as the Media Solutions division.
Annova LNG is making plans for up to six LNG trains at its proposed Brownsville facility, each capable of producing 1 MTPA.
Table-III: Comparison of FVC Pred, FEV1 Pred and FEV1/FVC with smoking status of Never Smoker, Ex-Smoker and Current Smoker using ANNOVA.
ANNOVA tests if there is statistically significant variation between means of different groups ([H.sub.0]: [[mu].sub.1] [not equal to] [[mu].sub.2] versus [H.sub.A]: [[mu].sub.1] [not equal to] [[mu].sub.2]), in this case whether there were broad patterns and differences in the rates that policy was included in consultant and local government authored documents.
Using procurement specialists, Annova continues to curate its range of products, ensuring that the product selection is aligned to well-mapped customer expectations.
Various optimization techniques such as Taguchi, DOE, ANNOVA, RSM are adopted to find the optimized parameter value while machining of different harder materials with EDM process.
Values in the same column with the same superscript are significantly different from each other; significance of associations was tested using Student's t-test or ANNOVA. Table 2: Knowledge of carbohydrate counting expressed with categories and its associations.
Group N Before castration Sham surgery 8 470.53 [+ or -] 5.49 Surgery 7 475.29 [+ or -] 6.15 Surgery/ stress 8 473.49 [+ or -] 4.75 Fluoxetine 7 479.15 [+ or -] 4.94 DSHH 8 469.64 [+ or -] 5.23 Group After After castration medication Sham surgery 474.32 [+ or -] 6.34 484.53 [+ or -] 5.63 Surgery 466.31 [+ or -] 5.43 488.54 [+ or -] 6.84 Surgery/ stress 457.73 [+ or -] 4.15 * 474.32 [+ or -] 6.22 (\$) Fluoxetine 462.69 [+ or -] 5.28 * 478.33 [+ or -] 6.39 (\$) DSHH 452.74 [+ or -] 6.36 * 488.16 [+ or -] 5.54 (\$) Compared with the sham surgery group, * P < 0.01, one way ANNOVA; (\$) P <0.01, t-test, compared with after castration.
Data were analyzed using SPSS version 16.0 unpaired t-test and oneway ANNOVA was used to compare age, PCI, and BMI.

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