Statistical analyses ANOVA

Data obtained from a national scientific research project entitled Energy and protein value of diets for ruminants , were organized and statistically analysed (ANOVA) to point out differences in energy (NE, Mcal/kg dry matter) and protein (NP, NP/GP, net protein/gross protein) efficiencies values of lucerne hay (Lh) and wheat straw (Ws) in diets for lambs. 

Data analysis led to the optimal conditions 55.5°C, molar ratio of 1.0, and enzyme concentration of 4.3% (w/w) corresponding to monolaurin molar fraction (43.3%). The best measured values are closer than those obtained from the statistical analysis. ANOVA demonstrated that modeling was successful with a coefficient of determination (R2) of 0.964. The plot... 

The liquid mean residence time, tm, in counter-current mode was significantly lower than in co-current mode. Statistical analysis (Yates method of ANOVA) indicates that along with the liquid flowrate, the direction of liquid travel is the most significant factor in the liquid mean residence time. This variation in the liquid phase mean residence time, suggests an increase in short-circuiting or channeling in the counter-current mode as a result of the gas-liquid interactions. 

Table 35-4 reports ANOVA comparing the METHOD B procedure to the METHOD A procedure for combined laboratories. Thus the combined METHOD B analyses for each sample were compared to the combined METHOD A analyses for the same sample. This statistical test indicates whether there is a significant bias in the reported results for each method, irrespective of operator or location. An apparent trend is indicated using this statistical analysis, that trend being a positive bias for METHOD B as compared to... 

Statistical analysis The data were analyzed using t-test for dependent variables or when have large sample or more than two combinations oneway ANOVA. 

Statistical analysis Each sample was taken in at least 5 replicates. The statistical analysis of the photosynthetic efficiency (Fv/Fm) of the species that were examined was performed using a one way-ANOVA analysis. 

Statistical analysis For statistical analysis of the behavioral tests an analysis of variance (two-way ANOVA) was used. For the symptomatology a Fisher exact probability test or an unpaired t-test with Welch s correction was used. In all tests p values <0.05 were considered significant. 

Statistical Analysis. Statistical analyses (two-way ANOVA) were performed by using the Statistical Analysis System (SAS, 1990). Means were compared by the least significant difference (LSD) test at a = 0.05. 

Statistical Analysis. Analysis of variance (ANOVA) of toxicity data was conducted using SAS/STAT software (version 8.2 SAS Institute, Cary, NC). All toxicity data were transformed (square root, log, or rank) before ANOVA. Comparisons among multiple treatment means were made by Fisher s LSD procedure, and differences between individual treatments and controls were determined by one-tailed Dunnett s or Wilcoxon tests. Statements of statistical significance refer to a probability of type 1 error of 5% or less (p s 0.05). Median lethal concentrations (LCjq) were determined by the Trimmed Spearman-Karber method using TOXSTAT software (version 3.5 Lincoln Software Associates, Bisbee, AZ). 

Statistical analysis proceeds through two-way analysis of variance (ANOVA). The focus in this methodology is to compare the treatment groups while recognising potential centre differences. To enable this to happen we allow the treatment means and gig to be different in the different centres as seen in Table 5.2. 

The analysis of variance (ANOVA) gives information on the significant effects. Data were analyzed using the general linear model (GLM) procedure from the Statistical Analysis System (SAS Institute, Cary, NC). A discussion and explanation of the statistics involved are given by Davies [19]. 

Taguchi also suggested the use of Pareto s ANOVA [12]. This technique does not require any statistical assumption so a statistical analysis of the responses cannot be performed. Figure 2.7 shows a Pareto s ANOVA table. 

Fig. 3A-D Postischemic damage in CA1 on day 4. A Staining for the TUNEL assay showing numerous positive cells. B Statistical analysis of TUNEL+ cells per frame of 0.4 mm2 in CA1 p < 0.001, one-way ANOVA followed by Tukey-Kramer post hoc. C Staining for Fluoro-Jade demonstrating similar pattern to the TUNEL stain. D Statistical analysis of Fluoro-Jade+ cells per frame of 0.4 mm2 in CA1 p < 0.001, one-way ANOVA followed by Tukey-Kramer post hoc. Scale bar = 200 pm...

Fig. 5 Statistical analysis of NeuN+ cells in the hippocampus. In CA1, cells were counted within frames placed in the proximal, middle, and distal CA1, while in CA2-CA4 a single frame was used (see Fig. 3 A). Note the marked reduction of positive cells in CA1 (left column of graphs) contrasting the insignificant reductions in CA2-CA4 (right column of graphs). p < 0.001, one-way ANOVA followed by Tukey-Kramer post hoc...

Fig. 7A, B Statistical analysis of the density of BrdU+ in DG. A In the short-term survival group after BrdU, the density peaked on day 9. B In the long-term-survival monkey DG, density was significantly higher in postischemic compared to control brains. p < 0.001 versus controls, one-way ANOVA followed by Tukey-Kramer post hoc...

Hence, hypothesis testing (ANOVA analysis followed by multiple comparison analysis) was used to determine NOEC and LOEC values expressed as % v/v of effluent. In order to satisfy statistical analysis requirements enabling NOEC and LOEC determinations, some bioassay protocols were adjusted to make sure that there were at least three replicates per effluent concentration and at least five effluent concentrations tested. TC % effluent values were then determined as follows ... 

Perform statistical analysis utilizing repeated measures ANOVA (JMP software, Version 5 from SAS Institute, Cary, NC) to evaluate the treatment effects on tumor growth. Compare tumor weight measurements between treatment groups utilizing one-way ANOVA. 

FIGURE 10.2 (CONTINUED) and unattached larvae. N = six (6) replicates (dishes) were done for all treatments. The results of the assay are expressed as percentage settlement of the seawater (untreated) control. Data are mean + S.E. Treatments lacking error bars indicate 100% settlement in all replicates. Statistical analysis of the data (separate one-factor analysis of variance ANOVA for each of Figure 10.2A and 10.2B, followed by Tukey s post-hoc comparison among means) showed that only extracts from D. pulchra significantly deterred settlement (at both natural and twice natural concentrations). 

Fig. 3. Comparison of METH-induced CPP in single histamine receptor gene knockout mice. Mice were injected 1 mg/kg of METH or saline every other day, and confined for 30 min to a compartment designed to condition the place preference. The CPP score were calculated using the staying time of mouse in each compartment for 15 min before and after the conditioning. Each value represents the mean S.E.M. of 6-18 mice. Statistical analysis was performed by means of one-way ANOVA followed by Tukey s test ( p < 0.05, p < 0.01).

Data are reported as means S.E.M. and statistical analysis was performed by the Student s t-test or by ANOVA followed by Dunnett s post hoc test, as appropriate. Experimental data were elaborated by means of Prism 3 program (GraphPAD Software for Science, San Diego, CA, USA), and differences were considered statistically significant for P < 0.05. 

In statistics, ANalysis Of VAriance (ANOVA) is a collection of statistical models and their associated procedures in which the observed variance is partitioned into components because of different explanatory variables. The initial techniques of the analysis of variance were developed by the statistician and geneticist R.A. Fisher in the 1920s and 1930s, and are sometimes known as Fisher s ANOVA or Fisher s analysis of variance due to the use of Fisher s F-distribution as part of the test of statistical significance. 

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