One-way analysis of variance ANOVA

One-way analysis of variance (ANOVA) to test the significant effect of the degrading impact on each soil characteristic was performed using the computer software, SPSS 10.0.5J (SPSS Japan Inc., Tokyo). The Dunnett T3 test was chosen as the post-hoc test. 

Significantly different (p < 0.05) by one-way analysis of variance (ANOVA) with the Least Significant Difference post hoc test using JMP program from SAS, Cary, NC on a Macintosh. Reproduced with permission from (Ischiropoulos et al., 1992a). 

Data were expressed as the mean standard error of the mean (SEM). Differences between means were determined using one-way analysis of variance (ANOVA) followed by the Tukey-Kramer post hoc comparison and two-sided t test. For comparing percentages, nonparametric tests were also applied (Mann-Whitney, Kruskal-Wallis). Differences were considered significant when p < 0.05. 

All data were presented as mean SEM. The data were evaluated statistically for differences by one-way analysis of variance (ANOVA) followed by post hoc Tukey test for multiple comparisons, p < 0.05 was regarded as significant. Calculations were done using GraphPad Prism. 

The experiments described here are principally diagnostic in nature where cellular biomass was significantly enhanced in bottles after resource (iron or light) amendment, relative to control (or other) treatments, we infer that algal growth rates in the control (or other) treatments were limited by a deficiency in that resource. The statistical significance of differences between mean values of parameters measured in different treatments were assessed using a two-tailed r-test for comparisons between two treatments, or a one-way analysis of variance (ANOVA) for comparisons between three or more treatments, at a confidence level of 95% (P = 0.05). 

Data are expressed as the means SE. Statistical significance is assessed by two-tailed unpaired Student s t-test or one way analysis of variance (ANOVA) followed by either Dunnett s test for multiple comparisons vs. control or the Newman-Keuls test for all pair-wise comparisons. Tests indicating a value of P < 0.05 indicate a statistically significant difference between groups. 

A one-way analysis of variance (ANOVA) (44, 45) was then performed on the data listed in Table V for each element. The ANOVA was followed with a Student-Newman-Keuls test (46) to determine the number of subgroupings that resulted from differences in the mean metal concentrations. Six elements (Cu, Fe, Mn, Ni, Pb, and Zn) were shown to be at significantly different concentrations when compared between the seven groupings. 

The Fcal ratio is also the result of a statistical method known as the one-way Analysis of Variance (ANOVA), which assumes the model Xij = fij + sij for each observation Xij, where Sij are the independent and normally distributed random errors (sij N(0,a)), and which has as objective to test Ho = i i = fi2 =. .. = jXk. The results of the ANOVA procedure, which include the decomposition of the total sum of squares of the deviation of all observations around the general... 

All reagents were of analytical grade unless otherwise mentioned. CRMs (marine sediment and mussel tissue), supplied by the National Institute for Environmental Studies (NIES), Tsukuba, Japan were used. Results obtained in the analysis of these CRMs are shown in Table 6.3. The results obtained were compared through one-way analysis of variance (ANOVA). 

For the first set of materials, and with the aim of assessing the dispatch conditions, a short-term stability study was conducted at 40°C. The layout chosen for the stability study was the so-called isochronous scheme samples were taken from the bulk, placed at 40° C and then moved back to the reference temperature (4°C), after 1 and 2 weeks. Then, at the same time, the samples were analysed for major components and trace elements. The results, 3 time-points (0, 1, 2 weeks) and 2 units analysed per time-point, were evaluated by one-way analysis of variance ANOVA. As some parameters (especially As, Cd, Cu, and to a minor extent also Mn, pH) showed a statistically significant slope of the regression line, it was decided to assure the dispatch of the samples at 4°C (with cooling elements). 

For homogeneity testing in natural materials, the between-bottle variability (S[,b) was evaluated following the IRMM approach (Linsinger et al., 2001), after the application of one-way analysis of variance (ANOVA) to the duplicates obtained in ten different units. 

Means and standard deviations (SD) were calculated with SPSS (Version 11.5.1, SPSS Inc., Chicago, IL, USA) statistical software. SPSS was used to verify significant differences between treatments by one-way analysis of variance (ANOVA) followed by least significant difference test (LSD) at p < 0.05 to identify differences among groups. 

Statistical calculations were carried out using Stata 8 (Stata Corp LP 2005) as a program to analyze data. To verify the homogeneity of variance, the Barlett test was applied, then One-way analysis of Variance (ANOVA) was performed, and significant differences (P < 0.05) among samples were determined with Tukey s honestly significant difference range. 

The difference of the log assay response between the assay runs was assessed using a one-way analysis of variance (ANOVA) with assay runs as fixed effect (Fig. 8.A3). This analysis showed that the mn means were not statistically significantly different... 

One-way analysis of variance (ANOVA) was used for assessment of the differences among groups and subgroups of subjects. Spearman s rank order correlations were used for analyses of the relationships between isoflavones and thyroid hormone parameters. 

When the effects of multiple doses of a cannabinoid on the response to the single dose A of the contractile agent are investigated, the size of each contraction produced in the presence of the cannabinoid can be compared to the size of the initial contraction evoked in the absence of the cannabinoid. This may be achieved by normalizing the data such that, for example, each contractile response elicited in the presence of the cannabinoid is expressed as a percentage of the initial contractile response, normalized to 100%. The effects of different cannabinoid doses can be compared with the initial contraction by performing a one-way analysis of variance (ANOVA) followed by a post hoc test such as Dunnett s test. 

Results are expressed as mean values SEM. Statistical analysis was performed by one-way analysis of variance (ANOVA) for repeated measurements, or by Student s t test with Bonferroni correction for multiple comparisons, where appropriate. Statistical significance was set at P < 0.05. 

Statistical analysis of Kie data was performed by means of one-way analysis of variance (ANOVA) and multiple comparisons were performed using Tukey s post-hoc test at a pre-set significance level of 5%. The results in the text are shown as average + one standard deviation, except when indicated. 

With three or more groups of observations to compare, it is incorrect to compare each pair of groups with a two-sample r-test. Instead, a one-way analysis of variance (ANOVA) should be carried out. The ANOVA technique can be generalized to deal with observations from many other types of experimental design. In each case, the analysis separates out the variation due to specified components of variation (e.g. the differences between group means in a one-way ANOVA) and the variation due to the residual or error terms. The former components of variation are then compared with the latter component to see if the systematic components are too large to have arisen by chance. 

Because of the inherent variability in the length and branching neurites of control as well as experimental neurons, we use one-way analysis of variance (ANOVA) for statistical analysis of the data. Analysis of variance allows comparison of variability within as well as between samples, resulting in a more complete assessment of significant changes. 

A sub-set of four samples from each treatment was used for each analysis. Statistical analysis of data was carried out using one way analysis of variance (ANOVA). Differences among mean values were established using the least significant difference (LSD) multiple range test (Steel and Torrie, 1980). Values were considered significant when p<0.05. 

The Michaelis-Menten model was fitted to the experimental data using standard nonlinear regression techniques to obtain estimates of and K (Fig. 4.1). Best-fit values of and K of corresponding standard errors of the estimates plus the number of values used in the calculation of the standard error, and of the goodness-of-fit statistic are reported in Table 4.3. These results suggest that succinate is a competitive inhibitor of fumarase. This prediction is based on the observed apparent increase in Ks in the absence of changes in Vmax (see Table 4.1). At this point, however, the experimenter cannot state with any certainty whether the observed apparent increase in Ks is a tme effect of the inhibitor or merely an act of chance. A proper statistical analysis has to be carried out. For the comparison of two values, a two-tailed t-test is appropriate. When more than two values are compared, a one-way analysis of variance (ANOVA),... 

All extractions and determinations were conducted in triplicate. Data is expressed as meaniS.D. The means were compared by using the one-way analysis of variance (ANOVA) followed by Duncan s multiple range tests. The differences between individual means were deemed to be significant atp< 0.05. A principal component analysis (PCA) was performed in order to discriminate between different maturity stages and irrigation regime on the basis of their fatty acids composition. 

Subsequently, the ratios of the inverted Cl widths were correlated to those of the face. The following statistical tests were used to describe the features of sample population, and the control, and to measure the presence of difference among the ethnic groups or in gender descriptive statistics, one way analysis of variance (ANOVA), P or NP correlation, and Chi square (p<0.05). 

---