Statistical test multiple-range

The grading of reflection reports from the three student populations is analyzed and correlated with a number of other statistics and performance indicators. All data were collected in Microsoft Excel and unported into Statgraphics Centurion XV for further analysis [7], We performed ANOVA tests, multiple range test or Kruskal-Wallis tests, and f-tests on the gathered information. 

Urine, feces and food were analyzed for calcium content by atomic absorption spectrophotometry. Data were subjected to statistical analysis by analysis of variance and Duncan s Multiple Range Test. 

At any rate the practitioner must follow a two-step process in setting up a calibration graph 1. Stabilize the response variance across the range needed and 2. choose an appropriate calculation function model. The response data is stabilized currently in two ways, either by weighting on a level-by-level basis or by applying some transformation function in the same manner to all the response values. The model chosen must approximate the data. It can be that a simple linear (as shown by a statistical test) function can serve this purpose adequately. The use of Mitchell s multiple linear function has been successfully... 

What statistical testing strategy will provide valid answers to the range of questions being asked, particularly in terms of controlling multiplicity in the confirmatory setting ... 

Statistics. Where appropriate, the data in each experiment were subjected to analysis of variance. Means were tested for significant differences (P < 0.05) by the sequential methods of Newman and Keuls multiple range test. 

Means followed by the same letter are not statistically different at the 95% level of confidence according to Duncan s multiple range test. 

This analysis allows us to split the variability observed for B into contributions due to different factors. The probability (p-value) provides a measure of the statistical significance (at a confidence level of 95%) of each factor. Overall at least one of the factors has had a significant effect (p = 0.0001) on the measured level of B. This is in a good agreement with previous observations. Multiple range tests (Fisher s least significant difference (LSD)) was performed to determine which of the treatment means were significantly different from each other, and the results are summarized in Table 4.5.5. 

Statistical significance between treatment means was determined by Duncan s Multiple Range Test (8). 

Each treatment was conducted in triplicate and all experiments were repeated at least twice. The statistical significance of the evaluated data was analyzed by one-way analysis of variance. Differences among the mean values were tested using the least significant difference multiple range test. Values were considered significant when p<0.05, except when otherwise indicated. 

Values reported are mean percent relative bioavailable arsenic for that group. Mean separation statistics were generated using Duncan s multiple range test (37). Multiple comparison of mean values are made between bioavailable As method (horizontally). Mean values with the same letter designation indicate no difference between groups at P < 0.05. 

Statistical Analyses. Results were expressed as the arithmetic mean for each group SEM. For each diet, after ANOVA, the means from groups OA, LA, and ALA were classified using Duncan s multiple range t-test. P < 0.05 was considered significant. 

The results of the DTS tests are shown in Table I. The values given are in MPa with the standard deviation indicated by the number in parentheses. All statistical information was generated with the general linear model program of the Statistical Analysis System software (5). Comparisons of the data were made with Duncan s Multiple Range Test (modified for unequal sample sizes) at p<0.5 ( -5). Values prece by an asterisk indicate no significant difference from each other. 

Each experiment was carried out in two replicates. Peak area count of each volatile compoxmd was obtained for each replicate. Analysis of variance (ANOVA) using a general linear model (GLM) procedure of the Statistical Analysis System (SAS, 2004, Cary, USA) was performed to evaluate differences between parameters. The parameters evaluated were type, cultivar, location, crop year, and interactions between them Means comparison between parameters was carried out by Duncan s multiple range test using SAS software. 

TABLE 1. Photosynthesis and transpiration rates. Mean values (=SE) of 6 to 14 replicates. S= Statistical significance according to the Analysis of the Variance. Values without a common subindex are significantly different at 0,95 level according to the Duncan Multiple Range Test. 

Statistical Analysis. Data were statistically analyzed by a two-way analysis of variance, and where significant differences were found, a Student-Newman-Keuls (SNK) multiple range test or Duncan s multiple-range test at a probability of a =... 

Mean values for serum fatty acid composition (n = 5) within a row having different superscripts (m, n for fat effect x, y for CLA effect A, B, Q and D for interaction effect) are significantly different by two-way ANOVA and Student-Newman-Keuls (SNK) multiple range test (P< 0.05). The factorial design of dietary treatments allowed for an n = 10 in the statistical analysis of main effects. 

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