Least significant difference LSD

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. 

Figure 1. Sensory descriptive analysis data of Napa Cabernet Sauvignon samples and the base wine. Mean ratings of 14 judges x 2 replicates and least significant differences (LSD, p<0.05) are shown. For sample codes, see Table II.

For data satisfying the ANOVA requirements, the least significant difference (LSD) is useful for making planned comparisons among several means. Any two means that differ by more than the LSD will be significantly different. The LSD is useful for showing on graphs. 

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. 

If significant differences are indicated in ANOVA, we are often interested in the cause. Is one mean different from the others Are all the means different Are there two distinct groups that the means fall into There are several methods to determine which means are significantly different. One of the simplest is the least significant difference (LSD) method. In this method, a difference is calculated that is judged to be the smallest difference that is significant. The difference between each pair of means is then compared with the least significant difference to determine which means are different. 

Figure 4. Sensory scores and least significant differences (LSD) for wines stored six months in oak barrels and one year in bottle.

Fisher s least significant difference (LSD) test Linear regression to test for dose-effect trends Pairwise comparison Pearson s correlation coefficient Student s t-test Williams s t-test... 

Data were analyzed by analysis of variance (ANOVA) and Fisher s protected least significant difference (LSD) to compare differences between means of cell mass, of total lipids, and of conversion rates. Means differences were considered significant at the p <. 05 level. 

All data were subjected to variance analyses. Whenever variation among fiber sources was statistically significant, the least significant difference (LSD) at the 5% and 1% level of probability was calculated for statistical inference of the results. 

An analysis of variance (ANOVA) was carried out to evaluate the significance of different adhesive formulations on shear strength. The results were further analyzed using the Least Significant Difference (LSD) test at p 0.05, to further evaluate the effects of adhesive formulations on the physical properties, shear strength and... 

There was a consistent decline in substrate TFR and air TFR the first three days [p <. 05, Least Significant Difference (LSD) test], after which the level of these behaviors -became rather uniform (Table 1). Locomotion declined also, but more slowly a significant (p <. 05) decline did not occur until Day 4, although locomotion continued to decline afterwards. 

Analysis of variance was performed using the software package Statgraphics Plus (Centurion) to detect significant differences (p< 0.05) between measurements on different days. The two-fector ANOVA test was performed on the results of the MRI measuiemraits corresponding to each measurement day (tissue type and tomato sample) and the F-ratio was calculated to measure how diffo ent the means at 95% Least Significant Difference (LSD) confidence level were in relation to the variations within each sample. 

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 color changes in the samples were monitored by measuring the Hunter L, a, b values of the pressurized and control fish samples using a Minolta Chromameter II reflectance equipment. The differences between the mean values were analyzed using the least significant difference (LSD) multiple comparison test of Steel and Torrie, (1960). The general linear model (GLM) procedure of the Statistical Analysis System (SAS) on McGill University mainframe was used. 

Mean AE values with the same superscripts are not significantly different (p > 0.05). Least significant difference (LSD) = 3.6341. 

Data were analysed using the Statgraphics software (STCC Inc., Rockville, Maryland). Multifactor analysis of variance, and multiple range analysis (Least significant difference, LSD) were done to determine the sources of variation in the data and the effects of antioxidant types, processing treatments and storage on the oxidation of the fish products. Tests of significance were done at the 99% confidence level (p < 0.01). 

Ten panelists, six products, one attribute, and two replications amounted to 120 time-intensity curves. An analysis of variance and the least significant difference (LSD) were computed for each primary and secondary parameter. 

The least significant difference (LSD) between NIF (or SNIF) values of a same compound smelt in the olfactograms of two products... 

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