In order to compare different stability statistics in studying GxE interaction on short- term yield trials in rapeseed, rank-correlation coefficients were used. Set 1 (T1) had 22 spring cultivars with Sarigol as the check genotype grown in eight environments, set 2 (T2) had 21 spring cultivars, plus the same check variety, in 16 environments and set 3 (T3) had 23 winter type cultivars, using SLM046 as a check genotype, in 18 environments. The relationship between CVi and bi was significant and positive. The rank-correlation coefficient between W2i and s2i  was very high. Thus using one of them, preferably s2i, is sufficient. Also a high rank-correlation coefficient was estimated among s2i  and S2di. In all of the sets, only positive and significant rank-correlation coefficient between YSi and mean yield was detected. It is indicated that using YSi criterion will result in the selection of stable and high yielding cultivars.

Multi-environment trial data are required to obtain stability performance parameters as selection tools for effective cultivar evaluation. The interrelationship among several stability parameters and their associations with mean yield, along with the repeatability of these parameters in consecutive years was the objective of this study. Barley yield data of 18 cultivars, proprietary of Dryland Agricultural Research Institute, evaluated in 12 locations over 2005-2007 in three locations of Iran was used for the combined analysis of variance in three datasets. I: Across locations in a single evaluation year (dataset A), II: Across locations in each of two single evaluation year (dataset B), III: Across all locations in three years (dataset C). For each dataset, cultivar phenotypic variance and other statistics were appropriately partitioned in its components. The interrelationship among the parameters and their associations with mean yield based on Spearman rank correlation was studied in each of the three single evaluation years (dataset A). Rank correlation coefficients were also used as estimates of the repeatability of these stability parameters across two year combinations (dataset B). The parameters 2  si Shukla and 2 Wi were consistently highly correlated with each other but not with mean yield in all single and pair evaluation years. The parameters bi, 2 Si and CV were consistently highly correlated with each other but not with mean yield in all single and pair evaluation years. Result showed that Shukla variance, Wricke ecovalence, SIPC1 and ASV AMMI stability statistics have high repeatability. Thus it is suggested that more stability statistics especially nonparametric methods be used for determination of repeatability methods.

Objective: Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables.Methods: A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed.Results: Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety.Conclusion: These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction.

Descriptive statistics is the process of summarizing gathered raw data from a research and creating useful statistics, which help the better understanding of data. According to the types of variables, which consist of qualitative and quantitative variables, some descriptive statistics have been introduced. Frequency percentage is used in qualitative data, and mean, median, mode, standard deviation; standard error, variance, and range are some of the statistics which are used in quantitative data. In health sciences, the majority of continuous variables follow a normal distribution. skewness and kurtosis are two statistics which help to compare a given distribution with the normal distribution.