Statistical Analysis. Means and standard error of the mean were calculated for the mycelial growth inhibition and germinated seeds after composts teas treatment measured for the three sets of experiments in each case. These means were statistically compared using the LSD Fischer test was used to determine if they were significantly different at P< 0.05.Cm : Cattle manureII. RESULTS
Statistical Analysis. Means and standard deviations for body composition measures were calculated for each athlete sub-group. These included total mass, lean mass, and fat mass for the whole body, as well as for the trunk, leg and arm regions. Data from repeated scans were used to calculate change in the mean (the mean difference between the repeated scan results), typical error of the measurements (TEMs; standard deviation of the difference scores of all athletes in the group divided by √2, in grams and %) and intraclass correlation coefficients (ICCs) for all body composition measures, using a published spreadsheet (Hopkins 2000b). To ensure normality of the sampling distribution, each of these measurements were firstly log transformed before analysis and back transformed after analysis, as recommended by Hopkins (2000a). TEMs were derived for the whole cohort, for each sub-group of athletes (each sport separated by gender; n = 7) and for male and females. To test whether the TEMs differed by height, weight or body fat percentage, TEMs were computed for the first and fourth quartiles when athletes were ranked according to each of these descriptors. Uncertainty in the TEM estimates were expressed as 90% confidence limits (CL). The typical error differences between the two groups for each demographic (gender, height, weight and body fat percentage) were considered clear, if the 90% confidence intervals (CI) of the groups did not overlap. Additionally, Pearson correlation coefficients were used to assess the relationship between the mean fat masses and the fat mass TEMs of the associated body regions. According to Hopkins (2000a), the TEM (which represents the error in both directions) should be multiplied by a factor of 1.5 to 2 before interpreting longitudinal changes. Thus, TEMs were doubled to provide a conservative ‘TEM threshold’ above which changes were considered likely (92%probability) to be ‘true’ changes. Data from the first scans were used as an estimate of baseline body composition. For the follow-up DXA scans, percentage changes (from baseline and between time points) in three whole body composition measures (total body mass, lean mass and fat mass) were calculated for all bob skeleton athletes and rugby players at each time point. Additionally, for the bob skeleton athletes only, percentage changes in leg lean mass were calculated at each time point as the emphasis of training was lower limb hypertrophy. The percentage changes in total lean mass, leg lean mass, an...
Statistical Analysis. Means and standard deviation were used for data comparison.
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Here are some templates for Outcome Measure and related Statistical Analysis of the data.• Adverse EventsTable summary of anticipated and unanticipated serious, other (not including serious) adverse events and All- Cause mortality through the entire duration of the study.
The Document Section is for the uploading of study documents (Study Protocol, Statistical Analysis Plan (SAP), and/or Informed Consent Form) to the PRS.
Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities, Addendum to Interim Guidance.
Form: Three-Party Nondisclosure Agreement (Statistical Analysis) ........................
According to LinkedIn, Statistical Analysis & Data Mining were the hottest skills that got recruiters’ attention in 2014.
More Definitions of Statistical Analysis
Statistical Analysis. Means, and standard deviations for each characteristic will be calculated. Paired sample t-test will be computed to assess changes in before treatment and follow-up scores. Statistical significance will be calculated and two-tail significance level of 0.05 will be used.
Statistical Analysis. Means and frequencies were used to summarise the demographic data. “t-tests” were used to ascertain whether any categorical variables were related to parenting stress. Pearson’s correlations were done to ascertain whether there was any correlation between demographic variables and levels of parenting stress.
Statistical Analysis. Means were compared by one-way ANOVA followed by Tukey’s HSD test using SPSS 6 for Windows or Systat 8.0 for Windows. Proportion data were arcsin transformed prior to analysis. A confidence level of p<0.05 was used throughout.
Statistical Analysis. Means comparison was carried out using a one way ANOVA. Post-hoc tests were conducted following a Student Newman-Keuls test using SPSS (version 22, SPSS Inc., Chicago, IL, USA) at a significance level P < 0.05.
Statistical Analysis. Means of SIDC of protein and AA were separated using independent (unpaired) samples t-test for unequal variances. Significant differences between means were separated by Least Significant Difference (LSD) test. Differences were considered to be significant at P < 0.05.
Statistical Analysis. Means, standard deviations (SD), medians, and interquartile ranges (IQR) were computed to describe the data. A customisable statistical spreadsheet and between-participant pre-race SD were used to compute effect sizes (ES) [16]. The smallest worthwhile difference in means was set to 0.20 of these SDs, except for foot-strike angle where it was set to 2.5˚ based on prior test- retest data [27]. Magnitudes of the ES were interpreted as trivial (ES < 0.2), small (0.2 ≤ ES < 0.6), moderate (0.6 ≤ ES < 1.2), and large (ES ≥ 1.2), and deemed clear if their 90% confidence interval [lower, upper] did not overlap thresholds for small positive and small negative effects. Variables were log-transformed to reduce bias arising from non-uniformity of error and used for interpreting all statistical comparisons, except for foot-strike angle where log- transformation was not appropriate. Statistical significance from paired t-tests was set at P <ACCEPTED MANUSCRIPT0.05. The 3-km and 10-km foot-strike angles were compared using the same statistical approaches. Levels of agreement and 90% confidence intervals between pre-race and post-race, 3-km and 10-km, and perceived and actual foot-strike patterns were computed using the Wilson score method incorporating continuity correction [9].
Statistical Analysis. Means or proportions were compute by age and gender and according to RTE intake category. Analysis ofvariance was used to determine if BMI, per cent overweight/obese differed by age and RTE consumption categories. Pair wise t-tests were performed where differences were found among the categories. Logistic regression was used to analyse the association between total energy, macronutrient intake, RTE consumption pattern and intake. The contrasts were examined between the possible pairs of cereal consumption categories using the Wald chi-square. An alpha level of 0·05 was used to determine significance for the analysis of variance comparisons except where otherwise noted. All analyses were performed using SAS® version 9.2 (SAS Institute, Cary, NC).