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 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 deviation were used for data comparison.
Examples of Statistical Analysis in a sentence
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Statistical Analysis and Determination of Regression Formulas for Main Dimensions of Container Ships Based on IHS Fairplay Data.
Statistical Analysis Agreement between the applied serological tests for small xxxx- nant brucellosis was assessed by calculating the Kappa statistic and associated 95% CI for each binary combination of tests (Xxxxxx & Xxxxxxxx, 2003).
Competency 1: Laboratory Safety and Laboratory Notebook Competency 2: Purification Techniques Competency 3: Spectroscopy Competency 4: Functional Group Interconversion Competency 5: Chromatography Competency 6: Statistical Analysis Competency 7: Computational See Appendix E: Competencies for Preparation in Organic Chemistry Laboratory.
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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. All analyses will be conducted using IBM SPSS 21.0.
Statistical Analysis. Means, standard errors of body weight and linear body measurements were obtained using the statistical package for social science SPSS 20 (2010). Pearson coefficient of correlation among body weight and linear body measurements was estimated, and the principal factor analysis was obtained from the correlation matrix. Principal component analysis was applied to linear body measurements, and linear body measurements were combined and formed unrelated components. The interpretation of principal components was improved by varimax rotation. The appropriateness of the principal component analysis was tested using communalities. Bartlett’s Test of Sphericity was conducted to determine the appropriateness of the common factor model in analysing body weight. Models for predicting body weight from (a) body measurements and (b) from principal components were obtained using the stepwise multiple regression procedure. Models with a higher coefficient of determination (R2) were considered better than models with low (R2).BW= a+BiXi+….BkXk (a) BW=a+BiCPi +BkPCk (b)where BW is the body weight, a is the regression intercept, Bi is the ith partial regression coefficient of the ith linear body measurement, and Xi or the ith principal component (PC).
Statistical Analysis. Means were analyzed using ANOVA and significant difference between the means were determined using Tukey’s pairwise comparison at α=0.05. Statistical analysis was performed using SAS 9.4.
Statistical Analysis. Means of data obtained were subjected to analyses of variance (ANOVA) to determine if means are significantlydifferent at p<0.05. Separation of means was carried out using Duncan multiple range tests.
Statistical Analysis. We analyzed the data in SAS version 9.3 (SAS Institute Inc., Cary, NC, USA). Children were the unit of analysis, since compound level data pertained to each individual child from which a blood sample was collected. When appropriate, we applied sampling weights to account for the unequal probabilities of selection into the study due to the sampling design. Some epidemiologists have argued that while analyses to estimate prevalence should always maintain representativeness, analyses to investigate causal associations need not necessarily do this (Xxxxxxx, Xxxxxxxxx, & Xxxxx, 2013). This distinction is between maintaining the abilities to make statistical inferences about prevalence and scientific inferences about causality (Xxxxxx, 2013). Thus, we performed analyses for prevalence estimates using only the 322 children from the 46 randomly sampled villages and included the sampling weights. For analyses aimed at answering questions about potential causal factors linked to BLL, we included all 383 children from all 54 villages and did not include the sampling weights. The weights represent the inverse of each child’s probability of selection at each stage in the sampling approach. We performed multiple imputation in SAS using the expectation-maximization method to account for missing values for 19 sleep area dust samples (5.0% of all observations) and 18 play area soil samples (4.7% of all observations). The missing values were assumed to be missing at random. The method used creates five estimates for each missing value, runs the statistical test requested, then takes the mean of the results from the five separate tests. Results
Statistical Analysis means the analysis of the Clinical Proof of Concept Study results performed in accordance with Section 3.5.
Statistical Analysis. Means and standard deviations were calculated for lengths and weights of each school of menhaden. Comparisons between the prevalence of different lesions in fish at different locations and years were tested using a non-parametric Chi-Square analysis at a significance level of p<0.05. Yearly concentrations of TPAHs were compared using a Kruskal-Wallis One Way Analysis of Variance on Ranks at a significance level (p<0.05). All statistics were run using SigmaPlot 11.0 (Systat Software Inc.) commercially available software.