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
Prior to the analysis of the final study data, a detailed Statistical Analysis Plan (SAP) will be written describing all analyses that will be performed.
Specific details will be provided in the Statistical Analysis Plan.
Guidelines for the Content of Statistical Analysis Plans in Clinical Trials.
Full details of the statistical procedures to be used will be provided in the Statistical Analysis Plan.
Simar (2012) Applied Multivariate Statistical Analysis, Springer- Verlag, Berlin.
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. All analyses will be conducted using IBM SPSS 21.0.
Statistical Analysis. Means and standard deviation were obtained from at least 3 repetitions. Dne-way ANDVA and Tukey’s Range Test were used to evaluate the differences among the three regions for each grape cultivar. Significance analysis was performed using SPSS (SPSS Inc., Chicago, IL) for Windows, version 20.0.(2) the negative indexes: yij=(max xij- xij)/(max xij-min xij)The number of principal components was determined by the cumulative variance of traits. Then, the function of the main ingredients was listed according to the eigenvectors of the correlation matrix. Lastly, the comprehensive quality ratings of the wine grapes were determined by the comprehensive principal component values. PCA was performed with the SAS factor procedure.
Statistical Analysis. Means ± standard deviations and proportions of participants identified with the various clinical criteria for MetSyn were calculated. The agreements between the clinical criteria were determined using kappa statistics (κ). Levels of agreement were considered slight, fair, moderate, substantial, and almost perfect with κ = 0.00–0.20, 0.21–0.40, 0.41–0.60, 0.61–0.80, and 0.81–1.00, respectively [43]. Differences in baseline risk factor levels between participants identified by the different clinical criteria for MetSyn were compared using a general linear model and Scheffe post hoc analysis was used to further explore significant findings.NIH-PA Author ManuscriptDifferences in the impact of 7% weight reduction on MetSyn status was compared with Chisquare statistics. (Note: these analyses do not meet the assumptions of independent samples but were conducted for descriptive purposes). All analyses were preformed using SAS version9.1 (SAS Institutes Inc., Cary, NC).
Statistical Analysis. Means, standard error (SE) for each parameter were computed for all the digesters using Microsoft Office Excel (2007).CHAPTER FOUR
Statistical Analysis. Means and standard deviations were calculated for continuous variables. Wilcoxon rank sum tests were used to test for differences between groups (e.g., result of lawsuit, changes in medical practice) in their responses to the scales. Spearman rank correlation coefficients were used to assess the degree of association between questionnaire scales (e.g., cooperation of participants, awareness of changes in medical care). Surveys with missing values were not excluded; all item-level responses were incorporated into the analysis.SPSS Statistics Version 19.0 (SPSS Inc., Chicago, USA) was used for all analyses. Statistical significance was set at a p-value of 0.05.
Statistical Analysis. Means and standard deviations were determined by using the Microsoft Excel and the compressive and shear forces were determined by using Ergo-Edge [54]. Correlations were, also, computed as described in Hayter [55] using Excel 2000 software.
Statistical Analysis. Means R S.E.M. are presented with n denoting the number of cells tested. In every series of experiments, measurements were performed on at least four di¡erent cell or oocyte preparations.