Statistical analyses were conducted using Statistical Analysis System 9.2 (SAS Institute, Cary, NC, USA). Analysis of variance was done following fixed model (Gomez and Gomez, 1984 ), and data was analyzed as a paired split-plot design. Mean comparisons between tall and semi-dwarf pairs were also made for all six traits in the study. Broad-sense heritability (H2) (repeatability) was estimated across environments using the formula H2 = σg2/(σg2 + σge2/y + σe2/ry), where σg2 is the genotypic variance, σge2 is the GE variance, and σe2 is the residual error variance for r replicates and y years. The Principal Component Analysis (PCA) was calculated using META-R statistical package (www.data.cimmyt.org ). The Pearson correlation coefficient between traits was calculated using PROC CORR procedure.
Statistical analysis system 9
Manufactured by SAS Institute
Sourced in United States
Statistical Analysis System 9.4 is a software package developed by SAS Institute for advanced statistical analysis and data management. It provides a comprehensive set of tools for data manipulation, analysis, and reporting. The software offers a wide range of statistical procedures, including regression, ANOVA, multivariate analysis, and time series analysis, among others. Statistical Analysis System 9.4 is designed to handle large and complex data sets, making it a valuable tool for researchers, analysts, and decision-makers across various industries.
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Lab products found in correlation
Spss version 25, by IBM (2 mentions)
Prism 7, by GraphPad (2 mentions)
Sigma stat for windows version 3, by Grafiti LLC (1 mentions)
Statistical package for social sciences for windows version 27, by IBM (1 mentions)
Jmp pro 15, by SAS Institute (1 mentions)
Prism version 9, by GraphPad (1 mentions)
Spss statistics 25, by IBM (1 mentions)
Spss statistics 19, by IBM (1 mentions)
Statistical package for social sciences software, by IBM (1 mentions)
Spss for windows version 17, by IBM (1 mentions)
122 protocols using statistical analysis system 9
1
Variance and Heritability Analysis of Crop Traits
2
Mitochondrial Membrane Potential Analysis
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Statistical analysis was performed using the software Statistical Analysis System 9.2 (SAS Institute, Cary, NC, USA). Data were tested for normality of residues and homogeneity of variances. Variables that did not comply with these statistical assumptions were subjected to transformations. Results were reported as untransformed means ± S.E.M. or median, minimum, maximum and quartiles (box plot) for parametric and non-parametric variables, respectively. High mitochondrial membrane potential was the only variable analyzed as non-parametric (WILCOXON PROC NPAR1WAY). PROC MIXED was used to evaluate the effect of treatment, incubation time and interaction between treatments x incubation time. Comparisons were performed using least square means (LS means). Person and Spearman correlations analysis were performed to verify the correlation between parametric and non-parametric variables, respectively (PROC CORR). All statistical analyses were calculated with a significance level of 5 %.
3
Statistical Analysis of Experimental Data
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The response
parameters were subjected to analysis of variance (ANOVA) and mean
separation test using the Statistical Analysis System 9.2 (2010, SAS
Institute, Cary, NC). Mean and standard error values were assessed
to assemble graphs using the SigmaPlot software 10 (MMIV Systat Software,
Inc., San Jose, CA).
parameters were subjected to analysis of variance (ANOVA) and mean
separation test using the Statistical Analysis System 9.2 (2010, SAS
Institute, Cary, NC). Mean and standard error values were assessed
to assemble graphs using the SigmaPlot software 10 (MMIV Systat Software,
Inc., San Jose, CA).
4
Hypertension Prevalence and Associations
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Differences between means or medians of quantitative measures were tested using Student’s t-test or the Mann–Whitney U test. Qualitative data were expressed as proportions; testing for differences was performed using a chi-square test. Logistic regression models were used to estimate prevalence, awareness, treatment, control, and medication-control of hypertension with 95% confidence intervals (CI) after adjusting for gender, age, and occupation. A generalized linear model, stratified by sex, was used to test for associations between SBP or DBP and salt intake. All analyses were performed with Statistical Analysis System 9.2 (SAS Institute Inc., Cary, NC, USA); a p-value < 0.05 was considered statistically significant.
5
Sexual Risk Behavior Data Collection
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Sexual risk behavior data regarding the type of sexual partners, number of sexual partners, condom use, homosexual and group sexual behaviors and illicit drug using were collected from all participants. The specific questions are shown in the supplementary table .
To control the data quality, the interviewers were trained before the investigation; the physicians helped explain the aims of investigation to potential participants in order to avoid their considerations, which may partly benefit to the data authenticity. After the survey, the interviewer would then check the completed hardcopy questionnaires for accuracy and completeness and errors in questionnaire would be reviewed with participants. Before data analysis, logical check between different variables was performed in Statistical Analysis System 9.2(SAS Institute Inc., Cary, North Carolina, USA), including ranges, consistency and logical relationships. Whenever such checks revealed errors, original hard copy questionnaires were revisited for final confirmation and correction.
To control the data quality, the interviewers were trained before the investigation; the physicians helped explain the aims of investigation to potential participants in order to avoid their considerations, which may partly benefit to the data authenticity. After the survey, the interviewer would then check the completed hardcopy questionnaires for accuracy and completeness and errors in questionnaire would be reviewed with participants. Before data analysis, logical check between different variables was performed in Statistical Analysis System 9.2(SAS Institute Inc., Cary, North Carolina, USA), including ranges, consistency and logical relationships. Whenever such checks revealed errors, original hard copy questionnaires were revisited for final confirmation and correction.
6
Serratus Anterior Muscle Activity Analysis
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Statistical analysis was performed using Statistical Analysis System 9.2 (SAS Institute Inc., Cary, USA). Inspection of sEMG data revealed a non-normal distribution. Therefore, a logarithmic transformation was performed to correct for skewness. A linear mixed model for repeated measurements was used to assess the differences between the three test positions for each muscle, separately. The model reference point was set at test position C, as it was estimated that this test position would produce most serratus anterior muscle activity based on the study by Ekstrom et al. [27 (link)]. The dependent variable was the logarithmically transformed RMS-value of EMG activity during the 3-s maximum contraction. The estimated values of the sEMG activity for each position and the relative differences between the positions with 95% confidence intervals (CIs) were calculated by use of the anti-logarithmic transformation.
7
Statistical Analysis of Parallel Experiments
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All results were centered at using three parallel experiments. Results were expressed as mean ± standard deviation and analyzed by Statistical Analysis System 9.2 (SAS Institute Inc., Cary, NC, USA). Analysis of variance was performed by ANOVA procedure. p-value of 0.05 or 0.01 was considered to be statistically significant.
8
Associations of Daily Physical Activity
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Means and standard deviations or frequencies are reported for all descriptive statistics as well as OPA and non-OPA. To describe the daily variability in OPA and non-OPA within a participant, a coefficient of variation (standard deviation divided by mean) was calculated. T-tests were used to determine differences in daily variability between men and women. Associations between usual OPA and usual non-OPA (averaged across the week) were assessed via general linear regression and R2. General linear random coefficients models allowing for random intercepts and slopes were used to examine associations between OPA and non-OPA within a given day (up to 12 days per participant), allowing a random intercept and slope for each participant. Analyses were also conducted to examine sex as a potential effect modifier of the association between OPA and non-OPA. The models testing differences by sex were also adjusted for potential confounders including age, ethnicity, job type and BMI. Crude and age-adjusted models are presented. Analyses were run with and without outliers. Relationships remained the same when outliers were removed, therefore all values were retained. Analyses were conducted in Statistical Analysis System 9.2 (SAS Institute Inc., Cary, NC).
9
Statistical Analysis of Variance
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The analysis of variance was performed with the Statistical Analysis System 9.2 (SAS Institute Inc., Cary, NC, USA), and the statistical difference of the means was determined by the Student's t-test or Duncan's multiple range test at a significance level of p<0.05.
10
Statistical Analysis of Plum Wine
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Analysis of variance (ANOVA) was performed using Statistical Analysis System 9.2 (SAS Institute, Inc., Cary, NC, USA). The statistical differences between the two analytical methods for acetaldehyde and between the contents of harmful substances from two plum wines were determined by Student’s t-test at p < 0.05. The statistical difference of means between more than three groups was determined using Duncan’s multiple range test at p < 0.05. The correlation between TSO2 and acetaldehyde contents in wines was determined via the Pearson correlation coefficient (r) using SPSS Version 25.0 (IBM Corp., Armonk, NY, USA). p < 0.05 and p < 0.01 were considered indicative of statistical significance.
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