Data were analyzed with the SAS package version 9.4 (SAS Institute, Cary, NC, USA). Firstly, the normality of the residuals of the variables studied was tested using the UNIVARIATE procedure. Sow body condition variables were analyzed using the MIXED procedure. The model included diet, time and their interaction as fixed effects and sows within treatment as the random effect. Time was analyzed with a repeated statement using Toeplitz covariance structure in the case of BFD and heterogeneous first-order autoregressive structure in the case of LMD, the models in each case with the smallest Schwarz Bayesian’s Information Criterion. Reproductive performance normal variables were analyzed using the GLM procedure. The model included the diet as the main effect. Reproductive performance variables whose residuals did not follow a normal distribution were analyzed with the Mann-Whitney U Test. The experimental unit was the sow in body condition variables and the litter in the case of reproductive performance variables. A p-value < 0.05 was considered a significant difference.
Sas package version 9
Manufactured by SAS Institute
Sourced in United States
SAS package version 9.4 is a comprehensive software suite designed for data analysis, reporting, and business intelligence. It provides a wide range of tools and capabilities for data management, statistical analysis, predictive modeling, and visualization. The core function of SAS package version 9.4 is to enable users to effectively and efficiently work with data from various sources, perform advanced analytics, and generate insightful reports to support informed decision-making.
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Lab products found in correlation
10 protocols using sas package version 9
1
Sow Body Condition and Reproductive Performance
2
Cardiovascular Disease Risk Prediction Model Evaluation
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The SAS package version 9.4 (SAS Institute, Cary, NC) was used for statistical analysis. To calculate the sample size for the comparison of the area under receiver operating characteristic (ROC) curve (AUC) for 20‐year follow‐up based on the AUC of 0.576 as observed in other studies
22 (link) with a null hypothesis of 0.5, we considered α=0.05, power (1‐β)=0.80 and a ratio of sample sizes in positive/negative groups of 1:8 (11% of cardiovascular disease [CVD] events). The Kolmogorov–Smirnov test was used to determine if all variables were normally distributed. Continuous variables were expressed as mean ± SD and compared among classes or categories by the analysis of covariance adjusted for proper confounders and followed by the Bonferroni's post hoc test. Categorical variables were compared by means of the Pearson χ2 test. The null hypothesis was rejected for values of P<0.05.
22 (link) with a null hypothesis of 0.5, we considered α=0.05, power (1‐β)=0.80 and a ratio of sample sizes in positive/negative groups of 1:8 (11% of cardiovascular disease [CVD] events). The Kolmogorov–Smirnov test was used to determine if all variables were normally distributed. Continuous variables were expressed as mean ± SD and compared among classes or categories by the analysis of covariance adjusted for proper confounders and followed by the Bonferroni's post hoc test. Categorical variables were compared by means of the Pearson χ2 test. The null hypothesis was rejected for values of P<0.05.
3
Factorial Analysis of Meat Slaughter
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The analysis was carried out with a factorial design. Statistical analysis was performed using the General Linear Model (GLM) procedure of the Statistical Analysis System (SAS) package Version 9.4 (SAS Institute Inc., Cary, NC, USA). Analysis of data was done using the sampling time and slaughter knife as the main effects within the ANOVA procedure. When noticeable effects were seen, a comparison of means was done using Duncan’s multiple range test. The statistical significance was set at p < 0.05.
4
Comparing Adverse Event Reporting Methods
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The incidences of AEs, ADRs, SAEs and SADRs for the two methods of data collection were compared using chi-square and Fisher’s exact tests at a statistically significant level of 5%. All statistical analyses were performed using the SAS package version 9.4 (SAS Institute Inc., Cary, NC) or R version 3.3.0 programs.
5
Uric Acid and Fatal Myocardial Infarction
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The SAS package version 9.4 (SAS Institute, Cary, North Carolina, USA) was used for statistical analysis.
A preliminary power analysis based on differences from stratified values of uric acid for α = 0.05 and power (1 − β) = 0.80 was performed. To our knowledge, no study exists about possible cut-off values of SUA discriminating individuals into doomed to and not doomed to develop fatal MI, much less after sex stratification. Consequently, based on previous work of our research staff [20 (link),21 (link)], we considered 1 mg/dl SUA as a possible difference able to stratify individuals according to the above-mentioned outcome. Power analysis showed that the number of individuals in the database (n = 23 467) represented a sample largely sufficient to avoid β error also after stratification by sex and by fatal MI. The Kolmogorov–Smirnov normality test was performed. Continuous variables were expressed as mean ± SD and compared among classes or categories by the analysis of covariance adjusted time to time for proper confounders and followed by the Bonferroni's post-hoc test. Categorical variables were compared by means of the Pearson χ2 test. In multivariate analyses, the covariables that were not independent from each other were previously log-transformed. The null hypothesis was rejected for values of P less than 0.05.
A preliminary power analysis based on differences from stratified values of uric acid for α = 0.05 and power (1 − β) = 0.80 was performed. To our knowledge, no study exists about possible cut-off values of SUA discriminating individuals into doomed to and not doomed to develop fatal MI, much less after sex stratification. Consequently, based on previous work of our research staff [20 (link),21 (link)], we considered 1 mg/dl SUA as a possible difference able to stratify individuals according to the above-mentioned outcome. Power analysis showed that the number of individuals in the database (n = 23 467) represented a sample largely sufficient to avoid β error also after stratification by sex and by fatal MI. The Kolmogorov–Smirnov normality test was performed. Continuous variables were expressed as mean ± SD and compared among classes or categories by the analysis of covariance adjusted time to time for proper confounders and followed by the Bonferroni's post-hoc test. Categorical variables were compared by means of the Pearson χ2 test. In multivariate analyses, the covariables that were not independent from each other were previously log-transformed. The null hypothesis was rejected for values of P less than 0.05.
6
Logistic Regression Modeling on Sociodemographic Factors
7
Statistical Analysis of Experimental Data
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Data were analyzed
using analysis of variance (ANOVA) followed by Duncan’s multiple
range test of SAS package, version 9.2 (SAS Institute, Cary, NC, USA).
Graphs were plotted using Origin 8.6. Differences were considered
significant at p < 0.05.
using analysis of variance (ANOVA) followed by Duncan’s multiple
range test of SAS package, version 9.2 (SAS Institute, Cary, NC, USA).
Graphs were plotted using Origin 8.6. Differences were considered
significant at p < 0.05.
8
Dietary Fatty Acids and Plasma Lipids
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Statistical analysis was performed using the SAS package version 9.2 (SAS Institute, Inc.). All values are expressed as means with their standard errors, unless otherwise indicated, with differences considered statistically significant at P,0•05. Impacts of the two dietary treatments (endpoint values) on the outcomes of interest were compared using the PROC MIXED procedure for repeated measures, with diet, sequence and sex as fixed factors, and study centre and subjects as random factors in the selected model, unless otherwise stated. Spearman's correlation coefficient analyses were used to assess the relationship between plasma 25(OH)D and serum lipid levels, as well as between intakes of total SFA, MUFA, PUFA and MCFA, as estimated by the FFQ, and their corresponding FA levels in plasma. Abnormally distributed variables were natural log-transformed before statistical analysis.
9
Statistical Analysis of Agronomic Traits
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After using the Bartlett's test of homogeneity of variances (Bartlett, 1937) the combined analysis was performed on the recorded data of physiological and agronomic traits according to Gomez & Gomez (1994) using Proc Mixed of SAS Package Version 9.2 (SAS/ STAT, 2008) . Means were compared by Revised Least Significant Difference (RLSD) at 5% level of significance (El-Rawi & Khalafalla, 1980) .
10
Marker-Trait Association Analysis
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Association analysis between markers and phenotypic traits was performed with a mixed linear model (MLM) using the TASSEL software. It was applied with singlefactor analysis of variance (SFA) without Q & K, general linear model (GLM) with Q (individuals membership in the population) [50] , mixed linear model (MLM) with K (genetic relatedness) and MLM with K + Q [20] . SFA, which did not consider the structure of the population, was performed using each marker as an independent variable in the GLM in both AMP. Analysis of variance (ANOVA) and determination of Pearson's correlation coefficients (R) among measured traits were performed with Proc Mixed of SAS package version 9.2 (SAS 2008).
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