The significance between different treatments was analyzed using SAS 9.2 for Windows. Fisher’s LSD (Least Significant Difference) was selected by Bonferroni correction for analyzing the factors including HIU power, treatment time and their interaction. The differences in the results were regarded as significant when P < 0.05.
Sas 9.2 for windows
Manufactured by SAS Institute
Sourced in United States
SAS 9.2 for Windows is a software application that provides a comprehensive platform for data analysis, statistical modeling, and reporting. It offers a wide range of tools and capabilities for data management, statistical analysis, and business intelligence. The software is designed to run on the Windows operating system.
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Lab products found in correlation
49 protocols using sas 9.2 for windows
1
Analyzing Treatment Effects Using SAS
2
Statistical Analysis of Maternal and Neonatal Outcomes
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Data were systematically transferred from medical records to Microsoft Excel 2010. Statistical analyses were performed using SAS 9.2 for Windows. Data frequencies (in percentage) were compared using the chi-square test; means were evaluated by analysis of variance (ANOVA), followed by the Tukey-Kramer test for multiple comparisons. Data on maternal, neonatal and delivery outcomes were assessed using logistic regression to calculate odds ratios with confidence intervals (CI 95 %). Significance level was set at p < 0.05.
3
Statistical Analysis of Experimental Data
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All the data were subjected to a one-way ANOVA procedure provided in SAS 9.2 for Windows. Differences were determined by Duncan’s multiple range tests. A significant difference was defined as p<0.05 and the results are presented as mean±standard deviation.
4
Biomass and Carbon Dynamics Analysis
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Normality of aboveground and below-ground plant biomass and SOC stocks was tested using the Kolmogorov–Smirnov test. The significance of differences among the three treatments considering the aboveground and below-ground plant biomass and 13C stocks was tested by ANOVA, which was calculated separately for each layer; P < 0.05 was considered statistically significant for treatment means. We used nonlinear least squares (function Bnls^) to fit Eq. 5 . Statistical analysis was performed using SAS 9.2 for Windows.
5
Evaluating Factors Influencing MOAI
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Primary endpoint was the change in MOAI before vs. after treatment. Due to non-normally distributed data a sign-test was used. A p-value <0.05 was considered statistically significant concerning the primary endpoint. All other analyses are regarded descriptive. The following factors were analyzed for their possible influence on MOAI change between admission and discharge: gender (male vs. female), age at admission (≤28d vs. >28d) and failure-to-thrive (SDS for weight ≤1.28 vs. >1.28). All analyses were done with statistical software (SAS 9.2 for Windows; SAS; Heidelberg, Germany).
6
Statistical Analysis Using SAS Software
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Statistical analysis was performed using the software SAS (p ≤ 0.05; SAS 9.2 for Windows; SAS Institute, Cary, NC).
7
Assessing PLR Coverage Probability
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A simulation experiment was conducted to study the empirical performance of PLR with respect to coverage probability. Coverage probability is defined as the probability that the procedure produces an interval that covers the true (regression coefficient) value. In order to investigate the performance of this method in the presence of complete and quasi separations, we created 1,000 datasets, each with different sample of sizes n = (30, 75, 150) and with a different number of covariates k = (2, 3, 4). We used Monte Carlo method to obtain the 95% coverage probability of PLR. We conducted the simulation experiment by using SAS 9.2 for windows (SAS Institute, Inc., Cary, NC, 2000) statistical software. The R, STATA, and SAS codes for doing PLR are given in the appendix .
8
Evaluating the Effect of Food Type on Nutrient Ratios
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The effect of food type (i.e. extruded, pelleted and canned for dog foods, extruded and canned for cat foods) on RL:TL was tested for significance using ANOVA by Proc GLM of SAS 9.2 for Windows (SAS Institute, Cary, NC, USA). In case P ≤ 0·05 for significant effects, pairwise comparisons were made using post hoc analysis and corresponding P values were reported. Results are presented as the means with their standard errors .
9
Arterial Injury and P2Y12 Deficiency
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Data are presented as mean±SE. Student t tests were used for the comparisons between the wild‐type (WT) and P2Y12‐deficient mice and between the control and sham groups. A paired t test was used for the comparison of the relative blood flow before and 1 hour after arterial injury. Two‐way ANOVA was used for the comparison among the genotype (WT/P2Y12 deficiency) and the injury (pre/post). Dunnett's test was used for the comparison between the control and all prasugrel groups. In all the analyses, statistical significance was defined as P<0.05. SAS 9.2 for Windows (SAS Institute Inc., Cary, NC) and EXSUS Ver. 7.7.1 (Arm Systex Co., Ltd., Osaka, Japan) were used for the analyses.
10
Predictors of Primary Care Quality
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Mean and standard deviation were applied for the description of average score. The differences in sociodemographic characteristics and quality of primary care measured by PCAT across healthcare provider types (GPs and non-GPs) were tested by bivariate chi square for categorical variables and by t test for continuous variables. In order to determine the association between healthcare provider types and quality of primary care, multiple linear regressions were performed after controlling for sociodemographic characteristics of providers. Finally, logistic regression was used to determine the independent predictors of GPs’ intention to stay. All analyses were conducted using SAS 9.2 for Windows.
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