All statistical analysis was performed using the SAS University Edition 3.5 (SAS Institute Inc., NC, USA) and MatLab (MatLab 2015b, The Mathworks Inc., MA, USA). Normality of the data distribution was evaluated by the Shapiro-Wilk test and by visual inspection of normal probability plots. Normally and not normally distributed data were expressed as mean ± SD and median and interquartile range, respectively. The primary outcomes of this study were the main cardiovascular and respiratory parameters: MAP, CI, PR, SVRI, PaO2, PaCO2 and DO2I. For the normally distributed data, comparisons between treatments and within each treatment were performed by a mixed-model ANOVA having the dog as random effect and time point as repeated effect, while the Friedman’s test was used for the non-normally distributed data. T-test and Wilcoxon sum rank test were used to compare normally and non-normally distributed data, respectively, between the treatments within each time point. Bonferroni procedure was used to correct the p value for multiple comparisons. In addition, the Dunnet’s test was used to compare each time point within the treatments with their respective T0. p < 0.05 was considered sufficient to reject the null hypothesis.
Sas university edition 3
Manufactured by SAS Institute
Sourced in United States
SAS University Edition 3.8 is a free software package that provides access to the SAS programming language and analytical tools. It is designed for personal, non-commercial use and offers a limited set of SAS features for learning and experimentation purposes.
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Lab products found in correlation
7 protocols using sas university edition 3
1
Cardiovascular and Respiratory Function
2
Milk Fat Fatty Acid Analysis
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Data of milk fat FA contents were analysed by repeated measurement analysis (Littell, Stroup, Milliken, Wolfinger, & Schabenberger, 2006) using the PROC MIXED of SAS University Edition 3.5 (SAS Institute Inc., Cary, USA). The model included the fixed effects of treatment, sampling time, period, sequence, and the interaction treatment x hour. The random effect was the animal nested within the interaction treatment x period x sequence. The repeated effect was the sampling time and the subject of the repeated measurements was the animal nested within the interaction treatment x period x sequence. A covariance structure appropriate for unequally spaced measures (compound symmetry, ANTE(1) or spatial power) was chosen on the basis of the Schwarz's Bayesian Information model fit criteria. The CONTRAST procedure was used to compare least squares means. Least squares means of each treatment at 1, 3, 6, 12 and 24 h were compared with their respective 0 h. Additionally, both experimental treatments were compared at each sampling time. Significant differences were declared at P<0.05.
3
Statistical Analysis of Cell Migration
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Descriptive data from the case-control cohort were analyzed using SAS University Edition 3.1 (SAS Institute Inc., Cary, NC). Differences in 2 × 2 discrete variables were determined using Fisher's exact test, while 2 × 3 discrete variables were compared using the Fisher-Freeman-Halton exact test46 (link). Regression analyses in Transwell migration assays were performed using 4 parameter logistic nonlinear regression analyses over the period of migration with post-hoc comparison by one-way ANOVA of the unshared maximum and time to 50% migration parameters. All nonlinear models were confirmed to have r-squared values >0.99, and accepted the null hypothesis of the Shapiro-Wilk normality test (p ≥ 0.05). All other analyses were performed in Prism 6.0 using a two-tailed independent t-test or one-way ANOVA with Bonnferroni or Dunnett's post-hoc tests where indicated.
4
Statistical Analysis of Cell Migration
5
Statistical Analysis of Treatment Effects
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The MIXED procedure of SAS University Edition 3.8 (SAS Institute, Cary, NC, USA) was used for statistical analysis. The statistical model included the treatment as fixed effect and the pen nested within treatment as random effect. When the fixed effect was significant, differences between least squares means were assessed by Tukeys’s test. Statistical significance was declared at p < 0.05.
6
Continuous Variable Analysis Protocol
7
Progesterone and Ultrasound in Aging
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SAS University Edition 3.8 (SAS Institute, Cary, NC, USA) was used in the statistical analysis. The GLM procedure was used to investigate differences in ultrasound parameters and blood plasma progesterone concentrations due to age. Regression analysis in the GLMSELECT procedure was used to investigate the relationship between progesterone concentrations and CLBF. Model performance was assessed by the coefficient of determination (R2), the root of the mean square of prediction error (RMSPE), the RMSPE expressed as a proportion of the observed mean (%RMSPE), and the concordance correlation coefficient (CCC). Furthermore, the mean square of prediction error (MSPE) was decomposed into mean bias (measure of precision), slope bias (measure of accuracy), and random error [9 ]. Statistical significance was declared at p < 0.05.
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