Data were first analyzed for normality of distribution using the Kolmogorov–Smirnov and Shapiro–Wilk tests. As the data were not normally distributed, a non-parametric (Wilcoxon test) comparison among different pathogens and APPs, and Dunn’s post hoc analysis (using Bonferroni error correction to adjust for multiple comparisons) were performed. A non-parametric (Spearman’s correlation) analysis was employed to check the correlations among the Hp, SAA, AGP, and CRP concentrations and correlation between the different methods of Hp mensuration, ELISA and SPARCL. p-values were considered significant at p < 0.05. All tests were performed using the software SAS University® (SAS Institute Inc., Cary, NC, USA).
Sas university
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SAS University is a free, downloadable software that provides access to SAS analytics and data management capabilities. It enables users to learn and explore SAS programming and data analysis tools.
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Lab products found in correlation
10 protocols using sas university
1
Comparing Acute Phase Proteins in Pathogens
2
Metagenomic Diversity Analysis and Shelf-life Assessment
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Parameters obtained on the diversity index from the metagenomic analysis were compared for each treatment and analyzed using the F-test, assuming the quantitative approach planned test focused on variance analysis (p < 0.05). For comparison of times of shelf-life from spoilage assessment, Duncan's test (α = 0.05) was applied. The values of pH, mesophilic, and LAB counts were compared to each storage time for control, CSP, and CB treatments by the F-test, assuming the quantitative planned test approach with Duncan's test. Moreover, these kinetics were fitted to the smoothed lines based on the second-order polynomial model and moving average for microbial counts of CSP treatment. The statistical software SAS University was used for the analyses (SAS Institute, Inc., Cary, NC).
3
Cardiovascular Response to Chemotherapy
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Mean (SD), median, IQR and range are used for presenting continuous variables, while categorical variables are presented as frequencies. To evaluate changes in parameters of interest after chemotherapy compared to baseline, the Wilcoxon signed-rank test was used. Univariable linear regression analyses were performed in order to identify factors significantly associated with the changes in SBP, Aix75, PWVc-r and PWVc-f, the change (after-before chemotherapy) was used as the dependent variable and baseline patient, treatment and laboratory characteristics (including gender, age, BMI, treatment type, treatment duration) were included as independent variables. All statistical analyses were performed using SAS University. Statistical significance was set at p=0.05 (two-sided).
4
Comparative Statistical Analysis Methods
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Descriptive statistics were reported using mean and standard deviation (SD) for continuous variables and using absolute frequencies and percentages for categorical variables. Comparative analyses were made using Student tests or Analysis of Variance tests for quantitative variables and Chi 2 tests or exact Fisher tests for qualitative variables. p-values of 0.05 or less were considered statistically significant. Statistical analyses were performed using SAS® University (SAS Institute, North Carolina, USA) and Excel® (Microsoft Corporation, Redmond, WA, USA).
5
Assessing Persistent Organic Pollutants and Fatty Acids in Multigenerational Rodent Study
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Data were analyzed using SAS University (Copyright © SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513, USA) using Mixed Procedure with a one-way analysis of variance (ANOVA) in 2 × 2 factorial design. Main effects of POPs, FA and interaction (POPs*FA) were considered. The number of pups/litter and the F0 females were also included in the expanded model and excluded when not significant. To increase diversity and minimize the effect of the F0 dams, male lineages were derived from an equivalent number of the F0 founder females throughout the four generations. Differences were considered significant at p ≤ 0.05.
6
SARS-CoV-2 Seroprevalence Trends Among Unvaccinated
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All statistical tests were performed using SAS University. Descriptive data are summarized as counts and percentages To account for testing error, adjusted seroprevalence was calculated using the formula: [17 (link)]. To remove the influence of COVID-19 vaccination on seroprevalence trends, we present seroprevalence stratified by month of serology testing only among participants who were not vaccinated for COVID-19 at the time of serological testing. Univariable logistic regression was used to examine the association between SARS-CoV-2 seropositivity and age, sex, work setting, work role, children in the household and vaccination status. Among a subgroup of participants that had not yet received the COVID-19 vaccine at the time of serology testing we also conducted logistic regression analyses to assess risk for infection (combining participant self-report of prior infection and seropositivity among unvaccinated individuals).
7
Factors Associated with Opioid Use in Chronic Conditions
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The two study groups (pain–hypertension group and pain–hypercholesterolemia group) were developed in accordance with the criteria set out above. Chi-square tests were used to determine statistically significant differences between opioid users and non-users. Several hierarchical logistic regression models were developed to identify the factors associated with opioid use. The first logistic regression model included predisposing factors, while subsequent models adjusted for an additional group of factors (enabling, need, personal health practices, and external environmental). Analyses were conducted using SAS University (SAS institute Inc., Cary, NC, USA) and accounted for the complex survey design of MEPS to obtain national estimates. The University of Arizona Institutional Review Board approved this study (1912255773).
8
Arthropod Guild Community Analysis
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Data of arthropods species were grouped based on their guild, comprised of herbivore insects, predators, parasitoids, and neutral insects, and was presented in the form of a graphic. The difference of individual quantity of the herbivore insects, spiders, and predatory insects amongst treatments was analyzed using Analysis of Variance (ANOVA). If there was a significant difference among treatments, the analysis was continued with Tukey's HSD (Honesty Significant Difference) test at 5% degree of significance. Analyses data was conducted using Software of SAS University Edition 2.7 9.4 M5. Data of abundance were used to analyze species diversity by using the Shannon index (H'). Degree of diversity was counted using the Evenness index (J') derived from the Shannon function, and Berger-Parker dominance biodiversity indices. The coefficient of Sorensen was counted to measure the degree of similarity of the spider or predatory insect among treatments (Magurran 2004) .
9
Maternal Diet Impacts Offspring Body Composition
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Body composition parameters were analysed with the statistical software SAS University (SAS Institute Inc., Cary, NC) using a GLM with maternal dietary treatment of the F0 generation,dietary treatment of the F1 generation and their interaction as classification variables.
10
Maceration Process Monitoring and Analysis
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Data were submitted to ANOVA and Tukey means test (p ≤ 0.05) by the software SAS ® University (S.A.S. Institute Inc, Cary, NC, USA). Predictive models for monitoring the maceration process were built using polynomial regression and the statistical software OriginPro ® 8.0 (OriginLab Corporation, Northampton, MA, USA). Principal Component Analysis (PCA) was generated using Pearson correlation matrix, additional variables (antioxidant activity values) and the XLStat software (Addinsoft Inc., Anglesey, UK). Additionally, Pearson correlation analysis was also applied to identify the positive and negative correlations between phenolic compounds and antioxidant activity at 5% level of significance.
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