The results (feed intake, nutrient digestibility, milk production and composition, microbial load, and antioxidant activity) were analyzed as a completely randomized design using the General Linear Model (GLM) procedure of the SAS statistical software (SAS, 2008 ). The model of this design is a completely randomized design with two treatments (CON, without C. erectus; CE, contains C. erectus), and each treatment includes eight replications based on the statistical model: Yij = µ + Ti + eij, where Yij is the observation, µ is the general mean, Ti is the effect of treatments, and eij is the standard error of term. Repeated data per time (blood metabolites) were analyzed as repeated measurements using the MIXED procedures of SAS statistical software (SAS, 2008 ) based on the statistical model: Yijk = µ + Ti + Hj + (T × H)ij + eijk, where Yijk is the observation, µ is the mean of observations, Ti is the effect of treatments, Hj is the effect of sampling day, (T × H)ij is interactions between the effect of treatments and sampling day, and eij is the standard error of term. The differences between treatments were analyzed using an independent t-test. Differences between treatment means were considered significant at P < 0.05.
Sas statistical software
Manufactured by SAS Institute
Sourced in United States, Austria, Japan, Cameroon
SAS statistical software is a comprehensive data analysis and visualization tool. It provides a wide range of statistical procedures and analytical capabilities for managing, analyzing, and presenting data. The software is designed to handle large and complex datasets, allowing users to perform advanced statistical modeling, regression analysis, and data mining tasks. The core function of the SAS statistical software is to enable users to extract insights and make data-driven decisions.
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Lab products found in correlation
R statistical software, by R Project for Statistical Computing (7 mentions)
R software, by R Project for Statistical Computing (5 mentions)
Graph prism, by GraphPad (4 mentions)
Prism 8, by GraphPad (4 mentions)
Sas software, by SAS Institute (4 mentions)
Sas version 9, by SAS Institute (4 mentions)
Proc glot, by SAS Institute (3 mentions)
Stata software, by StataCorp (3 mentions)
Spss version 15, by IBM (3 mentions)
Prism 7, by GraphPad (3 mentions)
Spss software, by IBM (3 mentions)
Jmp pro statistical software, by SAS Institute (2 mentions)
Bellcurve for excel, by Social Survey Research Information (2 mentions)
Software, by MedCalc (2 mentions)
Stata statistical software, by StataCorp (2 mentions)
Sigmaplot, by Grafiti LLC (2 mentions)
Originpro, by OriginLab (2 mentions)
Stata, by StataCorp (2 mentions)
Prism software, by SAS Institute (2 mentions)
Sas system for windows, by SAS Institute (2 mentions)
1 676 protocols using sas statistical software
1
Effect of Cassia erectus on Livestock
2
Analyzing Embryonic Development and Gene Expression
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The average number of embryos per treatment group, number of embryos per pair, survival, hatching success, spawning rates, sex ratios, morphology measurements of offspring, and gene expression array confirmation by qPCR were analyzed using an Analysis of Variance (ANOVA) with a least significant difference (LSD) post-hoc test when a significant ANOVA was observed (α = 0.05) using SAS statistical software. Total body weight and ovary weights between the control and 30 ppb treatment groups was analyzed with a T-test (p < 0.05). Counts of ovarian follicles and corresponding stages of development were assessed using a combination of Chi-square and a generalized linear mixed model (p < 0.05) in SAS statistical software. Hormone analysis was performed using an ANOVA and Tukey’s post-hoc test when a significant ANOVA was observed (α = 0.05). Microarray analysis was completed with an ANOVA and a Tukey’s post-hoc test when a significant ANOVA was observed (α = 0.05). In addition, a mean absolute log2 expression ratio of at least 0.585 (50% increase or decrease in expression) must be satisfied.
3
Comparing Screening Decisions in Older Women
4
Survival Analysis of Gastric Cancer Patients
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Categorical variables were examined using the Chi-Squared test, and continuous variables were assessed using the t-test. To assess the association between the OS of patients with GC after surgery and the level of ATD use, the Kaplan–Meier method was used and the log-rank test employed to examine differences in survival among ATD users. Cox proportional hazard models were used to compare HRs with 95% CIs after adjusting for sex, age, level of urbanization, and income level. A P value of <.05 or 95% CI was considered statistically significant. All analyzes were performed using SAS statistical software (version 9.4; SAS Institute, Cary, NC, USA).
5
Estimating Cataract Surgery Wait Times
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The primary objective was to estimate wait times 1 and 2 for cataract surgery with this method. Wait times 1 and 2 represented the categorical and continuous outcomes. We investigated the proportion of patients who had a wait time 1 or 2 of less than 3, 6 and 12 months, and the proportion that did not meet the target time of 182 days for wait times 1 and 2. The primary analysis used the ranking method. All patients were compared with the wait time targets for priority 4 cases as no information on surgical priority groups were available. To evaluate a differing set of assumptions, secondary analyses computed wait times stratified by referral source and a subgroup analysis evaluated wait times in patients with a single referral and patients with multiple eligible referrals separately. A second subgroup analysis evaluated wait times based on the age of the patient. Temporal patterns in the percentage of patients not meeting provincial wait time thresholds were descriptively recorded. All analyses were conducted at ICES using SAS statistical software (version 9.4).
6
Feeding Regimes on Laying Hen Performance
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The results were expressed as mean ± standard error (SE ). For variables laying performance, a 2-way ANOVA model was used to estimate the main effects of feeding treatment, age, and their interaction with each group as replicate (n = 6) by SAS statistical software (version V8, SAS Institute, Cary, NC). For variables gene expression and blood parameters during the rapid calcification period, a 1-way ANOVA model was used to estimate the main effect of feeding treatment with each hen as replicate (n = 12). For egg quality, a 2-way ANOVA was used to estimate the main effect of feeding treatment, time, and their interaction with each group as replicate (n = 6). The dynamic changes of plasma parameters within a day were analyzed with repeated measurement analysis with each group as replicate (n = 6). When the main effect of the treatment was significant, the differences between means were assessed by Duncan's multiple comparisons test. P < 0.05 was considered to be statistically significant.
7
Laying Hen Performance and Egg Quality
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In the study, data of laying performance, egg quality parameters, aerobic bacterial load of eggshell, serum biochemical parameters, and jejunal histomorphological traits of laying hens were analyzed using the GLM procedures of SAS statistical software (Version 9.1., SAS Institute Inc, 2003 ). Each replicate was considered an experimental unit for performance parameters (n = 4 cages/treatment), egg quality parameters (n = 25 eggs/treatment), aerobic bacterial load of eggshell (n = 15 eggs/treatment), serum biochemical parameters, and jejunal histomorphological traits (n = 20 hens/treatment). Significant differences among the means were compared using the Tukey test and were considered statistically different at a level of P < 0.05. Effects of the LS levels (0.5, 1 and 2 kg per ton of feed) were determined by the contrast analysis of the GLM procedure. Orthogonal polynomial contrasts were also applied to determine the linear and quadratic responses to different levels of LS.
8
Randomized Experimental Design Analysis
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This research was performed in the form of a simple random design with three replications and the achieved results were analyzed with the use of the SAS statistical software (9.3.1, New York, USA). Comparison of data means was performed with the use of Duncan's multirange test with the confidence level of 95%.
9
Cognitive Impairment Prevalence and Predictors
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Descriptive statistics such as frequency and proportion for any discrete variable and mean and standard deviation for any continuous variable are reported. Associations be-tween discrete groups were compared using a chi-square test or Fisher's exact test. Two-group means were compared using a 2-sample t-test. The mean MoCA scores were compared using a 1-tailed t-test for unequal variance. Statistical significance was determined as p < 0.05. Linear regression analysis of MoCA scores was performed for multiple potential factors and predictors. Significant factors were simultaneously controlled for using a multivariable linear regression analysis. The data were analyzed using SAS statistical software (version 3.0, SAS Institute).
10
Excess Mortality and Depressive Symptoms in Mild Dementia
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The differences in the patients' characteristics between the categories of depressive symptoms were tested using either a Kruskal-Wallis test for continuous variables or a Pearson chi-square test for categorical variables.
Cox regression models were used to evaluate the excess mortality in relation to the levels of depression severity in patients with mild dementia. Hazard ratios (HRs) and corresponding 95% confidence intervals (95% CIs) were calculated for all-cause mortality. Both crude and multivariable analyses adjusting for the potential confounding factors (age, sex, smoking, alcohol consumption, education, BMI, household status, MMSE, CCI, QoL-AD, NPIQ, ADSC-ADL, medication, and RCT allocation) were performed.
All test results with p < 0.05 were considered as statistically significant. All analyses were performed using SAS statistical software (version 9.4).
Cox regression models were used to evaluate the excess mortality in relation to the levels of depression severity in patients with mild dementia. Hazard ratios (HRs) and corresponding 95% confidence intervals (95% CIs) were calculated for all-cause mortality. Both crude and multivariable analyses adjusting for the potential confounding factors (age, sex, smoking, alcohol consumption, education, BMI, household status, MMSE, CCI, QoL-AD, NPIQ, ADSC-ADL, medication, and RCT allocation) were performed.
All test results with p < 0.05 were considered as statistically significant. All analyses were performed using SAS statistical software (version 9.4).
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