The experiment was laid out using a randomized complete block design that considered the rice lines and drought treatments as the primary sources of variation. Data from all measurements of the root and shoot parameters were documented, and descriptive analysis, including means, standard deviations (SD), coefficients of variation (CV), and analysis of variance (ANOVA), were calculated for the parameters under drought and control treatments using the SAS statistical software packages (SAS Institute, Inc., Cary, NC, USA). The data were analyzed using a one-way ANOVA via PROC GLM in SAS to determine the effect of drought on the shoot and root growth and developmental and physiological parameters. The Fisher’s protected least significant difference test at p = 0.05 was employed to test differences among the treatments for the measured parameters. The standard errors of the mean were calculated using Sigma Plot 13.0 (Systat Software, Inc., San Jose, CA, USA) and presented in the figures as error bars.
Sas statistical software package
Manufactured by SAS Institute
Sourced in United States
SAS statistical software package is a comprehensive analytics platform that provides a wide range of tools for data analysis, modelling, and reporting. The software offers advanced statistical methods, data management capabilities, and visualization tools to help users extract insights from their data. The core function of the SAS statistical software package is to enable users to perform statistical analysis, predictive modelling, and decision-making support.
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Lab products found in correlation
Sigmaplot 13, by Grafiti LLC (1 mentions)
Spss version 17, by IBM (1 mentions)
Stata software package, by StataCorp (1 mentions)
Stata, by StataCorp (1 mentions)
Spss statistic, by IBM (1 mentions)
Stata statistical software, by StataCorp (1 mentions)
Originpro software version 9, by OriginLab (1 mentions)
151 protocols using sas statistical software package
1
Drought Response in Rice Lines
2
Predicting Pregnancy Complications in Diverse Populations
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OBSTETRICS Original Research classified as East Asian and Latin Hispanic and Indigenous participants were classified as mixed.
We reported the C-statistic for PE and preterm PE based on the FMF predicted risk. We computed the detection rate (sensitivity), false-positive rate (1specificity), positive and negative predictive values, positive and negative likelihood ratios, diagnostic odds ratio (OR) (odds of a positive test among the cases divided by the odds of a positive test among the noncases), and accuracy (true positive and true negative divided by total observations) of the screening test. The individual risks estimated by the FMF algorithm were dichotomized into positive or negative screening tests using risk cutoffs of 1 in 70 or 1 in 100. The chosen cutoffs were based on previous reports on the algorithm corresponding to the estimated risk cutoffs for a 10% to 15% false-positive rate. 1, 17 The performance of the test was measured for PE, preterm PE, SGA of <10th percentile and <3rd percentile, and fetal death. We also created a composite outcome, including all of the above complications, and a severe composite outcome, including preterm PE, SGA of less than third percentile, and intrauterine fetal death.
Analyses were conducted using SAS statistical software packages (version 9.3; SAS Institute Inc, Cary, NC). A type I error of 5% was considered in all analyses.
We reported the C-statistic for PE and preterm PE based on the FMF predicted risk. We computed the detection rate (sensitivity), false-positive rate (1specificity), positive and negative predictive values, positive and negative likelihood ratios, diagnostic odds ratio (OR) (odds of a positive test among the cases divided by the odds of a positive test among the noncases), and accuracy (true positive and true negative divided by total observations) of the screening test. The individual risks estimated by the FMF algorithm were dichotomized into positive or negative screening tests using risk cutoffs of 1 in 70 or 1 in 100. The chosen cutoffs were based on previous reports on the algorithm corresponding to the estimated risk cutoffs for a 10% to 15% false-positive rate. 1, 17 The performance of the test was measured for PE, preterm PE, SGA of <10th percentile and <3rd percentile, and fetal death. We also created a composite outcome, including all of the above complications, and a severe composite outcome, including preterm PE, SGA of less than third percentile, and intrauterine fetal death.
Analyses were conducted using SAS statistical software packages (version 9.3; SAS Institute Inc, Cary, NC). A type I error of 5% was considered in all analyses.
3
Light spectrum effects on plant physiology
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The physiological analyses experiment, i.e., TPC, antioxidant capacity, and anthocyanin content were designed according to a randomized complete block design (RCBD) with four replications (three plants of each cultivar per replication). Statistical analysis was conducted with IBM SPSS (IBM Corporation; Armonk, NY, USA). One-way analysis of variance (ANOVA) was used to evaluate the data for each parameter. The mean ± SE (standard error) were analyzed with Duncan’s multiple range test (DMRT) using significance tested at the p < 0.05 level.
In order to investigate the relationships between different variables in two cultivars and two developmental stages: biomass accumulation, physiological response parameters and antioxidant capacity and anthocyanin content depending on the four different light spectrum ratios (T1; 3R:1B, T2; 1R:1B, T3; 2R:1G:2B and T4; 1R:3B). A principal component analysis (PCA) was conducted using the SAS statistical software package (Cary, NC). Further, the relationship between traits (physiological response, antioxidant capacity and anthocyanin content) were assessed using Pearson’s correlation coefficient test. Additionally, a hierarchical clustering analysis was performed using Ward’s method (also in SAS) which outputs results in heat map format to facilitate a visual assessment of clustering and key determinants.
In order to investigate the relationships between different variables in two cultivars and two developmental stages: biomass accumulation, physiological response parameters and antioxidant capacity and anthocyanin content depending on the four different light spectrum ratios (T1; 3R:1B, T2; 1R:1B, T3; 2R:1G:2B and T4; 1R:3B). A principal component analysis (PCA) was conducted using the SAS statistical software package (Cary, NC). Further, the relationship between traits (physiological response, antioxidant capacity and anthocyanin content) were assessed using Pearson’s correlation coefficient test. Additionally, a hierarchical clustering analysis was performed using Ward’s method (also in SAS) which outputs results in heat map format to facilitate a visual assessment of clustering and key determinants.
4
Statistical Analysis of Experimental Data
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The results were expressed as mean ± SD and analyzed using the SAS statistical software package (SAS Institute, Cary, NC, USA). The t-test was used when comparing two independent samples and the ANOVA test was used when comparing multiple samples. Differences with a p value of less than 0.05 were considered statistically significant.
5
Analyzing Regional Variations in Mortality-Related Exposures
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All analyses were conducted for the entire study population living in the United States as well as separate analyses for Medicare beneficiaries living in each of three US regions (East, Central, and West). In general, we examined the variation in MRR estimates per 10 μg/m3 increase in exposure; although for analyses comparing MRRs for base to those for time-adjusted models, we make comparisons based on an interquartile range (IQR) increase in exposure given their different variabilities. We further present graphical summaries of this variation using linear regression. SAS statistical software package (SAS Institute Inc., Cary, NC, 2003) and R-Studio, Inc., (Boston, MA) were used for all analyses.
6
Evaluating Smoking Cessation Interventions
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To identify tobacco deprivation effects on withdrawal symptoms and smoking urges, presmoking assessments during “baseline” and “deprivation” sessions were compared. To determine if EC or CC use altered smoking urges and withdrawal symptoms, reports of withdrawal symptoms and smoking urges were compared prior to, and following, EC or CC administration during deprivation sessions. Separate mixed models were used to test for effects between each time point, with dose condition as a variable only for models examining change in smoking urges and withdrawal symptoms before and after CC or EC use during deprivation sessions. When significant effects of dose condition were found, post hoc analyses were conducted using t-tests to examine differences between least-square means of each dose condition. Hochberg's step-up procedure [11 (link)] was used to control error rates for each family of pairwise comparisons. Mixed models were fit using PROC Mixed in the SAS statistical software package, version 9.3 (SAS Institute Inc., Cary, NC).
7
Statistical Analysis of Research Data
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The results were analyzed using the SAS statistical software package (SAS Institute Inc., Cary, USA). All of the results are expressed as the mean ± SD. A t-test was used to compare two independent trials. Differences of p < 0.05 were considered to be statistically significant.
8
Associations of Social Engagement and Mortality
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We used χ2 test and Cox proportional hazards models to investigate the association between social engagement and all-cause mortality. Further, this study used growth mixture modeling to estimate trajectory classes of social engagement over time. Growth mixture model provides a method by which we can develop a probable representation of unobserved group classification and group differences – based on observed information and user specified constraints. Once the social engagement trajectory classes were derived from the growth mixture models for identifying homogeneous subpopulations within the larger heterogeneous population and for describing longitudinal change within each unobserved sub-population and examining differences in change among unobserved sub-populations, the classes were then coded into a series of dummy variables to examine the relationship between the patterns of social engagement over time and mortality using Cox proportional hazards models, which are semiparametric models that do not assume a specific hazard function. For all analyses, the two-tailed criterion for statistical significance was P ≤ 0.05. All analyses were conducted using the SAS statistical software package, version 9.2 (SAS Institute Inc., Cary, NC, USA).
9
Interrupted Time Series Analysis of KDPC
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Segmented regression analysis of interrupted time series with control was carried out for analysis in this study, using the following equation (Equation 1):
Yt: average length of stay of month t
t: time period (month)
time: a continuous variable started in January 2007 by month
Case: a binary variable (0 control hospitals; 1 case hospitals)
KDPC implementationt: a binary variable (0 before June 2012; 1 after July 2012)
time after KDPC implementationt: a continuous variable started in July 2012
Seasont: seasonality (1 spring, 2 summer, 3 autumn, 4 winter)
μpZp: independent variables (1···p)
10
Microbiological Approaches Comparison
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Characteristics of the study groups and the performances of microbiological approaches were compared using the Wilcoxon rank-sum or McNemar’s test. P < .05 were considered statistically significant. Analyses were performed using the SAS statistical software package, version 9.1 (SAS Institute, Cary, NC) and R, version 3.1.3.
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