Statistical analysis system

Manufactured by SAS Institute

Sourced in United States

The Statistical Analysis System (SAS) is a software platform designed for statistical analysis, data management, and business intelligence. It provides a comprehensive set of tools for data processing, modeling, and reporting. The core function of SAS is to enable users to efficiently analyze and interpret complex data sets, supporting a wide range of statistical methods and techniques.

Automatically generated - may contain errors

Lab products found in correlation

Spss version 18, by IBM (3 mentions) Statistical package for the social science, by IBM (2 mentions) Stata, by StataCorp (2 mentions) Sas software, by SAS Institute (2 mentions) Statistical package for the social sciences spss 22, by IBM (1 mentions) Miseq platform, by Illumina (1 mentions) Qiaamp fast dna stool mini kit, by Qiagen (1 mentions) Spss version 21, by IBM (1 mentions) Dataassist software v3, by Thermo Fisher Scientific (1 mentions) Spss 22, by IBM (1 mentions) Stata 16, by StataCorp (1 mentions) Office excel 2007, by SAS Institute (1 mentions) Pasw statistics 18, by IBM (1 mentions) Spss statistic, by IBM (1 mentions) Statistical product and service solutions version 20, by IBM (1 mentions) Simca p, by Sartorius (1 mentions) Turbomass 4, by PerkinElmer (1 mentions) Proc rank, by SAS Institute (1 mentions) Spss statistics version 23, by IBM (1 mentions) Spss version 23, by IBM (1 mentions)

Close spelling variants of this product

428 protocols using statistical analysis system

Dietary Tryptophan and Diquat Oxidative Stress

Check if the same lab product or an alternative is used in the 5 most similar protocols

All data, expressed as means with their pooled standard errors, were analysed as a 3 × 2 factorial using the general linear model procedures of the Statistical Analysis System (Version 8.1; SAS Institute, Gary, NC). The factors in the models included the main effects of dietary tryptophan levels (0.18, 0.30 or 0.45%) and diquat injection (diquat or sterile 0.9% NaCl solution) as well as their interaction. Furthermore, all data were also analyzed using one-way ANOVA, followed by Ducan’s Multiple Range test of the Statistical Analysis System (Version 8.1; SAS Institute, Gary, NC). Statistical significance was considered at P < 0.05, tendency at P < 0.10.

Mao X., Lv M., Yu B., He J., Zheng P., Yu J., Wang Q, & Chen D. (2014). The effect of dietary tryptophan levels on oxidative stress of liver induced by diquat in weaned piglets. Journal of Animal Science and Biotechnology, 5(1), 49.

+ Open protocol

+ Expand

Fetal Gene Expression in IUGR Placentomes

Check if the same lab product or an alternative is used in the 5 most similar protocols

Data were subjected to least-squares analysis of variance using the General Linear Models procedures of the Statistical Analysis System (SAS Institute, Cary, NC, USA) and are presented as least-squares means with overall standard error of the mean (SE). There was no effect of fetal sex in the statistical model; therefore, it was removed from the statistical model. Differences in means were considered to be statistically significant when p ≤ 0.05, while p ≤ 0.10 was considered a tendency toward significance. Data from qPCR analyses for placentomes from NR non-IUGR and NR IUGR pregnancies were subjected to least-squares analysis of variance using the General Linear Model procedures of the Statistical Analysis System (SAS Institute, Cary, NC, USA).

Edwards A.K., Dunlap K.A., Spencer T.E, & Satterfield M.C. (2020). Identification of Pathways Associated with Placental Adaptation to Maternal Nutrient Restriction in Sheep. Genes, 11(9), 1031.

+ Open protocol

+ Expand

Pork Quality Traits and Metabolites

Check if the same lab product or an alternative is used in the 5 most similar protocols

All statistical analysis was performed using the Statistical Analysis System (2009) . Pearson correlation coefficients between blood and muscle metabolites or between blood and muscle metabolites and pork quality traits were determined using the CORR procedure. To establish multiple regression models for pork quality traits, blood or muscle metabolites were used as independent variables in the REG procedure. Furthermore, the stepwise procedure was used to examine the percentage of variation in certain pork quality traits explained by blood and muscle metabolites. The qualitative variable (batch) was coded as dummy variables to eliminate their effect on the regression model.

Choe J.H., Choi M.H., Ryu Y.C., Go G.W., Choi Y.M., Lee S.H., Lim K.S., Lee E.A., Kang J.H., Hong K.C, & Kim B.C. (2015). Estimation of Pork Quality Traits Using Exsanguination Blood and Postmortem Muscle Metabolites. Asian-Australasian Journal of Animal Sciences, 28(6), 862-869.

+ Open protocol

+ Expand

Adolescent Smoking Prevalence and Risk Factors

Check if the same lab product or an alternative is used in the 5 most similar protocols

All data analyses were performed using the Statistical Analysis System software package [22 ]. Descriptive statistics were used to estimate smoking prevalence. The chi square test was used to compare the smoking status of the studied adolescents by their sociodemographic characteristics. The level of statistical significance was defined as P ≤ 0.05. The regression analyses were first performed, variable-by-variable (univariate analyses) to estimate odds ratios (OR) and their 95% confidence intervals (95% CI) for the association of adolescents smoking with the studied risk factors. Then, all variables with a statistically significant association with the dependent variable (smoking) were included in the final predictive model based on the stepwise regression with a p value of 0.01 as entry criterion and a p value of 0.05 as exclusion criterion. Finally, the obtained predictors from stepwise model were entered into multivariate logistic regression model while controlling for age, sex, school level and parents' education as possible known confounders.

Al-Zalabani A, & Kasim K. (2015). Prevalence and predictors of adolescents’ cigarette smoking in Madinah, Saudi Arabia: a school-based cross-sectional study. BMC Public Health, 15, 17.

+ Open protocol

+ Expand

Analyzing Experimental Data with GLM

Check if the same lab product or an alternative is used in the 5 most similar protocols

The general linear models (GLM) procedure in the Statistical Analysis System (SAS User’s Guide, 1985, Statistical Analysis System Inc., Cary, NC) was used to analyze the data from all the experiments. Significant differences were determined using Tukey’s multiple range test and P < 0.05 was considered significant.

Shen X., Zhang N., Wang Z., Bai G., Zheng Z., Gu Y., Wu Y., Liu H., Zhou D, & Lei L. (2015). Induction of autophagy improves embryo viability in cloned mouse embryos. Scientific Reports, 5, 17829.

+ Open protocol

+ Expand

Assessing Food Insecurity Prevalence in Emerging Adults

Cited 2 times

Check if the same lab product or an alternative is used in the 5 most similar protocols

Frequencies, percentages, and χ² tests were examined to assess the prevalences of food insecurity in the past year and food insufficiency in the past month across sociodemographic characteristics of emerging adults. Sociodemographic characteristics of interest were assessed as part of the C-EAT survey (ie, sex, parental status, employment status, household receipt of food assistance benefits, living situation, vehicle ownership) or baseline EAT 2010 survey (ie, ethnicity/race, parental SES) and examined within the full sample of 720 survey respondents.¹² (link)
^,¹⁸ (link) The statistical significance of probability tests was determined based on the criteria P < 0.05. Analyses were conducted using the Statistical Analysis System.²⁰

Larson N., Alexander T., Slaughter-Acey J.C., Berge J., Widome R, & Neumark-Sztainer D. (2021). Barriers to Accessing Healthy Food and Food Assistance During the COVID-19 Pandemic and Racial Justice Uprisings: A Mixed-Methods Investigation of Emerging Adults’ Experiences. Journal of the Academy of Nutrition and Dietetics, 121(9), 1679-1694.

+ Open protocol

+ Expand

Gene Expression Data Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols

The general linear model (GLM) procedure within the Statistical Analysis System (SAS User’s Guide, 1985, Statistical Analysis System Inc., Cary, NC. USA) was used to analyze data from all experiments. A paired Student’s t-test was used to compare relative gene expression. P values of <0.05 were considered significant.

Park Y.G., Lee S.E., Kim E.Y., Hyun H., Shin M.Y., Son Y.J., Kim S.Y, & Park S.P. (2015). Effects of Feeder Cell Types on Culture of Mouse Embryonic Stem Cell In Vitro. Development & Reproduction, 19(3), 119-126.

+ Open protocol

+ Expand

Analyzing Experimental Data with GLM

Check if the same lab product or an alternative is used in the 5 most similar protocols

Shen X.H., Jin Y.X., Liang S., Kwon J.W., Zhu J.W., Lei L, & Kim N.H. (2018). Autophagy is required for proper meiosis of porcine oocytes maturing in vitro. Scientific Reports, 8, 12581.

+ Open protocol

+ Expand

Comparative Analysis of Phenotypic Traits

Check if the same lab product or an alternative is used in the 5 most similar protocols

All data were shown as mean ± standard error (SE) and all analyses were conducted with using the Statistical Analysis System (Cary, NC, USA, version 9.2). The differences between two groups were subjected to the independent sample t-test (P < 0.05). The differences among different groups were subjected to a least significant difference (LSD) test at the P < 0.05. ^* and ^** indicate significant differences at P < 0.05 and P < 0.01, respectively, compared with PH-1.

Kong X., van Diepeningen A.D., van der Lee T.A., Waalwijk C., Xu J., Xu J., Zhang H., Chen W, & Feng J. (2018). The Fusarium graminearum Histone Acetyltransferases Are Important for Morphogenesis, DON Biosynthesis, and Pathogenicity. Frontiers in Microbiology, 9, 654.

+ Open protocol

+ Expand

Analysis of Participant Characteristics and Satisfaction in Arthritis Program

Check if the same lab product or an alternative is used in the 5 most similar protocols

We computed descriptive statistics (n and % or mean, SD, minimum, and maximum) for participant characteristics, self-reported behaviors and program satisfaction questions, using the Statistical Analysis System (Cary, NC), version 9.3. To compare the participant characteristics to the BRFSS sample of persons with arthritis, we conducted Chi-squared analyses of categorical data.
For the open-ended questions in the participants’ self-administered questionnaires, two research team members independently coded verbatim responses and organized them into thematic categories. They compared their independent coding results and reached consensus for any areas of disagreement in their initial coding. The categorization of responses under meaningful themes was not mutually exclusive. Rather, the coders assigned all conceptual codes that were applicable to the written responses for each open-ended question; therefore, percentages may not add to 100% and are reported below provide an indication of the relative rank of the themes identified in responses to each question.

Sharpe P., Wilcox S., Schoffman D, & Baruth M. (2016). Participation, satisfaction, perceived benefits, and maintenance of behavioral self-management strategies in a self-directed exercise program for adults with arthritis. Evaluation and program planning, 60, 143-150.

+ Open protocol

+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!