Sas for windows

Manufactured by SAS Institute

Sourced in United States

SAS for Windows is a software application that provides statistical analysis and data management capabilities for Windows-based computers. It offers a comprehensive suite of tools for data manipulation, analysis, and visualization. The core function of SAS for Windows is to enable users to efficiently manage, analyze, and interpret their data.

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Lab products found in correlation

Sas version 9, by SAS Institute (4 mentions) Spss for windows version 21, by IBM (2 mentions) Sas system for windows, by SAS Institute (2 mentions) Sas stat, by SAS Institute (2 mentions) Spss statistics for macintosh, by IBM (2 mentions) Canoco, by Microcomputer Power (1 mentions) Prism software version, by GraphPad (1 mentions) Spss statistics for window, by IBM (1 mentions) Sas stat 14, by SAS Institute (1 mentions) Sas graph, by SAS Institute (1 mentions) Version 9, by SAS Institute (1 mentions) Sigmaplot, by Grafiti LLC (1 mentions) Matlab, by MathWorks (1 mentions) Prism software, by GraphPad (1 mentions) Sas 9.4 ts level 1m1, by SAS Institute (1 mentions) Endnote, by Clarivate (1 mentions)

Close spelling variants of this product

SAS for Window v9.4 SAS for Window release 6 SAS package for Windows SAS system for Windows V 9.1 SAS version 9.2 for Windows 7 SAS/STAT software for Windows version 9.4 SAS 9.2 software for Windows SAS System for Windows x64 Systems SAS® software version 9.2 for Windows® SAS®9.3 version 6.1.7061 for Windows

192 protocols using sas for windows

Childhood Obesity Risk Factors

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Variables were compared using t-test for means and χ² test for proportions between children classified as having normal birth weight and high birth weight. The joint associations of birth weight, time spent in MVPA and sedentary behavior with the risks of obesity, central obesity and high body fat were evaluated using a multi-level (3-level) logistic regression model (SAS version 9.4, PROC GLMMIX) by individual (level 1), nested in school (level 2) and study site (level 3).. Two categories of birth weight (normal and high), MVPA (low and high), and sedentary time (low and high) were used in the analyses. Study sites (level 3) and schools (level 2) nested within study sites were viewed as having random effects. The analyses were adjusted for highest parental education, infant feeding mode, gestational age, child age, sex, unhealthy diet pattern scores, healthy diet pattern scores, and sleeping time. Likelihood ratio test was used to examine the interactions between birth weight and MVPA or sedentary behavior with the odds of obesity, central obesity, and high body fat. The criterion for statistical significance was p<0.05. All statistical analyses were performed using SPSS for Windows, version 21.0 (Statistics 21, SPSS, IBM, USA) or SAS for Windows, version 9.4 (SAS Institute, Cary, NC).

Qiao Y., Zhang T., Liu H., Katzmarzyk P.T., Chaput J.P., Fogelholm M., Johnson W.D., Kuriyan R., Kurpad A., Lambert E.V., Maher C., Maia J.A., Matsudo V., Olds T., Onywera V., Sarmiento O.L., Standage M., Tremblay M.S., Tudor-Locke C., Zhao P, & Hu G. (2017). Joint association of birth weight and physical/sedentary activity with obesity at children ages 9–11 years in 12 countries. Obesity (Silver Spring, Md.), 25(6), 1091-1097.

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ANOVA Analysis of Experimental Parameters

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Statistical analyses were performed using by SAS for Windows (Ver. 9.4, SAS Institute Inc., Cary, North Carolina). An Analysis of variance (ANOVA) followed by a post hoc test using the Bonferroni method was used to compare parameters between each group; P < .05 was considered significant. All data are expressed as the mean ± SD.

Eguchi Y., Murayama S., Kanamoto H., Abe K., Miyagi M., Takahashi K., Ohtori S, & Aoki I. (2019). Minimally invasive manganese‐enhanced magnetic resonance imaging for the sciatic nerve tract tracing used intra‐articularly administrated dextran–manganese encapsulated nanogels. JOR Spine, 2(2), e1059.

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Genetic Associations in Type 2 Diabetes

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The continuous variables were expressed as the mean ± standard deviation or median (interquartile range) and were compared between the T2D and NGT study participants using Student’s t test. Before the association analysis, the T2D patients and NGT subjects performed the Hardy-Weinberg equilibrium test. We excluded the SNPs that failed the Hardy-Weinberg equilibrium test (p<0.01). Tests of normality were conducted for all quantitative traits. The allelic frequencies of the diabetic patients and controls were compared using a χ²-test, and the ORs with 95% CIs are presented. The quantitative traits were analysed by linear regression using the additive model, with adjustments for age, gender and BMI using PLINK [20 (link)], and the regression coefficients ± standard error were presented, with 95% CIs. The skewed distributed quantitative traits, including the fasting and 2-h insulin levels, HOMA-B, HOMA-IR and STUMVOLL, were logarithmically transformed (log₁₀) to approximate the univariate normality. To adjust for multiple comparisons, 10,000 permutations (using PLINK) were performed for each trait to assess the empirical p values. Statistical analyses were performed using SAS for Windows (version 8.0; SAS Institute, Cary, NC, USA), unless otherwise specified. A two-tailed p value of < 0.05 was considered to be statistically significant.

Sun X., Zhang R., Jiang F., Tang S., Chen M., Peng D., Yan J., Wang T., Wang S., Bao Y., Hu C, & Jia W. (2015). Common Variants Related to Serum Uric Acid Concentrations Are Associated with Glucose Metabolism and Insulin Secretion in a Chinese Population. PLoS ONE, 10(1), e0116714.

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Comparison of Ready-to-Eat Cereal Effects

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All values are reported as means with their respective standard errors. Statistical analyses were conducted using SAS for Windows (version 9.1.3 or higher, Cary, NC, USA). Descriptive statistics are presented for the outcome parameters for each RTE cereal. Response differences among RTE cereals were assessed using repeated measures analysis of variance (ANOVA) including subject as a random variable, and test diet as a fixed effect. The model was reduced until only test diet and any significant (p < 0.05) terms remained. Pairwise comparisons between all treatment conditions were conducted using Tukey’s adjustment for multiple comparisons.
All tests of significance, unless otherwise stated, were performed at α = 0.05, two-sided. Assumption of normality of residuals from the final model of each outcome parameter was investigated by the Shapiro–Wilk test [19 (link)].

Lafond D.W., Greaves K.A., Maki K.C., Leidy H.J, & Romsos D.R. (2015). Effects of Two Dietary Fibers as Part of Ready-to-Eat Cereal (RTEC) Breakfasts on Perceived Appetite and Gut Hormones in Overweight Women. Nutrients, 7(2), 1245-1266.

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Rapid Salmonella Detection: LAMP-BART and MDA

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Means and standard deviations of T_max for LAMP-BART and 3M MDA Salmonella and T_t for conventional LAMP were calculated by Microsoft Excel (Seattle, WA). The values were compared using the analysis of variance followed by post-hoc multiple comparisons using the Least Significant Difference (LSD) test (v9.1; SAS for Windows, Cary, NC) and differences were considered significant when P < 0.05. Standard curves to quantify Salmonella in pure culture were generated by plotting T_max or T_t values against log CFU/reaction, and linear regression was calculated using Microsoft Excel. Quantification capabilities of the assays were derived based on the correlation coefficient (R²) values from the standard curves.

Yang Q., Domesle K.J., Wang F, & Ge B. (2016). Rapid detection of Salmonella in food and feed by coupling loop-mediated isothermal amplification with bioluminescent assay in real-time. BMC Microbiology, 16, 112.

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Fitness and Echocardiographic Parameters

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In accordance with standard approaches to the analysis of fitness data, treadmill times were categorized into quartiles using age- and sex-specific thresholds of treadmill performance. This allows each participant to be categorized into one of four mutually exclusive fitness categories that is independent of age and sex, with quartile 1 as the lowest fit group and quartile 4 as the highest fit. In addition, treadmill times from the Balke protocol can also be used to estimate continuous measures of fitness [metabolic equivalents (MET's)](24 (link)).
Baseline characteristics and echocardiographic parameters collected were stratified by gender and then compared across quartiles of fitness using the Jonckheere-Terpstra trend test for all continuous variables and the Cochran-Armitage trend test for categorical variables. The association between fitness and echocardiographic parameters [indexed LA volume(LAVol/BSA), indexed LVEDD( LVEDD/BSA), RWT, EF, E/e′ ratio] were estimated using linear regression with fitness (METs) entered as a continuous independent variable in both age-adjusted and multivariable models adjusted for age, BMI, hypertension, and diabetes. All statistical analyses were performed using SAS for Windows (release 9.2; SAS Institute, Inc. Cary, NC).

Brinker S.K., Pandey A., Ayers C.R., Barlow C.E., DeFina L.F., Willis B.L., Radford N.B., Farzaneh-Far R., de Lemos J.A., Drazner M.H, & Berry J.D. (2014). Association of Cardiorespiratory Fitness with left ventricular remodeling and Diastolic Function: the Cooper Clinic Longitudinal Study. JACC. Heart failure, 2(3), 238-246.

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Descriptive Data Analysis in SAS

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Descriptive data analysis was performed using SAS for Windows. Mean, standard deviation, median, minimum (Min), and maximum (Max) were calculated.

Rocholl M., Weinert P., Bielfeldt S., Laing S., Wilhelm K.P., Ulrich C, & John S.M. (2021). New methods for assessing secondary performance attributes of sunscreens suitable for professional outdoor work. Journal of Occupational Medicine and Toxicology (London, England), 16, 25.

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Quantifying Formyl Adduct Levels

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All pairs of assay duplicates were inspected for possible outliers. When a given pair had more than a 5-fold difference, each value was compared with the mean of all like observations and any value deviating by more than two standard deviations was excluded from analysis. Five exceptionally low values of formyl adducts in volunteer subjects (three in plasma and two in saliva) were excluded.
Statistical modeling and testing were performed with SAS for Windows (v. 9.3, SAS, Cary, NC). Differences in median adduct levels between populations were tested with Wilcoxon rank sums available in the NONPAR1WAY procedure. Random-effect models were fitted to the log-transformed adduct data from repeated blood samples of volunteer subjects using the MIXED procedure. Variance components were estimated by nesting multiple blood specimens within subjects and nesting duplicate assays within blood specimens. This resulted in three variance components, representing variation in (logged) adduct levels across subjects, within subjects and within assays (error term). The coefficient of variation representing technical replicates (CV_e, in natural scale) was estimated as

{CV}_{e} = \sqrt{exp (error variance) - 1}

(Crow and Shimazu, 1988 ).

Regazzoni L.G., Grigoryan H., Ji Z., Chen X., Daniels S.I., Huang D., Sanchez S., Tang N., Sillé F.C., Iavarone A.T., Williams E.R., Zhang L, & Rappaport S.M. (2017). Using lysine adducts of human serum albumin to investigate the disposition of exogenous formaldehyde in human blood. Toxicology letters, 268, 26-35.

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Longitudinal Analysis of Repeated Measures

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The summary measurements are presented as means and standard deviation (SD) unless otherwise stated. Repeatedly measured variables were analyzed using linear mixed model. Pairwise comparisons between different time points were performed only if the overall change over time according to linear mixed model was significant (P < 0.05). Two-tailed P-values are presented, and all analyzes were performed using SAS for windows (version 9.1.3, SAS Institute Inc., Cary, NC, USA).

Hashima J.N., Rogers V., Langley S.M., Ashraf M., Sahn D.J., Ohtonen P., Davis L.E., Hohimer A.R, & Rasanen J. (2015). FETAL VENTRICULAR INTERACTIONS AND WALL MECHANICS DURING DUCTUS ARTERIOSUS OCCLUSION IN A SHEEP MODEL. Ultrasound in medicine & biology, 41(4), 1020-1028.

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Asthma Phenotype Comparative Analysis

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To evaluate the differences in characteristics and parameters among the four asthma phenotypes, we used the t test for independent comparisons between two groups, Fisher exact test for categorical variables, or Kruskal–Wallis test for continuous variables. Multiple testing was conducted using the Bonferroni correction when needed. P < 0.05 was considered significant. All statistical analyses were performed using SAS for Windows (version 9.2).

Lee E., Lee S.H., Kwon J.W., Kim Y.H., Yoon J., Cho H.J., Yang S.I., Jung Y.H., Kim H.Y., Seo J.H., Kim H.B., Lee S.Y., Kwon H.J, & Hong S.J. (2017). Persistent asthma phenotype related with late-onset, high atopy, and low socioeconomic status in school-aged Korean children. BMC Pulmonary Medicine, 17, 45.

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