Sas sas9

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SAS (SAS9.4) is a robust software platform designed for advanced data analysis and business intelligence. It provides a comprehensive suite of tools for data management, statistical analysis, and reporting. The core function of SAS is to enable organizations to extract meaningful insights from their data, supporting informed decision-making.

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Lab products found in correlation

Spss 20, by IBM (2 mentions) Sas 9, by SAS Institute (2 mentions) Sigmaplot 12, by Grafiti LLC (1 mentions) Spss spss v 21, by IBM (1 mentions) Spss version 17, by IBM (1 mentions) Rstudio, by Posit (1 mentions) Version 20, by MedCalc (1 mentions) Statistical package for the social sciences version 25, by IBM (1 mentions) Sigmaplot 13, by Grafiti LLC (1 mentions)

18 protocols using sas sas9

Fatty Acid and Gene Expression Analysis

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The fatty acids content and gene expression level were analyzed by three-way analysis of variance (ANOVA) using the general linear model (GLM) procedure of SAS (SAS9.4, Cary, NC, USA) with the main factors of group, feeding time and tissue. The serum lipid contents were compared by two-way ANOVA using the GLM procedure of SAS (SAS9.4, Cary, NC, USA). Fisher’s protected least significant difference (LSD) was utilized to separate means when a significant main effect was observed or to compare subclass means when main effects or their interactions were significant. Partial correlation analysis was conducted using Proc GLM to analyze the correlation between detected fatty acids and related gene expression and serum lipid levels in each tissue. Stepwise regression analysis was done to predict the main factors contributing to fatty acids composition. Statements of significance were based on p<0.05. Before statistical analysis, arcsin transformation was made for alpha-linolenic acid (C18:3) output for its low percentage.

Wang B., Liu Z., Chen X., Zhang C, & Geng Z. (2023). Green cabbage supplementation influences the gene expression and fatty acid levels of adipose tissue in Chinese Wanxi White geese. Animal Bioscience, 36(10), 1558-1567.

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Gene Expression Analysis Using GLM

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Data in all experiments were analyzed using the general linear model test, using the analysis of variance program of SAS (SAS 9.0, Cary, NC, USA). Mean values between different WT and overexpression lines or different treatments at different times were defined as significant when the value of Fisher’s protected least significant difference (LSD) was lower or equal to 0.05 probability.

Zhuang L., Cao W., Wang J., Yu J., Yang Z, & Huang B. (2018). Characterization and Functional Analysis of FaHsfC1b from Festuca arundinacea Conferring Heat Tolerance in Arabidopsis. International Journal of Molecular Sciences, 19(9), 2702.

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Analyzing Physiological and Gene Expression Data

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Physiological data and qRT-PCR data were subjected to the analysis of variance (ANOVA) analysis of variance according to the general linear model procedure of SAS (SAS 9.0, Cary, NC). Differences between the means were distinguished by Fisher’s protected least significance difference (LSD) test at the 0.05 probability level.

Zhuang L., Yuan X., Chen Y., Xu B., Yang Z, & Huang B. (2015). PpCBF3 from Cold-Tolerant Kentucky Bluegrass Involved in Freezing Tolerance Associated with Up-Regulation of Cold-Related Genes in Transgenic Arabidopsis thaliana. PLoS ONE, 10(7), e0132928.

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Statistical Analysis of Experimental Treatments

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All data was analyzed by using SPSS 20 (IBM, Armonk, NY, USA) and the SAS (SAS 9.1, SAS Institute, Cary, NC). Differences among treatments were determined by using the Fisher’s protected least significance difference (LSD) test at P ≤ 0.05. The two-way ANOVA was made before using Fisher’s LSD.

Tang M., Li Z., Luo L., Cheng B., Zhang Y., Zeng W, & Peng Y. (2020). Nitric Oxide Signal, Nitrogen Metabolism, and Water Balance Affected by γ-Aminobutyric Acid (GABA) in Relation to Enhanced Tolerance to Water Stress in Creeping Bentgrass. International Journal of Molecular Sciences, 21(20), 7460.

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Statistical Analysis of Experimental Treatments

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The data was analyzed by using SPSS 20 (IBM, Armonk, NY, USA) and the SAS (SAS 9.1, SAS Institute, Cary, NC). Differences among treatments were tested by using Fisher’s protected least significance (LSD) test (Dunnett’s test) at a 0.05 probability level. The two-way ANOVA was made before using the Fisher’s LSD.

Liu T., Liu Z., Li Z., Peng Y., Zhang X., Ma X., Huang L., Liu W., Nie G, & He L. (2019). Regulation of Heat Shock Factor Pathways by γ-aminobutyric Acid (GABA) Associated with Thermotolerance of Creeping Bentgrass. International Journal of Molecular Sciences, 20(19), 4713.

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Statistical Analysis of Experimental Data

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SigmaPlot^® 12.5 (Systat software, Inc., Palo Alto, CA, USA) was used for descriptive statistics, and statistical analysis software (SAS, SAS 9.4 Copyright © SAS Institute Inc., Cary, NC, USA) was used for the remaining statistical analysis. Descriptive statistics were presented in the form of proportions for binary variables, means with standard deviations for normally distributed variables, and medians with interquartile ranges for non-normally distributed variables. Fisher’s exact test was used for comparing proportions in 2 × 2 tables, and the Jonckheere–Terpstra test was applied when comparing proportions in 2 × K ordinal tables. Binary logistic regression was applied when a potential predictor variable was measured on a continuum. A p-value < 0.05 was considered statistically significant.

Fehr G., Rigali M., Weller G., Grap S.M., Coleman M., Parekh U., Chinchilli V.M, & Dalal P.G. (2023). Efficacy of Infrared Vein Visualization versus Standard Technique for Peripheral Venous Cannulation in Infant and Toddler Populations: A Randomized Study. Children, 10(10), 1652.

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Chongqing Tobacco Survey Analysis

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SAS (SAS 9.4; SAS Institute, Cary, NC) software was used to clean the original data of the 2020 Chongqing Tobacco Survey. SPSS (SPSS V.21.0, Inc., Chicago, Illinois) software was used to perform essential weighting, non-response correction, and post-stratification correction on the cleaned data. Then, the complex sampling module was used for data analysis. Frequencies between groups were compared statistically using the Rao-Scott χ² test. Logistic regression was used to analyze the factors affecting smoking attempts and behavior. Variables with statistical significance in the univariate logistic regression analysis were subjected to multivariate logistic regression analysis, including: age (25–44, 45–64, and ≥65 years); residence (urban, rural), whether or not had seen the media campaign for tobacco control publicity (yes/no); education level (elementary and lower, junior, senior, college and higher); occupation (government staff, service staff, teacher, medical staff, student, military, unemployed, retirees, or other); and whether or not there was secondhand smoke exposure (yes/no). An inspection level α=0.05 and a difference with p<0.05 were considered statistically significant.

Chen Q., Dai J.N., Chen X.D., Qin T., Lai W.Y, & Wang Y. (2022). Awareness of hazards due to tobacco among people aged 15 years and older in Chongqing, China, in 2020: A cross-sectional analysis. Tobacco Induced Diseases, 20, 112.

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Difficult Intubation Prediction Factors

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The sample size was calculated as the required sample size for an incidence rate of 6.3% for IDS > 5 based on the original report^{[8 (link)]} with a marginal error not exceeding 2% with a 95% confidence level. The calculated sample size was 567 patients. Continuous variables are presented as mean ± standard deviation, and categorical variables are presented as count and percentage. Multiple linear regression modelling was used to estimate the average delay caused by individual IDS factors. Regression modelling was also performed with DSI by subtracting the mean of with an IDS score of 0 (easy intubation) to test the robustness of the original model. A mixed-effects model, with the same intubation performer assumed as the random effect, was also employed to control for the potential effect of autocorrelation of the 12 anaesthesiologists. A similar model was applied to the subjective outcome of the PDI of practice doctors. The 12 DI prediction factors were entered into the model to test the individual and combined effects of IDS factors and common DI prediction risk factors on DSI. All the statistical analyses were 2-sided and evaluated at a significance level of 0.05. All analyses were performed using SPSS (version 17.0, SPSS Inc., Chicago, IL). The generalised linear mixed model was calculated using SAS (SAS 9.4; SAS Institute Inc., Cary, NC).

Kang T.W., Wang J.D., Tsai Y.S., Lin C.R, & Tseng C.C. (2022). Intubation difficulty scale contributors and time delay in clinical practice. Medicine, 101(4), e28724.

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Statistical Analysis of Treatment Effects

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To compare differences among different treatments, all data were subjected to ANOVA using the MIXED procedure of SAS (SAS9.4, Cary, NC, United States). Treatment means were calculated using the LSMEANS statement and means were separated using the PDIFF option. Least square means were compared using the Tukey–Kramer adjustment. The differences were considered to be statistically significant if P ≤ 0.05 or 0.001 < P ≤ 0.01 and were considered extremely significant if the P < 0.001. While 0.05 < P ≤ 0.1 was considered as having a trend of difference.

Chen Y., Xie Y., Zhong R., Liu L., Lin C., Xiao L., Chen L., Zhang H., Beckers Y, & Everaert N. (2021). Effects of Xylo-Oligosaccharides on Growth and Gut Microbiota as Potential Replacements for Antibiotic in Weaning Piglets. Frontiers in Microbiology, 12, 641172.

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Behavioral and Biochemical Analysis of Baicalein Treatment

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Statistical analyses of biochemical and histological data were performed using GraphPad Prism software (GraphPad Software, Inc., La Jolla, CA). Data were analyzed using Student’s t-test or ANOVA with Tukey’s post-hoc analysis for two- or multi-group comparisons, respectively. TFor the time to hidden platform endpoint in MWM task was analyzed with SAS 9.4 (SAS Institute Inc. Cary, NC) software. D, data were summarized as mean ± standard deviation (SD) for each group (i.e., sham, vehicle-CCI, or baicalein-CCI) on each day. PROC TRANSREG in SAS SAS 9.4 (SAS Institute Inc. Cary, NC) software was usedrun to check if a Box-Cox transformation (22 ) was necessary to make the data normally distributed. It was found that the data could be regarded as normally distributed after log-transformation. To test if the three groups had the same rate of decrease over the 5 days, a linear mixed model was built on the log-transformed data, where group and day and their interaction were used as fixed effects, and animal id was used as a random effect. For time to platform under visible condition, data were summarized as mean ± SD for each group. The comparison between groups was performed by one-way ANOVA followed by Tukey’s multiple comparisons on log-transformed data. Statistical significance was set at p<0.05, and data are expressed as mean ± standard deviation.

Kenny E.M., Fidan E., Yang Q., Anthonymuthu T.S., New L.A., Meyer E.A., Wang H., Kochanek P.M., Dixon C.E., Kagan V.E, & Bayır H. (2019). Ferroptosis contributes to neuronal death and functional outcome after traumatic brain injury.. Critical care medicine, 47(3), 410-418.

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