Sas 9.3 version

Manufactured by SAS Institute

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SAS 9.3 is a software version designed for statistical analysis and data management. It provides a suite of tools for data manipulation, statistical modeling, and reporting. The core function of SAS 9.3 is to enable users to efficiently analyze and interpret data, supporting decision-making processes.

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SAS 9.4 (20063 citations) SAS version 9.4 (18810 citations) SAS version (194 citations) SAS version 9.3 for Windows (44 citations) Version 9.4 (20 citations) SAS version 9.3 statistical software (19 citations) SAS version 9.3 or higher (13 citations) SAS Version 9.3 TS Level 1M2 (10 citations) SAS version 9.3 forwindow (4 citations) SAS version 9.3 or 9.4 (4 citations)

10 protocols using sas 9.3 version

Statistical Analysis of Experimental Data

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Data were expressed as the mean ± standard error of means (SEM). All data were analyzed by the Student t test and a P value of 0.05 or less was considered statistically significant (SAS 9.3 version; SAS Institute Inc., Cary, NC, USA).

Pan J.H., Lim Y., Kim J.H., Heo W., Lee K.Y., Shin H.J., Kim J.K., Lee J.H, & Kim Y.J. (2017). Root bark of Ulmus davidiana var. japonica restrains acute alcohol-induced hepatic steatosis onset in mice by inhibiting ROS accumulation. PLoS ONE, 12(11), e0188381.

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Soybean Agronomics and Quality Analysis

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Factorial analysis of variance for all collected data was performed following the procedure of Gomez and Gomez (1984) , using the statistical analysis system computer software SAS 9.3 version (SAS Institute Inc, 2008 ). The normality of the data was tested by using the scatter plot technique. The treatments that showed significant differences was subjected to least significant difference (LSD) test for mean separation at 0.05 probability level. Furthermore, correlation analysis was made to determine the relationship between the agronomics and quality attributes of soybean. Regression analysis was used to determine the relationship of grain yield with other selected plant traits and oil content with protein content.

Agegn A., Bitew Y, & Ayalew D. (2022). Response of yield and quality of soybean [Glycine max (L.) Merrill] varieties to blended NPSZnB fertilizer rates in Northwestern Ethiopia. Heliyon, 8(5), e09499.

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Multivariate Analysis of Antihypertensive Medication Adherence

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All the analyses were performed using statistical analysis software (SAS) 9.3 version. Descriptive analysis was used for continuous variables: mean, SD, standard error (SE) and categorical variables: frequency tables: absolute and relative frequencies. MPR for FG-LR was categorized in ranges of 0≤MPR<0.3, 0.3≤MPR<0.5, 0.5≤MPR<0.8, and 0.8≤MPR<1. MPR was analyzed using descriptive statistics, whereas factors affecting MPR were analyzed using multivariate regression analysis. Analysis of covariance was used to evaluate variation in MPR values among AHT drugs. P-value of <0.05 was considered significant.

Cui B., Dong Z., Zhao M., Li S., Xiao H., Liu Z, & Yan X. (2020). Analysis of Adherence to Antihypertensive Drugs in Chinese Patients with Hypertension: A Retrospective Analysis Using the China Health Insurance Association Database. Patient preference and adherence, 14, 1195-1204.

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Secondhand Smoke Exposure in Appalachia

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Age of participants was presented as mean ± standard deviation (SD), all other continuous or discrete variables were presented as medians (interquartile range, IQR), frequencies, or percentages as appropriate. Serum cotinine levels were log-transformed due to skewed distribution. Approximately 51% (n=162) of the samples in our study were below the level of detection (0.05 ng/mL), however, reported actual values were used for statistical analyses. SHS exposure prevalence in our study cohort were compared to (1) national estimates using NHANES 2007–2008 data1 and (2) estimates from total states containing Appalachian counties (n=13), and Ohio, using the 2007 National Survey of Children’s Health (NSCH) data.11 A two-tailed P-value of <0.05 was used to judge statistical significance. All analyses were performed using SAS 9.3 version (SAS Institute Inc., Cary NC).

Yeramaneni S., Yolton K., Kannan K., Dietrich K.N, & Haynes E.N. (2019). Serum Cotinine versus Parent Reported Measures of Secondhand Smoke Exposure in Rural Appalachian Children. Journal of Appalachian Health, 1(1), 15-26.

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Echocardiographic Measurements: Retrospective and Prospective Analysis

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Descriptive statistics were used to summarize demographic and echocardiographic measures. Continuous variables were summarized as mean ± SD, or mean (10^th, and 80^thpercentile [P10, P80]), as appropriate. Categorical variables were presented by the absolute and relative frequencies or as numbers and percentages. Comparisons between the retrospective and prospective samples were performed using independent-samples t test or Mann-Whitney U test for continuous variables and chi square or Fisher’s exact test for categorical variables. Intraobserver and interobserver variability of IVSd, LVPWd, LVEDD, LVESD and LVM measurements were determined in 100 randomly selected patients using intraclass correlation coefficient (ICC). A 2-sided p value < 0.05 was considered statistically significant and performed by using SAS 9.3 version (SAS Institute, Inc, Cary, NC).

Choudhry S., Salter A., Cunningham T.W., Levy P.T., Nguyen H.H., Wallendorf M., Singh G.K, & Johnson M.C. (2017). Normative Left Ventricular M-mode Echocardiographic Values in Preterm Infants Up To Two Kilograms. Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 30(8), 781-789.e4.

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Statistical Analysis of Experimental Data

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All the collected data were first checked for fitting the analysis of variance (ANOVA) assumptions. Then, all data were subjected to analysis of variance (ANOVA) using SAS 9.3 version (SAS, 2012 ). Means were compared by using least significant difference (LSD) test at 5% probability level. The significant difference between the treatment mean were separated by using a letters from a to z. Homogeneity of the variance (Bartlett's test) was tested by using Minitab 15 (Minitab version 15, Minitab Inc., State College, PA, USA) statistical software to check the validity of the data and transformation of data was carried out for those who failed the test. The presentation of the mean values was done after retransformation of the transformed data's were carried out.

Hailegeorgis A, & Anbesse D. (2021). Effect of harvesting stage, form and duration of storage on some physico-chemical properties of Korarima (Afframomum (Braun) P.C.M. Jansen) collected from Masha Woreda, Sheka Zone, Southwestern, Ethiopia. Heliyon, 7(11), e08271.

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Secondhand Smoke Exposure in Appalachia

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Age of participants was presented as mean ± standard deviation (SD), all other continuous or discrete variables were presented as medians (interquartile range, IQR), frequencies, or percentages as appropriate. Serum cotinine levels were log-transformed due to skewed distribution. Approximately 51% (n=162) of the samples in our study were below the level of detection (0.05 ng/mL), however, reported actual values were used for statistical analyses. SHS exposure prevalence in our study cohort were compared to (1) national estimates using NHANES 2007–2008 data¹ and (2) estimates from total states containing Appalachian counties (n=13), and Ohio, using the 2007 National Survey of Children’s Health (NSCH) data.¹¹ A two-tailed P-value of <0.05 was used to judge statistical significance. All analyses were performed using SAS 9.3 version (SAS Institute Inc., Cary NC).

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Normality Testing and Variance Homogenization

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All the data were tested for the normality using Sapiro -Wilkin test. Non normal data was normalized by square root transformation then Homogeneity of error variances of nonsegregating generations was tested by using Bartlett's test (Bartlett, 1937) , and when the variances were heterogeneous at 1 % and 5% level of significance the data was transformed by dividing observations of each environment/year by the square root of MSE of that environment/year which makes the error variances homogeneous and pooled analysis was performed on transformed data. SAS 9.3 version was utilized for ANOVA, correlation and regression calculations.

Choudhary P. (2021). Degree of Sporulation (SP) and Normalized Difference Vegetation Index (NDVI) as Important Parameter for Characterizing Field Resistance against Cercospora Leaf Spot (CLS) in Mungbean. International Journal of Current Microbiology and Applied Sciences, 10(01), 537-544.

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Genetic Associations with Major Depressive Disorder and Obesity

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For association studies, chi-square (Chi 2 ) or Fisher exact tests for binomial variables were used. Differences in genotype frequencies as well as deviation from Hardy-Weinberg equilibrium were assessed using Chi² test. All genetic analyses for CRTC1 SNPs were performed using a dominant model (wild type vs. variant allele carriers), the same model used for the previously mentioned psychiatric samples (Choong et al., 2013) .
Multivariable regression analyses were used to test the association between the CRTC1 SNPs and MDD adjusting for age, sex and BMI. For PsyCoLaus data, a Generalized Linear Model (GLM) adjusted for age and sex was used to test the association between CRTC1 rs6510997C>T polymorphism and obesity markers (BMI, fat mass and waist circumference) using SAS 9.3 version (SAS Institute Inc., Cary, NC, USA). In a first step, we tested for the interaction between CRTC1 SNPs and MDD status for an effect on obesity markers. For the Radiant and NESDA/NTR studies BMI was the only available obesity marker. For the data of these studies, linear regression models adjusted for age, sex and principal components were used to test the association between CRTC1 polymorphisms and BMI.

Quteineh L., Preisig M., Rivera M., Milaneschi Y., Castelao E., Gholam-Rezaee M., Vandenberghe F., Saigi-Morgui N., Delacrétaz A., Cardinaux J.R., Willemsen G., Boomsma D.I., Penninx B.W., Ching-López A., Conus P, & Eap C.B. (2016). Association of CRTC1 polymorphisms with obesity markers in subjects from the general population with lifetime depression. Journal of affective disorders, 198.

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Oral Cavity Cancer Epidemiology Analysis

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Softwares such as © SAS 9.3 version (SAS Institute Inc, Sas North Carolina, US), © MS-FoxPro 6.0 version (MS-FoxPro, Washington State, US), MS-Excel, IARC/IACR tools, and IARC-crg. Tools were used for data collection, sorting, check, and statistics. In our study, we calculated several variables including crude incidence rate, mortality, China age standardized rate (National Population Structure in 2000), world age standardized rate (world Segi's population), cumulative rate, age-specific rate, truncated rate, and so on. Cumulative rate expresses the probability of the onset of oral cavity cancer between birth and specific age (74 years old). Truncated rate is the calculation of rates over the truncated age of 35-64 years old, using WHO world standard population.

Zheng C.M., Ge M.H., Zhang S.S., Tan Z., Wang P., Zheng R.S., Chen W.Q, & Xia Q.M. (2015). Oral cavity cancer incidence and mortality in China, 2010. Journal of cancer research and therapeutics, 11 Suppl 2.

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