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Sas system for windows release 9

Manufactured by SAS Institute
Sourced in United States

The SAS System for Windows release 9.3 is a comprehensive software suite that provides a wide range of analytical and data management capabilities. It includes tools for data access, data manipulation, statistical analysis, and reporting. The software is designed to work on the Windows operating system.

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5 protocols using sas system for windows release 9

1

Survival Analysis of Medical Intervention

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The data are given as n (%), mean (SD), median, and range, or mean mortality rate per 1,000 patient‐years. For comparisons of categorical variables, Fisher's exact test was used. Median survival ages with 95% confidence intervals (CIs) were obtained from life tables. Hazard ratios 9HRs) with 95% CI were calculated from both univariate and multivariate Cox regression models. A p‐value of <0.05 was considered statistically significant. Statistical computations were done using SAS System for Windows, release 9.3 (SAS Institute, Cary, NC, U.S.A.).
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2

Prenatal Stress Impacts Dopamine

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Statistical analyses were conducted using the SAS system for Windows, release 9.3. In order to control for litter effects, body weight, DA content, and performance in behavioral assessments were analyzed using a linear mixed model with dam as a random effect nested in treatment groups. Planned comparisons using a t-test were performed following each ANOVA. A one-way ANOVA was performed to compare differences in average dam weights throughout gestation between experimental groups. Means and standard errors (SE) were calculated as simple statistics and presented graphically using Graphpad Prism 6.
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3

Insulin and Rapamycin Effects on Protein Expression and mRNA Abundance

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Data were analyzed using the MIXED procedure of SAS System for Windows Release 9.3 (SAS Institute Inc., Cary, NC). Data were analyzed as a completely randomized design. For insulin and time titration experiments for protein expression data, the model contained the effects of insulin and time, along with their interaction; to account for nonconstant variance in the data, all values were log transformed before being modeled. Each representative was modeled as a random blocking factor with a Kenwood‐Rodgers degrees of freedom adjustment. For insulin × rapamycin experiments for protein expression data and AAT mRNA abundance data, the model contained the effects of insulin and rapamycin, and along with an interaction term. Each representative was modeled as a random blocking factor. For insulin and time titration experiments for AAT mRNA abundance data, the model contained the effects of insulin and time, and along with an interaction term. Each representative was modeled as a random blocking factor with a Kenwood‐Rodgers degrees of freedom adjustment. Treatment means were computed using the LSMEANS option, and pairwise t‐tests were used to separate means and significance was determined at P < 0.05. All data are presented at the original scale with standard errors.
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4

Burden of Diseases and Injuries Worldwide

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Regional classifications are based on the reporting regions of the
Global Burden of Diseases, Injuries, and Risk Factors 2010 Study21 (link) with the exception that we
broke out India and China separately because of their large population sizes.
Regional and global data were weighted by national population sizes. All
statistical analyses were completed using SAS System for Windows release 9.3
(SAS Institute, Cary, North Carolina) and the R statistical package V 3.0. Data
are presented as means (95% CI), unless otherwise noted.
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5

Statistical Analysis of Experimental Data

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Data were analyzed by one-way analysis of variance. When a statistically significant difference (P ≤ 0.05) was found, differences among groups were inspected using Duncan’s multiple range test. All statistical analyses were carried out using the SAS System for Windows release 9.3 (SAS Institute, Cary, NC).
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