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Sas program for windows

Manufactured by SAS Institute
Sourced in United States

SAS program for Windows is a software application that provides a comprehensive data analysis and management platform. It enables users to access, manipulate, and analyze data from a variety of sources. The core function of the software is to offer a robust and flexible environment for statistical analysis, predictive modeling, and reporting.

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Lab products found in correlation

3 protocols using sas program for windows

1

Evaluating Pork Supply-Processed Meat Linkages

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CCA was used to evaluate relationships between supply variables set of pork and production
variables set of processed meat products in Korea by PROC CANCORR procedure of SAS program for
Windows, version 9.2.
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2

Comparative Bone Health Evaluation

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For statistical analysis, the SAS program for Windows (version 9.1.3, SAS Institute, Cary, NC, USA) was used. The CAN-pro 4.0, professional edition, was applied to the nutritional analysis of the food diaries. Per protocol analysis, mainly focusing on participants who completed the pre-experiment and postexperiment, was applied to the study.
The differences in all variables (BMD, minerals and biochemical markers associated with bone metabolism, bone health knowledge, scores of health beliefs and self-efficacy, and values of nutrients) among the three groups were tested by analysis of covariance and orthogonal contrasts with a covariance of prescores. The similarity analysis of general characteristics and all variables among the three groups were performed by analysis of variance. The general characteristics of the participants and the distributive characteristics of all variables were compared and are presented with the descriptive statistics such as mean, standard deviation, frequency, and percentage.
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3

Genetic Polymorphism and Adenoma Risk

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All statistical analysis was performed using the SAS program for Windows (version 9.3; SAS Institute Inc., Cary, NC, USA). Chi-square analysis was used to determine the Hardy-Weinberg equilibrium. A t-test was used to assess the differences in mean values for each variable between categories. A logistic regression analysis was used to establish a prediction model for adenomatous polyp susceptibility with the numbers from the presence or absence of the polymorphism in cases and controls. All statistical analysis was performed using a confidence level of 95%.
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