The OEHHA obtained the raw data from DPR through a secure access website. More than 90 000 records were downloaded for the period of 2011-2013. To assure data consistency within each laboratory, we used SAS version 9.1 statistical software (SAS Institute Inc., Cary, North Carolina) for data cleaning (eg, reformatting the data, flagging missing information, removing duplicates, and correcting typographical errors) and processing.
Sas version 9.1 statistical software
Manufactured by SAS Institute
Sourced in United States
SAS version 9.1 is a statistical software package developed by SAS Institute. It provides tools for data management, analysis, and reporting. The software supports a wide range of statistical techniques, including regression, ANOVA, and multivariate analysis. SAS version 9.1 is designed to work with various data sources and can be used for a variety of applications, including business analytics, scientific research, and predictive modeling.
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Lab products found in correlation
4 protocols using sas version 9.1 statistical software
1
Data Cleaning and Processing Protocol
2
Immunological Response to Photodynamic Therapy
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In the tables displaying the main and clinical characteristics at baseline, continuous variables are presented as mean values and standard deviations (SDs) and were analyzed using a non-parametric Wilcoxon-Mann–Whitney test. Categorical variables are presented as numbers and percentages and were analyzed by the Fisher’s exact test. All the tests were two-sided, and a p value <0.05 was considered statistically significant.
For the immunological data, p values for the differences between the PDT and control groups at each time point (i.e., day 3, day 5 and day 21) were estimated via two-tailed Wilcoxon-Mann–Whitney tests. The p values for the differences between the PDT and control groups across time were estimated via a 2-way ANOVA on ranks. We also performed a test to evaluate the linear trend between the PDT and control groups across time using a 2-way ANOVA on ranks with a first degree polynomial contrast for time. The data were analyzed using SAS version 9.1 statistical software (SAS Institute, Cary, NC, USA).
For the immunological data, p values for the differences between the PDT and control groups at each time point (i.e., day 3, day 5 and day 21) were estimated via two-tailed Wilcoxon-Mann–Whitney tests. The p values for the differences between the PDT and control groups across time were estimated via a 2-way ANOVA on ranks. We also performed a test to evaluate the linear trend between the PDT and control groups across time using a 2-way ANOVA on ranks with a first degree polynomial contrast for time. The data were analyzed using SAS version 9.1 statistical software (SAS Institute, Cary, NC, USA).
3
Serum and Tissue Experiments in Mice
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Serum and tissue experiments were conducted three times in parallel with each mouse, and the mean values were obtained. SAS® version 9.1 statistical software (SAS Institute, Inc.) was used to analyze the data. One-way ANOVA followed by Tukey's test was used for multiple comparisons. P<0.05 (alpha level) was considered to indicate a statistically significant difference (19 (link)).
4
Personality Traits and Obesity Associations
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SAS version 9.1 statistical software (SAS Institute, Cary, NC, USA) was used for data management and statistical analysis. One-way analysis of variance (ANOVA) was used for multiple comparisons of the BMI groups, and post-hoc tests were performed using Fisher’s least significant difference (LSD) method. The variables age, age2, and education were used as covariates. Because only limited income data were available for male participants, we did not include income as a covariate. Data were expressed as adjusted means and standard errors.
Multiple linear regression analysis was used to determine the personality traits associated with BMI and waist circumference. Logistic regression analysis was used to analyze personality traits predicting underweight, overweight, and obesity. All P-values were two-tailed, and statistical significance was defined as a P-value <0.05.
Multiple linear regression analysis was used to determine the personality traits associated with BMI and waist circumference. Logistic regression analysis was used to analyze personality traits predicting underweight, overweight, and obesity. All P-values were two-tailed, and statistical significance was defined as a P-value <0.05.
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