This study used the SAS 9.4 statistical software for Windows (SAS Institute, Cary, NC, USA) provided by the Academia Sinica Branch of the Data Welfare Center of the Ministry of Health and Welfare for the analysis. The descriptive statistics were expressed in the form of percentages, averages, and standard deviations, and the chi-square test was used to compare the differences among the three groups (children, adults, and elderly). Differences in the cause of injury and the proportion of women who suffered from sexual assault among different age groups were determined. In addition, logistic regression was used to analyze the risk of sexual assault for women in different age groups or with various occupations (including dependent occupations). According to the central limit theorem, (a) if the sample data are approximately normal, then the sampling distribution will also be normal; (b) in large samples (>30 or 40), the sampling distribution tends to be normal regardless of the shape of the data; (c) the means of the random samples from any distribution will themselves have a normal distribution [7 (link)]. A p value < 0.05 was considered to be statistically significant.
Sas 9.4 statistical software for windows
Manufactured by SAS Institute
Sourced in United States
SAS 9.4 is a comprehensive statistical software package for Windows. It provides a range of analytical and data management capabilities, including data manipulation, statistical modeling, and reporting tools. The software is designed to help users analyze, manage, and visualize data effectively.
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Lab products found in correlation
2 protocols using sas 9.4 statistical software for windows
1
Sexual Assault Risk Analysis by Age and Occupation
2
Occupational Factors and Depression
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A χ2 test was conducted to identify the prevalence of likely depression according to general and occupational characteristics of the participants. The distribution of the quality of direct supervisors' behavior according to depressive mood was described. The odds ratio (OR) of likely depression according to the quality of direct supervisors' behavior were assessed by multiple logistic regression analysis adjusting for control variables. In Model 1, general characteristics including sex, age group, education level, and health condition were adjusted. Model 2 was further adjusted by adding occupational characteristics including working condition satisfaction, employment status, night working status, long working days, shift working status, income level, job stress, colleague support, ERI, mental burden of the job, job autonomy, and occupational groups to Model 1. Additionally, we stratified the participants by the level of satisfaction with working conditions for analysis. All analyses were performed using SAS 9.4 statistical software for Windows (SAS Inc., Cary, NC, USA).
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