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Spss statistical package 19.0 for windows

Manufactured by IBM
Sourced in United States

SPSS statistical package 19.0 for Windows is a software application designed for statistical data analysis. It provides a comprehensive set of tools for data management, analysis, and visualization. The core function of SPSS is to enable users to perform a wide range of statistical procedures, including regression analysis, hypothesis testing, and multivariate techniques.

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Lab products found in correlation

2 protocols using spss statistical package 19.0 for windows

1

Exploring Bone Mineral Density in Depression

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Statistical analyses were performed using the SPSS statistical package 19.0 for Windows (SPSS Inc., Chicago, IL, USA). Differences between MDD status groups (no MDD history / single episode / recurrent episodes) were examined using ANOVA for continuous variables and Chi-squared tests (Fisher’s Exact Test) for categorical variables.
Univariate and multiple linear regression techniques were used to determine the association between exposure (i.e. history of MDD episodes or use of antidepressants) and outcome (i.e. BMD at the forearm, lumbar spine, total hip or total body). Age, weight, height, smoking, activity level, calcium intake, alcohol intake, socio-economic status, current use of bisphosphonates, corticosteroids, gonadal hormones, calcium or vitamin D supplements were each explored as effect modifiers with MDD episodes and antidepressant use regressed on BMD at each site and included in the final model if significant (p<0.05).
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2

Factors Predicting Osteoporosis Prevalence

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Statistical analyses were performed using the SPSS statistical package 19.0 for Windows (SPSS Inc., Chicago, IL). Mean ± standard deviations were calculated for continuous variables, whereas proportions were calculated for categorical variables. Demographic characteristics, clinical characteristics, and the level of blood samples were compared between the osteoporosis group and the nonosteoporosis group using the Student t test for normally distributed continuous variables, and the Chi-square test was used for normally distributed categorical variables. According to epidemiological surveys of osteoporosis or other similar literatures,[15 (link),16 (link)] we divided the participants into 10-year-old group, and the prevalence of osteoporosis was presented as percentage (%). To find the most important factors predicting the outcome of osteoporosis, multiple logistic regression analysis was performed. The results from the multiple logistic regression were presented as odds ratios (ORs) and 95% confidence intervals (CIs). Multiple linear regression analysis was performed to determine the correlations between the forearm BMD and bone turnover makers, controlling age, height, body weight, and duration of menopause. A probability value of < 0.05 was accepted as the level of statistical significance.
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