All continuous variables are displayed as means (standard deviation, SD) for normally distributed variables, or median (interquartile range, IQR) for variables with abnormal distribution, and were compared by a Student’s t-test and by Mann–Whitney U-test, respectively. Categorical variables are displayed as numbers (%) of patients within each group, and they were compared by a chi-squared test. The cohort was divided into three groups according to low, moderate, and high bilirubin and pollutants levels. We used one-way ANOVA and the Tukey post-hoc analysis to evaluate the differences within these groups. The contribution of bilirubin levels to changes in FEV1 values under different levels of NOx and CO was evaluated using a logistic regression model. FEV1 was defined as the dependent variable, and the model was adjusted for bilirubin levels, age, body mass index (BMI), sex, smoking status (current/past/never), and diabetes diagnosis (yes/no). All analyses were conducted using the SPSS Statistics 22.0 statistical package (IBM Corporation, Armonk, NY, USA).
Spss statistics 22.0 statistical package
Manufactured by IBM
Sourced in United States
SPSS Statistics 22.0 is a statistical analysis software package developed by IBM. It provides a comprehensive set of tools for data manipulation, analysis, and visualization. The software is designed to handle a wide range of data types and offers a variety of statistical techniques, including descriptive statistics, regression analysis, and hypothesis testing. SPSS Statistics 22.0 is widely used in various fields, including social sciences, business, and academic research.
Automatically generated - may contain errors
Lab products found in correlation
2 protocols using spss statistics 22.0 statistical package
1
Bilirubin Levels and Air Pollutants Impact Lung Function
2
Eosinophil Levels and Lung Function
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All continuous variables are displayed as means (SD) for normally distributed variables or median (IQR) for variables with abnormal distribution, while categorical variables are displayed as numbers (%) of patients within each group.
The levels of eosinophil blood count in patients with normal lung function (FEV1 >80% and FVC >80% and FEV1/FVC >0.7) and patients with abnormal lung function (FEV1 <80% or FVC <80% or FEV1/FVC <0.7) were compared by a Student’s t-test for normally distributed variables and by Mann–Whitney U-test for nonnormally distributed variables. To assess associations among categorical variables, we used a chi-squared test.
To quantify the contribution of eosinophil levels to the change of the FEV1 test, we used logistic regression models (Enter method), where pulmonary function tests (PFTs) were regarded as the dependent variables, and potential confounding parameters together with eosinophil levels were regarded as the independent variables. These confounders included parameters with known or suspected influences on PFTs. Continuous variables included age and first FEV1, while dichotomized variables were gender, smoking, and diabetes.
The IBM SPSS Statistics 22.0 statistical package was used to perform all statistical analyses (IBM Corporation, Armonk, NY, USA).
The levels of eosinophil blood count in patients with normal lung function (FEV1 >80% and FVC >80% and FEV1/FVC >0.7) and patients with abnormal lung function (FEV1 <80% or FVC <80% or FEV1/FVC <0.7) were compared by a Student’s t-test for normally distributed variables and by Mann–Whitney U-test for nonnormally distributed variables. To assess associations among categorical variables, we used a chi-squared test.
To quantify the contribution of eosinophil levels to the change of the FEV1 test, we used logistic regression models (Enter method), where pulmonary function tests (PFTs) were regarded as the dependent variables, and potential confounding parameters together with eosinophil levels were regarded as the independent variables. These confounders included parameters with known or suspected influences on PFTs. Continuous variables included age and first FEV1, while dichotomized variables were gender, smoking, and diabetes.
The IBM SPSS Statistics 22.0 statistical package was used to perform all statistical analyses (IBM Corporation, Armonk, NY, USA).
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!