Differences between continuous variables were assessed using the Mann–Whitney test due to the small sample size and non-normal distribution. To compare categorical variables, the χ2 test was used. Correlation analysis was performed using Spearman's correlation test. Statistical significance was set at p<0.05. Statistical analysis was performed using IBM SPSS Statistics for Window, Version 24.0 (IBM Co., Armonk, NY, USA) statistical software.
Spss statistics for window version 24
Manufactured by IBM
Sourced in United States
SPSS Statistics for Windows, version 24.0 is a statistical software package developed by IBM. It provides tools for data management, analysis, and presentation. The software is designed to handle a wide range of data types and can perform a variety of statistical procedures, including descriptive statistics, regression analysis, and hypothesis testing.
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Lab products found in correlation
5 protocols using spss statistics for window version 24
1
Statistical Analysis of Continuous and Categorical Variables
2
Survival Analysis of Fistula Development
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Overall survival (OS) was defined as the time from the start date of the initial treatment until the date of death from any cause or until the latest documented follow-up. The survival rates were estimated using the Kaplan–Meier method and were compared using log-rank tests for categorical variables. To compare the incidence of fistula development according to the bronchoscopic findings, chi-squared or Fisher’s exact tests were used. P < 0.05 was considered statistically significant in two-tailed tests. Statistical analysis was performed using IBM SPSS Statistics for Window, version 24.0 (IBM Corp., Armonk, NY, USA).
3
Statistical Analysis of Clinical Data
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For statistical analysis, IBM SPSS Statistics for Window version 24.0 (IBM Co., Armonk, NY, USA) was used. Continuous variables are expressed as median with interquartile range (IQR). Categorical variables are presented as number and percentage. The Mann-Whitney U test, Kruskal-Wallis test, Pearson correlation analysis, Fisher’s exact test, and linear by linear association were used. A P<0.05 was considered statistically significant. All P values were 2-sided.
4
Bacterial Growth-Promoting Effects on Plants
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All data were subjected to Shapiro-Wilk test in order to check the normal distribution. Data that were not normally distributed were log-transformed (untransformed data are reported here). The one-way analysis of variance (ANOVA) with the Duncan test for multiple comparisons of the means was conducted (p = 0.05). In addition, Pearson's correlation analysis was performed to define possible relationships among variables. All data were analyzed using SPSS Statistics for Window, version 24.0 (IBM Corp., Armonk, NY, USA) and graphs were made using Microsoft Excel 2019. Moreover, in order to find out the possible contribution of the bacterial plant growth-promoting traits on plant nitrogen uptake, root, shoot and total dry weights, a regression analysis between these parameters was conducted using the data analysis tools available on Excel 2019 at 95% confidence interval.
5
Surgical Outcomes and Survival Analysis
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Characteristics of the patient (sex, age, date of diagnosis, stage, location, time between diagnosis and surgery) were analyzed for differences between the local excision group and the extensive resection group. For the clinical node negative (cN0) and clinical node positive (cN+) groups differences in date of diagnosis, sex, age and location were analyzed. The Independent T-Test was used to analyze parametric continuous data, Fischer's exact test or chi-square test was used to analyze categorical variables. Kaplan-Meier survival tables and survival curves were plotted to display survival rates over time. Survival was compared using log-rank statistics.
Univariable and multivariable Cox regression analysis was used to assess prognostic factors of survival for the different surgical techniques.
Survival was defined as the number of months between the date of diagnosis and the date of death. Factors significant at a 10% significance level in univariable analysis were included in the multivariable analysis. Enter method was used to identify independent predictors.
All the statistical analyses were conducted using SPSS software (IBM SPSS Statistics for Window version 24.0). All tests were 2-sided, confidence intervals were set at 95%, and a p-value of 0.05 or less was considered to be statistically significant.
Univariable and multivariable Cox regression analysis was used to assess prognostic factors of survival for the different surgical techniques.
Survival was defined as the number of months between the date of diagnosis and the date of death. Factors significant at a 10% significance level in univariable analysis were included in the multivariable analysis. Enter method was used to identify independent predictors.
All the statistical analyses were conducted using SPSS software (IBM SPSS Statistics for Window version 24.0). All tests were 2-sided, confidence intervals were set at 95%, and a p-value of 0.05 or less was considered to be statistically significant.
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