Spss statistics software version 26
SPSS Statistics software version 26 is a data analysis and statistical software package developed by IBM. It provides a comprehensive set of tools for data management, analysis, and reporting. The software is designed to help users collect, analyze, and interpret data effectively.
Lab products found in correlation
216 protocols using spss statistics software version 26
Bone Volume Percentage Analysis
Statistical Analysis of Survival Outcomes
To analyze overall survival, Kaplan–Meier estimator was used. To adapt to the prolonged observation period, we used both Breslow and log rank test to check for statistical significance and receive a more detailed interpretation. Chi-squared tests and Fisher’s exact test were performed to compare qualitative and categorical variables, while independent samples T-test, due to the small sample size and its robustness against possibly skewed data, was used to compare continuous variables. The Kruskal–Wallis test was carried out for ordinal variables and the one-way ANOVA for continuous variables for several independent samples. Odds ratio and confidence intervall for factors, which might influence the development of CAL or therapeutical outcome, were calculated using the chi-squared test. A post hoc Bonferroni correction followed this to adjust for multiple testing. Pairwise deletion was used for missing data. Statistical significance was assumed for a p-value ≤ 0.05.
Demographic Data Impact on Outcomes
Predicting Major Complications and Outcomes in Elderly PEG Patients
Statistical analyses were performed using Student's t‐test for normally distributed continuous variables, a chi‐square test, and a Fisher's exact test for noncontinuous variables. To identify parameters influencing major complications, we examined potential factors using univariate analysis, and after determining relevant risk factors (P values <0.05), these factors were entered into a multivariate analysis using a binary logistic regression model. Odds ratios (ORs) and corresponding 95% confidence interval (CI) were generated for all variables. P values <0.05 were considered significant. To identify the parameters influencing mortality and PEG removal, Cox proportional hazard models were used for multivariate analysis using significant variables. A hazard ratio (HR) and 95% (CI) were determined. Kaplan–Meier curves were drawn and compared using the log‐rank test and log‐rank (mantel‐cox) test. Data were analyzed with IBM SPSS Statistics software, version 26.
Fear Conditioning and Extinction Biomarker Analysis
Statistical Analysis of Results
Gastroschisis Feeding Protocol Outcomes
Statistical Analysis of FET Outcomes
We analyzed the FET outcome data using R statistical software (R version 3.1.1 (2014-07-10), R Foundation for Statistical Computing, Vienna, Austria,
Placental Perfusion Analysis in FGR
Data Analysis Methodology for Comparative Study
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