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Statistical package for the social sciences spss for windows version 23

Manufactured by IBM
Sourced in United States

SPSS for Windows, version 23.0 is a software package used for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and presentation. The software is designed to handle a wide range of data types and offers a user-friendly interface for conducting various statistical procedures.

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16 protocols using statistical package for the social sciences spss for windows version 23

1

Evaluating Student Satisfaction in Virtual Learning

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Results from the five-point Likert scale questions in both the student- and teacher-targeted questionnaires were presented in diverging stacked bar charts. Awarded marks and satisfaction grouped by program and program year were presented using box plot. Satisfaction was also demonstrated and compared on a five-point scoring system with 1 being the lowest part (very poor) and 5 the highest part (excellent) of the scale. Correlation analysis between students’ satisfaction and variables of Likert scale was done using nonparametric Spearman’s rank coefficient. Unpaired two-tailed unequal variance t-test was used to determine significant difference between marks awarded during virtual learning (2020) and traditional face-to-face teaching (2019). Data analysis was done using Tableau v3 and Statistical Package for the Social Sciences (SPSS) for windows, version 23.0 (SPSS Inc., Chicago, IL, USA).
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2

Statistical Analysis of Clinical Trial Data

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Categorical variables were expressed as percentages while continuous variables were expressed as mean ± standard deviation (SD) or median with range depending on the normality of distribution of the variables. Kaplan-Meier methodology was used to determine the mPFS and mOS. Differences between categorical variables were tested using Chi-Squared test or Fisher Exact test. For continuous variables, the differences were compared using independent t-test or Mann-Whitney U test. Multivariate analysis was performed using logistic regression. A p-value of < 0.05 was considered statistically significant. Statistical analyses were performed by using the software package, Statistical Package for the Social Sciences (SPSS for Windows version 23.0, SPSS Inc., Chicago, IL, USA).
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3

Clinical Phenotype Comparison Analysis

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Categorical variables are presented as percentages. The difference between clinical phenotypes was compared using the chi-squared test, with post-hoc analysis taking adjusted standardized residual of > 2 as significant. Continuous variables are presented as the mean ± standard deviation (SD), or median with inter-quantile range. Differences between clinical phenotypes were compared using one-way ANOVA test or Kruskall-Wallis H test. The post-hoc analysis was performed using Tukey test or Dunn’s procedure with a Bonferroni adjustment, respectively. The significant p-value in this study was < 0.05. Statistical analyses were performed using the software package, Statistical Package for the Social Sciences (SPSS for Windows version 23.0, SPSS Inc., Chicago, IL, USA).
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4

Statistical Analysis of Experimental Data

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Data were subjected to one-way ANOVA and presented as mean ± SD. Significant differences between means at p < 0.05 were obtained using the Tukey’s HSD-multiple range post-hoc test. Statistical analyses were done using IBM Statistical Package for the Social Sciences (SPSS) for Windows, version 23.0 (IBM Corp., Armonk, NY, United States).
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5

Statistical Analysis of Experimental Data

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Data were subjected to one-way analysis of variance (ANOVA) and presented as mean ± SD. A statistically significant difference between means were obtained at p < 0.05 using Tukey’s HSD multiple range post hoc test. IBM Statistical Package for the Social Sciences (SPSS) for Windows, version 23.0 (IBM Corp., Armonk, NY, USA) was used to carryout statistical analyses.
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6

Metabolomic Analysis of ANOVA Data

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One-way analysis of variance (ANOVA) was used in analyzing the data and presented as mean ± SD. Significant differences between means at p < 0.05 were obtained with the Tukey’s HSD-multiple range post-hoc test. Statistical analyses were done using IBM Statistical Package for the Social Sciences (SPSS) for Windows, version 23.0 (IBM Corp., Armonk, NY, United States). The GC-MS identified metabolites were subjected to statistical analysis (distinct changes and distribution) using the MetaboAnalyst 4.0 online server (Chong et al., 2018 (link)). Graphs were prepared using Microsoft Excel Spreadsheet and MetaboAnalyst 4.0 online server.
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7

Statistical Analysis of Sleep Study Data

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The study participants’ data was summarized using descriptive statistics. Baseline characteristics, PSG statistics, adherence, tolerance and pulmonary function data were reported as mean (standard deviation; SD) for normally distributed continuous variables and as median (interquartile range; IQR) and frequency (percent) for skewed continuous variables and categorical variables, respectively. Normally distributed, continuous variables were compared using Student’s t tests and skewed continuous variables were compared using Wilcoxon pair signed-rank test. A p value less than 0.05 indicated statistical significance. Data analysis was carried out using IBM Statistical Package for the Social Sciences (SPSS) for Windows, Version 23.0 (IBM Corp, Armonk, NY).
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8

One-Way ANOVA with Tukey's Post Hoc

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Results are presented as mean ± SD and were analyzed using one‐way analysis of variance (ANOVA). The Tukey's HSD‐multiple range post hoc test was used in obtaining significant differences between means at p < 0.05. The statistical analysis was carried out using the IBM Statistical Package for the Social Sciences (SPSS) for Windows, version 23.0 (IBM Corp., Armonk, NY, USA).
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9

Lipidomic Analysis of Metabolic Profiles

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Results are presented as mean ± SD and were analyzed using one-way analysis of variance (ANOVA). The Tukey’s HSD-multiple range post-hoc test was used to derive significant differences between means at p < 0.05. The statistical analysis was performed with the IBM Statistical Package for the Social Sciences (SPSS) for Windows, version 23.0 (IBM Corp., Armonk, NY, USA). The GC–MS identified lipid metabolites were subjected to clustering analysis which covers for heat maps and principal component analysis (PCA) using the MetaboAnalyst 5.0 online server (https://www.metaboanalyst.ca/).
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10

Metabolic Profiling of Cell Samples

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Data were presented as mean ± SD, following analysis with one-way analysis of variance (ANOVA). Significant differences between means were determined at p < 0.05 using the Tukey’s HSD-multiple range post-hoc test. The statistical analysis was carried out using the IBM Statistical Package for the Social Sciences (SPSS) for Windows, version 23.0 (IBM Corp., Armonk, NY, USA). The normalized data of the LC-MS identified cell metabolites were subjected to clustering analysis which covers for heat maps and principal component analysis (PCA) using the MetaboAnalyst 5.0 online server (https://www.metaboanalyst.ca/). Pathway enrichment values for identified pathways were presented as raw p.
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