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Spss statistical analysis software version 20

Manufactured by IBM
Sourced in United States

SPSS statistical analysis software, version 20.0, is a comprehensive data analysis tool designed to help users analyze and interpret data. The software provides a wide range of statistical techniques and algorithms to support decision-making processes and research projects. SPSS version 20.0 offers a user-friendly interface and is compatible with various data formats, making it accessible to users with diverse analytical needs.

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Lab products found in correlation

7 protocols using spss statistical analysis software version 20

1

Feature Selection for Categorical Data

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The feature selection was performed by regression for categorical data method with beta coefficient >0.00 and p-value<0.05 for selection of best features using SPSS statistical analysis software, version 20 (SPSS, Chicago, IL, USA). The beta coefficient value is a measure of how strongly each “predictor variable” influences the “criterion variable”. The higher beta coefficient value implies greater impact of the “predictor variable” on the “criterion variable”.
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2

Comparing Marine and Freshwater Fish Resistance

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Data analysis was performed with SPSS statistical analysis software version 20. Statistical analysis was performed in order to determine whether there was any significant difference in between two types of fish (marine and freshwater fish) and the MAR index of resistant isolates using the independent t-test. The significance level was set at p ≤ 0.05. One-way analysis of variance (ANOVA) followed by appropriate post hoc text (Tukey) was performed to determine the significant differences between the type of fishes and MAR index of resistant isolates. A difference was considered statistically significant when p ≤ 0.05.
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3

Comparative Statistical Analysis of Experimental Data

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Data are presented as mean ± standard deviation (SD) (SPSS Statistical analysis software version 20.0). All variables indicated approximately normal distribution by Kolmogorov-Smirnov test and homogeneity by Levene's test, simultaneously one-way analysis of variance (ANOVA), followed by post hoc analysis using the Student-Newman-Keuls- (SNK-) q test. Differences were considered to be statistically significant at a P value < 0.05 (P < 0.05).
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4

Tacrolimus Pharmacokinetics and Efficacy

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The characteristics of the study group were expressed as median and interquartile range or number and frequency (%). Mann–Whitney U test (not normally distributed data) was employed for the comparison of TDD, C0, and C0/D with respect to the CYP3A5 genotype. In addition, Mann–Whitney U test was used to compare eGFR, Tac IPV, and C0/D between Tac formulation, whereas Chi square test was performed to compare the frequency of AR and presence of CYP3A5*1/*3 genotype. Linear regression analysis was performed to evaluate the potential influence of independent predictors on eGFR. All analyses were performed with SPSS statistical analysis software, version 20.0 (SPSS, Chicago, IL, United States) at the significance level set at p < 0.05. MATLAB R2017b (MathWorks) software was used to perform the MC simulation.
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5

Statistical Analysis of Clinical Data

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Descriptive statistics were used to summarize clinical characteristics of the study group. The Shapiro–Wilk test (p > 0.05, normal distribution of data assumed) and Q-Q plots were used to confirm the assumption of normal distribution. Numerical variables were expressed as the mean ± SD in case of normal distribution, median (interquartile range) if a variable did not follow normal distribution, and as a percentage for categorical data. If normal distribution was met, the one-way ANOVA with the Tukey correction was used for group comparisons; otherwise, the non-parametric Kruskal–Wallis test was applied. The Chi-squared test was used to compare categorical variables. The correlation between two quantitative variables was determined by Spearman’s correlation test. ROC analysis was used to determine AUC parameters and cut off values. A p-value below 0.05 was considered significant. All analyses were performed by means of the SPSS statistical analysis software, Version 20.0 (SPSS, Chicago, IL, USA).
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6

Comparative Analysis of CSAL Scores

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Besides system modeling, statistical analysis included descriptive statistics, presented as frequency (%) and absolute number, but as well mean and standard deviation. In addition, in order to compare CSAL between adverse effect score groups, Mann–Whitney U test (not‐normally distributed data) was performed. All analyses were performed with SPSS statistical analysis software, version 20.0 (SPSS) at the significance level set at p < .05.
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7

Statistical Analysis of Clinical Data

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Various methods of descriptive and analytical statistics were applied. Thus, descriptive statistics was used to summarize clinical characteristics of the study group. An assumption of normal distribution was tested using Shapiro ilk tests (p > 0.05 normally distributed data assumed) and Q-Plots. Numerical variables were expressed as a mean ± SD in case of normal distribution or median (interquartile range) if variable did not follow normal distribution, and as percentages for categorical data. If normal distribution was met, the one-way ANOVA with Tukey correction was used for group comparisons, otherwise the non-parametric Kruskal-Wallis test was applied. Chi-square test was used to compare categorical variables. P-value below 0.05 was considered significant. All analysis was performed with the SPSS statistical analysis software, Version 20.0 (SPSS, Chicago, Illinois, USA).
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