All statistical analysis was performed using IBM SPSS statistical analysis package (IBM, Armonk, NY). A two-way ANOVA was used to assess the significance of predictor variables gel stiffness and age of cardiac extracellular matrix for dependent variables of either media nitrite concentration or urea concentration in Figures 3 , 4 , respectively. Tukey’s post-hoc testing was performed to assess the significance of inter-group differences. In the any case where a significant interaction term was identified from two-way ANOVA results, comparisons of interest were defined a priori and independent samples t-tests were leveraged to assess significance of variable interactions defined a priori against p-value adjusted with Bonferroni correction to account for multiple comparisons. For qRT-PCR experimental data which exhibited non-normal distributions, a Kruskal-Wallis one-way ANOVA was used to assess the significance of the predictor variable age of cardiac extracellular matrix coating. Post hoc comparisons were then performed pairwise to further elucidate significant difference between groups.
Spss statistical analysis package
Manufactured by IBM
Sourced in United States
SPSS is a statistical analysis software package developed by IBM. It is designed to perform a wide range of statistical analyses, including descriptive statistics, regression, and multivariate analysis. SPSS provides a user-friendly interface and a comprehensive set of tools for data management, analysis, and reporting.
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Lab products found in correlation
17 protocols using spss statistical analysis package
1
Cardiac ECM Biocompatibility Evaluation
2
Age-related Changes in Bone Toughness
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Bootstrapping was employed in all statistical analyses using SPSS statistical analysis package (IBM Corp., Armonk, NY, USA). Thus, requirements of conventional statistical analyses (eg, normality and equal variance) were no longer necessary. One‐factor ANOVA analyses were performed to determine the effect of age on the tissue‐level toughness of bone, the amount of GAGs, and the bound water content. Then, multiple comparisons (Fisher's PLSD) were implemented to determine the statistical differences between the age groups. Moreover, Pearson correlation analyses were performed to determine the correlation among the tissue‐level toughness, GAGs, and bound water in bone matrix, whereas the partial correlation analyses were conducted between the tissue‐level toughness of bone and GAGs or bound water when bound water or GAGs were used as covariate in the analysis, respectively. The statistical significance was considered only when p < 0.05.
3
Erythrocyte Recovery Optimization
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All data were analysed using the SPSS statistical analysis package (IBM Corporation, Armonk, USA). Baseline characteristics were described as counts and percentages (dichotomous variables), medians and interquartile range (continuous variables, non-normal distribution), or means and standard deviations (continuous variables, normal distribution). Red blood cell mass lost was calculated by the patients' hematocrit  volume of blood loss, and the red blood cell mass recovered was calculated by the hematocrit of the erythrocyte concentrate  volume of erythrocyte concentrate. The percentage of recovered red blood cell mass was calculated by red blood cell mass recovered/red blood cell mass lostÂ100. Differences in patient laboratory results and the results of the erythrocyte concentrate were tested using a Student's t-test to evaluate the concentration effect of the cell saver. Using a Pearson correlation, the relation between the volume of processed fluids and the percentage of recovered erythrocytes was determined. Furthermore, the correlation between the preoperative hematocrit and hematocrit of the erythrocyte concentrate was determined.
4
Statistical Analysis of Research Findings
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SPSS statistical analysis package (version 21.0 for Windows) was used to analyze the significance of the results. An independent t-test was used to compare two means, and ANOVA was used to compare more than two means. A p-value less than 0.05 was considered statistical significance.
5
Comparative Statistical Analysis of Patients
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The SPSS statistical analysis package (version 18) was used for all calculations. Patient and control groups were compared in relation to continuous variables with an independent t-test for independent variables and Chi-square test for categorical variables. Furthermore, one-way ANOVA and Pearson's correlation test were used. For all these comparisons, level of significance was considered to be less than 5%.
6
Comparison of Fibrosis Scoring Systems
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Categorical and continuous variables were compared between patients with and without significant fibrosis using Chi-squared and Student’s t-test or the Wilcoxon rank-sum test (according to the distribution of the data), respectively. Most of the numerical values did not follow a normal distribution and were expressed as medians and interquartile ranges. The diagnostic performance of each scoring system was then evaluated using receiver operating characteristic curves, and comparisons between the correlated AUROCs were performed using DeLong’s test[17 (link)]. The sensitivities (Sens) and specificities (Spec) of each scoring system were analyzed using the given low and high cutoffs for predicting F2, as reported previously-i.e., 0.88 and 1.77 for the FIB-8 score, 0.81 and 1.81 for the FIB-4 score, and -2.45 and 0.03 for the NFS, respectively[11 ,18 (link)]. All statistical analyses were performed using the SPSS statistical analysis package (version 18.0.0; SPSS Inc., Chicago, Illinois, United States), Stata (version 15; StataCorp), and R program version 4.1.1. A P value < 0.05 was considered statistically significant.
7
Statistical Analysis of Variable Associations
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The SPSS statistical analysis package (version 21.0 for Windows) was used to analyze the significance of the results. Chi-square tests of independence were performed to assess possible associations between the variables. The significance level was set at p <0.05. If the chi-square test of independence was statistically significant, then post-hoc comparison tests were conducted to determine the variable that produced the statistically most significant difference. The obtained standard residual (std.res) from post-hoc analysis was considered significant when std.res >1.96, >2.58 and >3.33 were at 0.05, 0.01 and 0.001 level of significance.
8
Comparative Analysis of Treatment Efficacy
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The experimental results are presented as means and standard deviations. Multiple comparisons were made using the least significant difference (LSD) test at p = 0.05 level. The data were statistically analyzed using analysis of variance (ANOVA) through the SPSS statistical analysis package (Version 13.0) at p = 0.05 level of significance.
9
Intensive Tobacco Cessation Intervention
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Data analysis will be undertaken by the investigators using the SPSS statistical analysis package.
Analysis of primary outcome of 7-day point prevalence tobacco abstinence at 6 months will be based on an intention-to-treat approach. It shall be done by estimating the mean difference in percentage of quit rates between the intensive and brief intervention groups at 6 months.
Regression analysis and /or ANOVA will be used for primary and secondary outcomes. For not normally distributed data, robust standard errors, truncation, or transformation will be used. For missing data, multiple imputations will be used. The net changes in primary and secondary outcome measures will be considered by exploratory analysis. Marginal means and treatment effect with its associated 95% CI and probability values will be presented and reported. The conventional significance level of 0.05 will be used in all analyses to reject the null hypothesis of no difference between two groups.
Analysis of primary outcome of 7-day point prevalence tobacco abstinence at 6 months will be based on an intention-to-treat approach. It shall be done by estimating the mean difference in percentage of quit rates between the intensive and brief intervention groups at 6 months.
Regression analysis and /or ANOVA will be used for primary and secondary outcomes. For not normally distributed data, robust standard errors, truncation, or transformation will be used. For missing data, multiple imputations will be used. The net changes in primary and secondary outcome measures will be considered by exploratory analysis. Marginal means and treatment effect with its associated 95% CI and probability values will be presented and reported. The conventional significance level of 0.05 will be used in all analyses to reject the null hypothesis of no difference between two groups.
10
Evaluating Microsatellite Instability in Cancers
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Confidence intervals were calculated at the 95% and 99% levels. Fisher’s exact test and Chi Square were used for statistical analyses with the SPSS statistical analysis package. Additionally, an inter-rater reliability test using Cohen’s kappa coefficient was used to measure correlation between the MSI and LOH results and low MMR protein expression [62 ].
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