Data were presented as mean ± standard deviation (n = 3). Measured levels of phytochemical content and antioxidant capacity were analyzed by one‐way analysis of variance (ANOVA) and Ducan's multiple comparison post‐test in SPSS statistical software 21.0 (SPSS Inc., Chicago, IL, USA) at a significant level with p‐value < 0.05.
Spss 21 statistical software
Manufactured by IBM
Sourced in United States
SPSS 21 is a statistical software package developed by IBM. It provides advanced analytics capabilities for data management, analysis, and reporting. SPSS 21 offers a range of statistical techniques, including regression analysis, hypothesis testing, and data mining, to help users gain insights from their data.
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Lab products found in correlation
54 protocols using spss 21 statistical software
1
Phytochemicals and Antioxidant Evaluation
2
Lifespan Analysis of C. elegans
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All data were reported as mean ± standard deviation (SD) from at least three replicates. The graphs were depicted using the GraphPad INSTAT software (GraphPad software, San Diego, CA, USA). The statistical significance of the observed differences was evaluated using the IBM SPSS statistical software 21.0 (SPSS Inc., Chicago, IL, USA) with a p-value < 0.05. A Kaplan-Meier analysis and log-rank test were carried out to analyze the lifespan of C. elegans.
3
Diabetes and Endothelial Function Evaluation
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All values were expressed as mean ± SD. The Kolmogorov-Smirnov normality test demonstrated that natural logarithmic-scaled RHI (L_RHI), 1,5-AG, HbA1c, FPG, LDL-C, TG, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were normally distributed, whereas IRI and HOMA-IR showed skewed distribution. For intergroup comparisons, the unpaired t-test was used for normally distributed data, the Mann–Whitney U test for data with skewed distributions. To assess potential correlations with L_RHI, the Pearson correlation coefficient was used for data with normal distribution pattern, whereas the Spearman rank-correlation coefficient was used for data with a non-normal distribution. Multivariate analysis was carried out employing the step-up procedure, using L_RHI as the dependent variable, and age, sex, body mass index (BMI), disease duration, use of presence/absence of treatment with α-glucosidase inhibitor treatment or insulin treatment, use of antihypertensive drugs, use of antihyperlipidemic drugs, history of cardiovascular disease (CVD), LDL-C, HDL-C, TG, SBP, DBP, HbA1c, 1,5-AG, and FPG as independent variables. The level of significance was set at p < 0.05. SPSS Statistical Software 21.0 (SPSS Inc., Chicago, IL) was used for all statistical analyses.
4
Gastric Cancer Survival Predictors
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Statistical analysis was performed by using the SPSS statistical software (21.0). Numerical variables were expressed as the mean and standard deviation (SD) when normally distributed and as the median and interquartile range (IQR) if not normally distributed. Statistical associations between clinicopathological characteristics and EMT status were assessed with a χ2 test for categorical variables and with a t-test or non-parametric tests (Mann–Whitney) for continuous variables. The linear regression (R2) was used to explore the correlations between two linear variables. Survival curves were estimated by using the Kaplan–Meier method. Death due to cancer (cancer-specific survival) was considered as the endpoint; as such, deaths due to causes other than gastric cancer were considered censored cases at the time of death. Survival curves were compared by using the log-rank test. A p-value < 0.05 was considered statistically significant.
5
Radiographic Evaluation of Surgical Outcomes
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Statistical analyses were performed utilizing SPSS statistical software 21.0 (Chicago, IL, USA). The preoperative and postoperative radiographic parameters were compared using paired t tests and Mann–Whitney U tests when appropriate. Comparisons between the two groups were performed using a single factor ANOVA test for continuous variables. P < 0.05 was considered statistically significant.
6
Statistical Analysis of Syndrome Elements
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SPSS statistical software 21.0 was used for statistical analysis. The normally distributed measurement data were all expressed as the mean ± standard deviation, while the nonnormally distributed data were expressed as the median (interquartile range) and the counting data were expressed as the frequency. For the measurement data among groups, the syndrome element scores and differences (d) were compared before and after treatment. Those with normal distribution and variance homogeneity underwent grouped t-test; otherwise, the Mann–Whitney U test was conducted. For the intragroup measurement data, the syndrome element scores were compared before and after treatment. Those with a normal distribution underwent the paired t-test; otherwise, the Mann–Whitney U test was used. The counting data were compared by χ2 test, and P < 0.05 indicated that the difference was statistically significant.
7
Comparative Statistical Analysis Protocol
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Statistical analysis was performed by SPSS statistical software 21.0 (SPSS Inc., Chicago, IL, USA). Independent samples t-test, chi-square, and Pearson’s and partial correlation were used for statistical analysis. Descriptive statistics were stated as mean ± standard deviation (SD). A value of p < 0.05 has been accepted as significant. According to the formula proposed by Hulley et al.,11 we calculated sample size as 60.09 subjects including both study and control groups by taking α (two-tailed) = 0.050, β = 0.80, q1 = 0.45 (proportion of subjects that are in Group 1), effect size = 0.580, and SD of the outcome in the population = 2.01.
8
Prognostic Significance of MSI2 Expression
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SPSS statistical software 21.0 was used to perform all of the statistical analyses. Spearman rank correlation analysis was performed to investigate the correlation of MSI2 expression to clinic-pathological variables. We decided to use non-parametric tests because of the non-normal distribution of the variable checked by means of the Shapiro–Wilk normality test. For such reason, the Mann–Whitney test was applied to explore the difference in expression between groups. Univariate survival analysis was performed with the Kaplan–Meier method to estimate both overall survival rates and disease-free survival, while comparing results between groups with the log-Rank test. A multivariate Cox Proportional Hazard Model was built in order to assess the prognostic significance of MSI2 expression after adjusting for covariates, including the clinic-pathological variables: age, grading, staging (8th AJCC edition), and gender, as covariates.
9
Statistical Analysis of Normally Distributed Data
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Normally distributed variables were expressed as mean ± SD and compared using unpaired t tests between two groups. Correlation coefficients were analyzed using Pearson's correlation. Differences were considered significant when p < 0.05. All statistical analyses were performed with SPSS statistical software 21.0 (SPSS, Inc., the US).
10
Analyzing Exercise Intensity Measures
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All the data were processed by SPSS statistical software 21.0 (SPSS Inc., Chicago, IL, United States ). The data were shown as mean ± SD, and the significance was set at p < 0.05. The Shapiro-Wilk test was used to test the normality of the data. If the data were normally distributed, one-way repeated measures analysis of variance was used to compare the differences between different drills, the LSD method was used for post-test, and the Pearson’s correlation coefficient was used for correlation analysis. . If not, the Friedman test was used for between-group difference analysis, the Wilcoxon signed rank test was used for post hoc analysis, and the Spearman’s correlation coefficient was used for correlation analysis.The agreement among TLHRV, TRIMP, and RPE was examined by the Bland-Altman test and intraclass correlation coefficient. Since the three measurements have different units, the percentage conversion method defined by Saboul et al. was used to convert the values measured for each indicator into a percentage; for example, TRIMP1V1 = 100×[TRIMP1V1/(TRIMP1V1+TRIMP2V2+TRIMP3V3+TRIMP4V4+TRIMP5V5)] (Saboul et al., 2016 (link)).
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