Statistical computations for biochemical analyses were performed using SPSS software package version 15.0 (SPSS). Data normality was assessed using the Kolmogorov–Smirnov test. Results are presented as mean ± standard deviation as appropriate. The Mann–Whitney U-test was applied to assess the differences in biomarker concentration between the 2 groups.
Spss software package version 15
Manufactured by IBM
Sourced in United States
SPSS software package version 15.0 is a comprehensive data analysis tool. It provides a wide range of statistical and analytical capabilities to help users extract meaningful insights from data. The software is designed to handle various data types and formats, enabling users to perform advanced statistical analyses, predictive modeling, and data visualization.
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Lab products found in correlation
12 protocols using spss software package version 15
1
Comparative Biomarker Analysis Protocol
2
Predictors of Prostate Cancer Upgrading
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A total of 257 low-risk PCa patients were stratified into two groups according to PGU. When comparing patients with and without PGU, we assessed the difference in clinicopathological profiles of patients using the chi-squared test, Fisher's exact test, and the Mann–Whitney test. Multivariate logistic regression with adjusting Epstein's clinical factors such as PSA density, clinical stage, number of positive biopsy core, percentage of tumor in a core, and age, was performed to identify an independent predictor of PGU. Predictive accuracy for the aforementioned multivariate logistic regression model was assessed with receiver operating characteristics–derived area under the curve (AUC) analysis. Another multivariate logistic regression model was built with the addition of genetic information derived from the exome sequencing, predictive accuracy was assessed by same method. The two AUCs were compared via a Mantel-Haenszel test. The SPSS software package version 15.0 (Statistical Package for Social Sciences, Chicago, IL, USA) and Medicalc software version 11 (Mariakerke, Belgium) was used for statistical analysis. A 2-tailed P<0.05 was considered significant for all analyses.
3
Statistical Analysis of LINC01420 in Thyroid Cancer
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Statistical analysis was performed using the SPSS software package, version 15.0 (SPSS, Inc.). Significant differences between two groups were compared using two-tailed Student's t-test. Statistical comparisons between two paired groups was performed using a paired t-test. For comparison of >2 groups, one-way ANOVA was used, followed by the Newman-Keuls post hoc test. Kaplan-Meier and Cox regression analyses were used to evaluate the association between LINC01420 and disease-free survival (DFS), as well as the prognosis of patients with TC. P<0.05 was considered to indicate a statistically significant difference.
4
Comparative Limb Morphometry Analysis
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SPSS software package version 15.0 (SPSS Inc., Chicago, IL, USA) was used to perform the statistical analysis. Values are expressed as mean ± standard deviation and range as appropriate. The statistical significance of the data was determined using Students’ t test. Paired t tests were performed to identify any statistically significant differences in measurements taken from the left versus right sides. Sex-related differences were assessed using a two-sample t test with equal variances. The differences in the laminar width at different planes were compared using a randomized block design of analysis of variance followed by the post hoc Student–Newman–Keuls q test for individual comparisons. The level of significance was set at P < 0.05 for all statistical analyses.
5
Prognostic Significance of SPC25 in Prostate Cancer
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The numerical data are presented as the mean ± standard deviation of ≥3 repeats. To determine the associations between SPC25 expression and the clinical characteristics of the tumors, Student's t-tests or Mann-Whitney U-tests were used as appropriate. For variables ≥3 groups, the Kruskal-Wallis H test was used for non-parametric analysis. Tukey HSD was used as the post hoc test. Kaplan Meier analysis, followed by the log-rank test, and Cox regression analyses were utilized to assess the association between SPC25 and overall survival as well as the prognosis of PCa. All tests were two-sided and P<0.05 was considered to indicate a statistically significant difference. Statistical analysis was performed using the SPSS software package, version 15.0 (SPSS, Inc., Chicago, IL, USA).
6
Analysis of Color Differences using SPSS
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Statistical Package for Social Sciences (SPSS)
software package version 15.0 (SPSS Inc., Chicago,
IL, USA) was used in this study. The distributions of
the variables were consistent with the assumptions of
normal distribution. Therefore, one-way analysis of
variance (ANOVA) and Bonferroni tests were used
for multiple and pairwise comparisons to analyze
color differences (ΔE), respectively. Confidence
interval was set 95% and p values less than 0.05
were considered statistically significant.
software package version 15.0 (SPSS Inc., Chicago,
IL, USA) was used in this study. The distributions of
the variables were consistent with the assumptions of
normal distribution. Therefore, one-way analysis of
variance (ANOVA) and Bonferroni tests were used
for multiple and pairwise comparisons to analyze
color differences (ΔE), respectively. Confidence
interval was set 95% and p values less than 0.05
were considered statistically significant.
7
UHRF1 Expression Predicts Prostate Cancer Prognosis
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Numerical data are expressed as the mean ± standard deviation (SD). All in vitro experiments were performed at least three times. BCR is defined as two sequential PSA values post-RP ≥ 0.2 ng/ml. The BCR-free survival time was regulated as the time from the date of surgery to BCR. Follow-up data were updated in December 2013. The probability of survival was estimated using the Kaplan-Meier method. The log-rank test was used to compare differences in survival times. To analyse the relationship between UHRF1 expression and the clinical characteristics of the tumours, the chi-squared test, t-test, Fisher’s Exact test, or Mann-Whitney U-test was used as appropriate. A Cox proportional hazards model was used to establish independent factor(s) that predicted survival. All tests were two-sided, and statistical significance was set at p < 0.05. Statistical analysis was performed using the SPSS software package, version 15.0 (SPSS Inc., Chicago, IL).
8
Tree Responses to Wind Load
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Mean values of each index for each tree species by room unit were used for statistical analysis. The responses of trees to wind load in leaf morphology, stem growth and biomass allocation were expressed by the differences in SLA, in stem diameter and in percentage of root biomass between T2 and CK, which produced significant effects on trees, respectively. The differences (D) were determined using the following equation [9 ]:
WhereT2 is the variable for trees under T2 treatment, CK is the variable for trees in the control.
General linear model (GLM) was applied to separate the variance explained by species, treatment, the interaction between them, and random effect of room. The difference among wind treatments were then analyzed by one-way ANOVA. Post-hoc statistical groupings were determined with a stringent Bonferroni correction. Simple linear regression was used to test relationships between LDI under control and the differences in morphology (the differences in SLA, in stem diameter, and in percentage of root biomass under T2) for the eight tree species. All analyses were performed with SPSS software package version 15.0 (SPSS, Chicago, IL).
WhereT2 is the variable for trees under T2 treatment, CK is the variable for trees in the control.
General linear model (GLM) was applied to separate the variance explained by species, treatment, the interaction between them, and random effect of room. The difference among wind treatments were then analyzed by one-way ANOVA. Post-hoc statistical groupings were determined with a stringent Bonferroni correction. Simple linear regression was used to test relationships between LDI under control and the differences in morphology (the differences in SLA, in stem diameter, and in percentage of root biomass under T2) for the eight tree species. All analyses were performed with SPSS software package version 15.0 (SPSS, Chicago, IL).
9
Statistical Analysis of Experimental Data
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The statistical calculations were carried out with the SPSS software package, version 15.0 (SPSS Inc., Chicago, IL, USA) for Windows. Results for descriptive statistics were expressed as mean ± standard deviation (SD). Statistical comparisons of continuous and multiple variables among the groups were performed using Kruskal–Wallis test, based on their distribution. According to the Kruskal–Wallis test, statistical significance was observed, and the Mann–Whitney U-test was used to determine the difference between two groups. P-values less than 0.05 was considered as statistically significant.
10
Genetic Factors in Obesity Development
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Continuous variables are presented as mean ± standard error, and dichotomous variables as frequencies and percentages. Association of the Ile269Asn mutation with obesity was tested using logistic regression. This association was adjusted for sex and admixture including samples with available global ancestry estimations (482 controls and 1062 cases). The distribution of quantitative variables was evaluated using the Kolmogorov–Smirnov test; variables with non-normal distribution were log-transformed. Treatment effects between Ile269Asn mutation carriers and non-carriers were calculated using paired Student t test. Differences in the treatment effect between the groups were calculated using linear regression models. Statistical analyses were performed with the SPSS software package version 15.0 (SPSS, Chicago, IL, USA).
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