All data are presented as means ± SD. ANOVA and Student-Newman-Keuls tests were used for multiple comparisons and a value of p < 0.05 was considered significant. Difference in serum MCT level between healthy people and pancreatic cancer patients was calculated using Wilcoxon Signed Ranks Test. Correlations between MCT and MVD were calculated using Pearson’s (r) analysis. All statistical analysis were performed with the SPSS 17.0 statistical software package (IBM, Armonk, NY, USA).
Spss 17.0 statistical software package
Manufactured by IBM
Sourced in United States
SPSS 17.0 is a statistical software package developed by IBM. It provides a comprehensive set of tools for data analysis, including descriptive statistics, correlation analysis, regression analysis, and more. The software is designed to help users analyze and interpret data efficiently.
Automatically generated - may contain errors
Lab products found in correlation
Prism 5, by GraphPad (2 mentions)
Statistical software package, by MedCalc (1 mentions)
Stata 12, by StataCorp (1 mentions)
Luciferase assay kit, by Promega (1 mentions)
Lipofectamine, by Thermo Fisher Scientific (1 mentions)
Opti mem, by Thermo Fisher Scientific (1 mentions)
Psicheck 2 vector, by Promega (1 mentions)
43 protocols using spss 17.0 statistical software package
1
Serum MCT Levels in Pancreatic Cancer
2
Comparison of Neuropsychiatric Measures
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Student's t-test was used to compare the HAMD scores, NPI scores, CTQ subcomponents scores, TV width and volume of patients from E+D and E-D groups. Bivariate correlation analysis was used to find the association of TV width/volume and total CTQ score/CTQ subcomponent scores. The data are expressed as the mean ± standard error. P value less than 0.05 was considered statistically significant. All statistical analyses were carried out using the SPSS, 17.0 statistical software package (IBM Corp., Armonk, New York, USA).
3
Genetic Diversity Analysis of Bai Population
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We performed data processing and statistical analysis using Microsoft Excel (Redmond, WA, USA) and SPSS 17.0 statistical software package (SPSS, Chicago, IL, USA), including Hardy‐Weinberg equilibrium (HWE) analysis and χ2 test. Accurate testing was used to determine whether the genotype frequency of each VIP variant in the Bai populations deviated from the HWE balance. The genotype frequencies of the Bai and 11 HapMap populations were calculated and compared using the χ2 test. All p values were obtained two‐sided, and p < .05 were considered statistically significant before correction. In order to reduce the error detection rate of multiple tests, after Bonferroni correction, p < .05/(81*11) was indicated statistically significant. The structure (version 2.3.4) software (Excoffier, Laval, & Schneider, 2007 ) was used to analyze and compare the genetic structure of 12 populations. The value of Fst was calculated using Arlequin (version 3.1) software to infer the degree of genetic differentiation between populations (Evanno, Regnaut, & Goudet, 2005 ).
4
Genetic Predictors of Autism Spectrum Disorder
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Statistical analysis included, besides descriptive statistics, nonparametric and parametric tests depending on the variable type. χ2 and t test were used to test possible differences between case and control group on several control variables. The χ2 test was also used for the assessment of possible genotype departure from Hardy-Weinberg equilibrium. Three binary logistic regression models were used to test the predictive effects on ASD, with the following sets of predictors: (1) individual genotypes, (2) individual genotypes and genotype-genotype interactions, and (3) individual genotypes, genotype-genotype interactions, maternal smoking status during pregnancy and smoking- genotype interactions. As effect size indicators we used odds ratio (OR, with the 95% confidence interval), percentage of correct classification and Nagelkerke r2. The probability level of ≤0.05 was considered statistically significant. For statistical analysis the SPSS 17.0 statistical software package (SPSS Inc, Chicago, IL, USA.) was used.
5
Genetic Polymorphisms and Bladder Cancer
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The distribution of the GSTA1 and GSTP1 polymorphisms for the case and control populations was tested for the Hardy–Weinberg equilibrium by χ2 test. As a measure of effect size, odds ratio (OR) with corresponding 95% confidence interval (95%CI) was used to describe the strength of association between the genotypes and bladder cancer modified by occupational exposure. Unconditional logistic regression analysis is applied. Bearing in mind that age and smoking are well established risk factors for bladder cancer, we adjusted OR by these variables as potential confounders. Interactions between GST polymorphisms and occupational exposure were included in the logistic regression models and also adjusted by potential confounding variables. The probability level of ≤0.05 was considered statistically significant. For statistical analysis the SPSS 17.0 statistical software package (SPSS Inc, Chicago, IL, USA.) was used.
6
Predicting Stroke Prognosis with PWI-ASPECTS
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Statistical analysis was performed using the SPSS 17.0 statistical software package (SPSS Inc., released 2008, Chicago, IL, USA). The correlation between the PWI-ASPECTS and the mRS score was evaluated by Spearman correlation analysis. The difference in PWI-ASPECTS score between the good prognosis group and the poor prognosis group was assessed by the Mann-Whitney test; Receiver Operating Characteristic (ROC) analysis of the PWI-ASPECTS score was performed in order to seek out the best predictive index for long-term prognosis. A P-value of less than 0.05 was defined as a difference with statistical significance. The data above were independently evaluated by two experienced physicians without prior knowledge of the patients’ medical conditions. Subjects on whom both physicians’ assessments agreed were recorded; cases without agreement were reassessed.
7
Fluorescence-based Protein Quantification
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Values are presented as the arithmetic mean ± standard error. Each experiment was repeated at least 3 times. The SPSS 17.0 statistical software package (Chicago) was used to perform ANOVA analysis of all data. Histograms were prepared with GraphPad Prism 5.0 software (San Diego, CA). Fluorescence intensity was analyzed by ImageJ software (National Institutes of Health). All results were considered statistically significant at a P value < 0.05.
8
Comparative Statistical Analysis of Experimental Data
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All experiments were repeated at least three times. Data were analyzed with SPSS 17.0 statistical software package (SPSS, IL, USA) and presented as mean ± SD. Comparisons between groups were performed by a Student's paired two-tailed t test. Two-way analysis of variance was used to examine differences between groups, with post hoc analyses performed by Student-Newman-Keuls method. P<0.05 was considered to be statistically significant.
9
Evaluating TACC3 Expression and Survival
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Statistical analysis was performed using the SPSS 17.0 statistical software package. The ROC curve used to define the TACC3 IRS cut-off value was analysed using the MedCalc statistical software package, version 11.0.1 (MedCalc Software bvba, Ostend, Belgium). Correlations between TACC3 expression and clinicopathologic variables were evaluated using Pearson's χ2 test. Overall survival (OS) and disease-free survival (DFS) curves were acquired using the Kaplan-Meier method and compared with the log-rank test. A Cox proportional hazard model was used to conduct the multivariate survival analysis shown in Table 2 (criteria P < 0.05). Analyses of differences between groups (in the RTCA, migration and nude mice xenograft assays) were achieved using a two-tailed t test. A two-sided P < 0.05 was considered statistically significant.
10
Statistical Analysis of Experimental Data
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All data were expressed as mean ± standard deviation. SPSS 17.0 statistical software package was used for statistical analysis (version 13.0, SPSS Inc., Chicago, IL, USA). The difference in means between two groups was assessed by the Student's t-test. Differences in means among three groups were analyzed using one-way ANOVAs followed by intergroup comparison using the Dunnett test. The difference between each time point in different groups was analyzed using two-way ANOVAs with time and drug treatment considered as factors followed by the Bonferroni post hoc test. A P-value of < 0.05 was considered statistically significant.
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