Statistical analyses were performed with IBM SPSS Statistics software for Windows (version 20.0; IBM, Armonk, NY) and GraphPad Prism software for Windows (version 6; GraphPad Software, San Diego, Calif). A priori subject stratification determined based on postbronchodilator FEV1 percent predicted measurement was performed. Nonparametric and parametric data were presented as medians (interquartile ranges) or means (SDs), respectively. Comparisons across groups were analyzed by using parametric and nonparametric ANOVA with post hoc testing for pairwise comparisons. Pairwise comparisons were made by using t tests or Mann-Whitney tests, as appropriate. Statistical significance was reached if the P value was less than .05. Factor and cluster analysis were carried out with IBM SPSS Statistics software for Windows (version 20.0). The Kaiser criterion was used to select the number of the factors, and Ward hierarchical clustering was used to determine the number of clusters (k). Cluster membership was derived by using k-means clustering (see the Methods section in this article's Online Repository for further details).
Spss statistics software for windows
Manufactured by IBM
Sourced in United States
SPSS Statistics is a software package used for interactive, or batched, statistical analysis. It is designed to perform a wide variety of data analysis and data management operations, including data entry, data manipulation, and statistical analysis.
Automatically generated - may contain errors
Lab products found in correlation
Sas software, by SAS Institute (2 mentions)
Prism software for windows, by GraphPad (1 mentions)
Stata se software, by StataCorp (1 mentions)
Spss statistics for window, by IBM (1 mentions)
Prism 7, by GraphPad (1 mentions)
Prism 8, by GraphPad (1 mentions)
Spss statistics software, by IBM (1 mentions)
145 protocols using spss statistics software for windows
1
Statistical Analysis of Lung Function in Cohorts
2
Statistical Analysis of Critically Ill Patients
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Statistical analyses were performed using IBM SPSS Statistics software for Windows (IBMCorp., Armonk, N.Y., USA). The distributions of the continuous and categorical variables were presented as mean ± standard deviation and median (minimum-maximum), and frequency/percentages respectively. Shapiro-Wilk test was used to test whether the quantitative data conformed to the normal distribution. Student-t test and Mann-Whitney U test were used to compare the quantitative variables with or without normal distribution between groups. The Pearson chi-square tests were used to compare the qualitative data between groups. Sensitivity, specificity, and receiver operating characteristic (ROC) curves were used to determine the diagnostic critical value for the parameters. Univariate logistic regression was used to explore the associations between predictor factors and outcomes. Multivariate logistic regression analysis was performed to assess the independent risk factors and all-cause death/blood transfusion of critically ill patients. The likelihood ratio (LR) method was used to input the independent variables, and the significance level for removal from the model was set at 0.1. In order to avoid multicollinearity, we deleted relevant variables in the multivariable model. Statistical significance was defined as P < 0.05.
3
Circulating RNA Age-Related Markers
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An enormous amount of data produced by the next generation sequencing makes the pre-selection process very important and challenging. In an attempt to limit the quantity of predictors which may save both time and cost, three statistical methods were conducted to select age-associated markers. According to circRNA-seq data, an adjusted p-value lower than 0.01 (the false discovery rate method) after the Spearman’s correlation analysis was regarded as a criterion to identify the age-associated circRNAs using IBM SPSS statistics software for Windows, version 21.0. Lasso regression was implemented for feature selection with an alpha value set in a range from 0.0005 to 1 within Python. Another feature selection approach SVM was performed in Python using the ‘LinearSVC’ command with the ‘C’ value set to 0.0202. The Spearman correlation coefficients (S.rho) were then calculated again for 30 validation samples according to qPCR results to validate age-dependent circRNAs. CircRNA host gene analyses were performed on string-db.org and the gene database of www.ncbi.nlm.nih.gov .
4
Evaluation of Salivary Flow with GR Extracts
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As the salivary flow rate was recorded specifically for each mouse, each mouse was acting as its negative control prior to the intervention. To evaluate the effect of GR extracts and 6-shogaol on salivary flow for each group, the salivary flow rate during the treatment weeks (mean of 16th to 18th week) was compared with the baseline (15th week) in each group. Paired-sample t test was used to analyse whether there was a significant difference between pretreatment and treatment weeks.
Second, the change in salivary flow rate of each mouse was calculated from the difference between the baseline salivary flow rate (15th week) and the mean salivary flow rate during the treatment weeks (16th to 18th week). The mean change in salivary flow was obtained for each group. One-way analysis of variance (ANOVA) with post hoc Tukey's honestly significant difference (HSD) test was used to analyse the differences in mean change in salivary flow rate amongst all 5 groups.
Statistical analysis was carried out using IBM SPSS Statistics software for Windows, version 25.0 (IBM Corp. P values <.05 were considered to indicate statistical significance.
Second, the change in salivary flow rate of each mouse was calculated from the difference between the baseline salivary flow rate (15th week) and the mean salivary flow rate during the treatment weeks (16th to 18th week). The mean change in salivary flow was obtained for each group. One-way analysis of variance (ANOVA) with post hoc Tukey's honestly significant difference (HSD) test was used to analyse the differences in mean change in salivary flow rate amongst all 5 groups.
Statistical analysis was carried out using IBM SPSS Statistics software for Windows, version 25.0 (IBM Corp. P values <.05 were considered to indicate statistical significance.
5
AR mRNA Expression and Survival Analysis
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The cut-off point for the AR mRNA level was determined by a web-based calculator cut-off finder (http://molpath.charite.de/cutoff .) and median values were used as the cut-off points for other factors to categorize continuous measurements [19 (link)]. Spearman’s rank-order correlation was used to measure the association between AR mRNA expression level and clinical factors. A Kaplan–Meier product-limit estimator was used to assess RFS distribution. Log-rank tests were used to analyze differences in RFS between the high- and low-risk groups. Univariate and multivariate COX models were used to analyze factors for predicting RFS. We derived relative risks and 95% confidence intervals (95% CIs). All tests were two-sided; alpha = 0.05 was considered significant. All analyses were conducted using IBM SPSS Statistics software for Windows, version 22 (IBM Corp., Armonk, NY, USA).
6
Prognostic miRNA Biomarkers in Renal Cell Carcinoma
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Statistical analysis was also made using IBM®SPSS®Statistics software for Windows (Version 22.0). The 2-ΔΔCq method, along with the Student t’ test were used in order to evaluate any statistical differences in the normalized expression levels of the miRNAs here explored. We used Cutoff Finder web application to generate optimum cut-off point for the three deregulated miRNAs in the plasma samples of RCC patients. Cutoff Finder selects the optimum cut-off point, separating patients into high-risk and low-risk groups, by fitting Cox proportional hazard models to the dichotomized variable and the survival variable. The optimal cut-off is defined as the point with the most significant (log-rank test) slip [35 (link)]. A Cox porportional hazard model was used to analyze the patients’ cancer-specific survival, considering as covariants TNM stage, Fuhrman nuclear grade and age (> 60 years). The concordance (χ) was used to compare the predictive ability of the association of well-known prognostic variables with the combined plasma levels of miR-210, miR-221 and miR-1233 with χ > 0.5 being considered with a good prediction ability [27 (link)].
7
Evaluating Gait Parameters of ELD Responders
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The gait parameters were quantitatively evaluated and compared among the responders, non-responders, and normal controls. Comparisons between responders and non-responders were performed to determine the significance of changes after ELD. The IBM SPSS Statistics software for Windows (version 20.0.0) was used for statistical analyses. Categorical variables were compared using a χ2 test, and continuous variables were compared using a Student t test. Spearman correlation analysis was used to calculate correlation coefficients between GRC scores and gait parameters. The statistical significance threshold was set at P < 0.05.
8
Analyzing Biological Correlations
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Data are presented as mean ± SEM, unless stated otherwise. The student’s t-test was used for comparative analysis. Correlation was determined using the Pearson correlation coefficient. P value of ≤ 0.05 was considered statistically significant. IBM SPSS Statistics software for Windows, Version 22.0, IBM Corp. was used for all analyses (IBM Corp., USA).
9
Comparative Analysis of PFPC Introduction
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Data for the periods before and after introducing the PFPC were compared using Student’s t-tests for continuous variables and Chi-squared tests for categorical variables, using IBM SPSS Statistics software for Windows, Version 22.0, Armonk, NY.
10
Surveying Preferences for Department
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Statistical analysis of the study was conducted using IBM SPSS Statistics software for Windows (Version 22.0, IBM Corp. Armonk, New York, NY, USA). The descriptive statistics of correct answers to the questionnaire are mean ± standard deviation, given as frequency and percentage. A chi-square test was used to compare those who preferred the department with those who did not want it. The probability of error was accepted as p < 0.05 [21 (link)].
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