ELISA antibody titers data were analyzed using 1-way ANOVA followed by post-hoc Tukey's HSD tests (IBM SPSS 12, USA). The clinical signs, ciliary activity, and macroscopic gross lesions data were analyzed using the Kruskal-Wallis H test (IBM SPSS 12) to obtain statistical analysis among the groups. The p values < 0.05 were considered significant.
Spss 12
Manufactured by IBM
Sourced in United States
SPSS 12.0 is a data analysis software product developed and marketed by IBM. It provides tools for data management, statistical analysis, and reporting. The core function of SPSS 12.0 is to enable users to perform a variety of statistical analyses on data, including descriptive statistics, hypothesis testing, and predictive modeling.
Automatically generated - may contain errors
Lab products found in correlation
Prism 8, by GraphPad (2 mentions)
Spss sample power 3, by IBM (2 mentions)
Sas 9, by SAS Institute (2 mentions)
Spss modeler, by IBM (1 mentions)
R2.14.0 software for windows, by R Project for Statistical Computing (1 mentions)
Prism 6, by GraphPad (1 mentions)
Design expert software, by Stat-Ease (1 mentions)
Accurus, by Alcon (1 mentions)
Prism 5, by GraphPad (1 mentions)
Prism 7, by GraphPad (1 mentions)
Fluoview fv1000, by Olympus (1 mentions)
100 protocols using spss 12
1
Statistical Analysis of ELISA and Clinical Data
2
Protein Expression Quantification Protocol
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Data were expressed as the mean values ± standard deviation (SD) from three independent experiments. Data were analyzed by Student's t test or one‐way ANOVA, followed by Tukey‐Kramer multiple comparison test using SPSS 12.0 software (IBM SPSS, Chicago, IL, USA). The statistically significant was regarded as p < .05.
3
Comparative Genetic Variant Analysis
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Continuous variables (normally distributed) were expressed as mean and standard deviation (SD), while non-parametric variables were expressed as median (interquartile range). Categorical variables were presented as numbers (proportions). Independent sample t-tests combined with Levene’s tests were used for comparison between groups of continuous variables, and Mann–Whitney U-tests were used for non-parametric variables. Categorical variables were compared using the chi-squared test. The Kaplan–Meier curve was used for device-free survival analyses. The P values for the comparison between curves in patients with versus without significant genetic variants identified, male versus female, TTN group versus other genes, and TTN group versus LMNA group were calculated using Log Rank (Mantel–Cox test). Statistical significance was set at a 2-sided p-value of <0.05. SPSS 12.0 (IBM SPSS Inc. Chicago, IL, USA) was used for statistical analysis.
4
Gender Impact on Research Productivity
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For the overall analysis of the data, SPSS® 12.0 (IBM SPSS, Chicago, IL, USA), was used. The software was sourced by the University of Córdoba, Spain. Descriptive statistics were analysed for the quantitative variables using measures of dispersion and position, and Chi-square was used as the test of significance for the qualitative variables. An independent samples t-test for the mean comparison was applied to analyse how the presence of women affects the productive results.
5
Statistical Analysis of Experimental Data
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Statistical analysis was performed by the independent T-test or a one-way analysis of variance (ANOVA) followed by Student–Newman–Keuls (SNK) test, using the SPSS 12.0 software (IBM SPSS, Chicago, IL, USA).
6
Comparative Statistical Analysis Techniques
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Statistical analysis was performed by the independent T-test or a one-way analysis of variance (ANOVA), followed by Student–Newman–Keuls (SNK) tests using the SPSS 12.0 software (IBM SPSS, Chicago, IL, USA).
7
Statistical Analysis of Experimental Data
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Data were analyzed using the SPSS 12 (IBM Corp., Armonk, NY, USA) for Windows. Mean ± standard deviation, median, and percentages are presented. Paired t-test is used to compare the means.
8
Mapping Glioma Tumor Connectivity Profiles
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Analysis of fMRI data included preprocessing and analysis steps, performed using multiple toolbox/packages within the MATLAB environment: SPM12, CONN toolbox, as well as Connectome Workbench (https://www.humanconnectome.org/software/connectome-workbench ), SPSS 12 and SPSS modeler (IBM Corp. Armonk, NY).
First, a seed-based connectivity analysis was performed to characterize the connectivity profile of the solid tumor in each patient (Figure 2 ). Clusters of significant cortical and subcortical connectivity were mapped onto known RSNs to establish network-level connectivity of solid tumor (“network mapping”) according to published methods based on Dice coefficient25 (Figure 3 ). Then, individual connectivity profiles were used to predict OS by means of a voxel-wise whole-brain multiple regression model (Figures 4 and 5 ). All the analyses were performed separately on newly diagnosed and recurrent glioma samples. Below we report details on each analysis.
First, a seed-based connectivity analysis was performed to characterize the connectivity profile of the solid tumor in each patient (
9
Normality Testing and Statistical Analysis
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All quantitative data were tested for normality. Under normal distribution, the group arithmetic mean (M) and SE/SD were calculated using the Microsoft Excel data analysis package (version 14.0.4760.1000, 32-bit). The data of immunohistochemical and morphometric studies obtained as a result of the calculation were processed using the computer program SPSS 12 for the Windows statistical software package (IBM Analytics, USA). Comparisons were made using analysis of variance. p-value <0.05 was considered statistically significant.
10
Puberty Health Determinants and Beliefs
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All data analyzed through SPSS 12 (IBM SPSS Statistics, Meadville, USA) and offered with frequency and mean (SD) for variables. Data were analyzed using one-sample Kolmogorov-Smirnov test for determine of normality. A series of univariate general linear models were used to assess the relationship between puberty health and health belief model constructs. In these analyses unadjusted (univariate) and adjusted (multivariate) linear regression coefficients and 95% confidence intervals (95% CI) were presented as effect size of interest. In addition, a multiple general linear model was used to assess the relationship. A p<0.05 were considered statistically significant.
+ Expand
All data analyzed through SPSS 12 (IBM SPSS Statistics, Meadville, USA) and offered with frequency and mean (SD) for variables. Data were analyzed using one-sample Kolmogorov-Smirnov test for determine of normality. A series of univariate general linear models were used to assess the relationship between puberty health and health belief model constructs. In these analyses unadjusted (univariate) and adjusted (multivariate) linear regression coefficients and 95% confidence intervals (95% CI) were presented as effect size of interest. In addition, a multiple general linear model was used to assess the relationship. A p<0.05 were considered statistically significant.
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