ANOVA was used to determine any significant differences between measurements, p < .05, α = 0.05. A univariate general linear model was applied for morphometric analysis, where “group” (for example, normoxia‐PBS) was the main factor, and “litter” (values from one litter) and “animal” (values from a single mouse) allocated to be random factors (800 measurements per mouse for MLI, five measurements per mouse for other morphometric tests, with two to four mice per litter analyzed at each time point). ANOVA was followed by a Dunnett's post hoc test against hyperoxia‐ and normoxia‐PBS controls. Error bars were standard error of the mean (SEM). For Western blot analysis samples were compared within one gel, control samples present in each gel. Three to four animals were analyzed per group. Data analysis was performed using PASW statistical software (IBM, Armonk, NY, http://www.ibm.com ).
Pasw statistical software
Manufactured by IBM
Sourced in United States
PASW statistical software is a data analysis and statistical software package developed by IBM. It provides a comprehensive set of tools for data manipulation, analysis, and visualization. The software is designed to help users explore, analyze, and interpret complex data sets.
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Lab products found in correlation
12 protocols using pasw statistical software
1
Comparative Statistical Analysis of Lung Morphometry
2
Microbial Diversity Analysis Protocol
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Data were analyzed with the statistical software Statistix version 10 (Analytical Software, Tallahassee, FL, USA). A paired t-Test was used to compare symptoms and global acceptance.
Differences in alpha diversity between both phases were estimated using a non-parametric test and Monte Carlo permutations, for each of the three alpha diversity estimators (PD whole tree, chao1, and observed OTUs). We also estimated the existence of statistically significant differences between groups for the beta diversity unweighted and weighted UNIFRAC distances, using a PERMANOVA test.
To assess whether specific differences occurred in some bacterial taxa between phases, we selected the taxa that were present in at least 80% of the samples per each phase. The non-parametric Kruskal-Wallis test was used to compare the existence of differences in the relative abundance of the bacterial taxa between phases using the PASW statistical software, version 20.0 (IBM Inc., Chicago, IL, USA).
Differences in alpha diversity between both phases were estimated using a non-parametric test and Monte Carlo permutations, for each of the three alpha diversity estimators (PD whole tree, chao1, and observed OTUs). We also estimated the existence of statistically significant differences between groups for the beta diversity unweighted and weighted UNIFRAC distances, using a PERMANOVA test.
To assess whether specific differences occurred in some bacterial taxa between phases, we selected the taxa that were present in at least 80% of the samples per each phase. The non-parametric Kruskal-Wallis test was used to compare the existence of differences in the relative abundance of the bacterial taxa between phases using the PASW statistical software, version 20.0 (IBM Inc., Chicago, IL, USA).
3
Influence of Residence on FFB Questionnaire Responses
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The outcome of the FFB questionnaire is summarized using means and standard deviations by item and by the eight subscales and the two overall summary scores (for FFB). Items are further characterized with percentages. To test whether the country of residence has an influence on the answers to individual items, the P-values of Pearson’s χ2 test for homogeneity and Fisher’s exact test or of the χ2 test after rebinning were computed. When testing whether the country of residence has an influence on the subscales and main scales, χ2 and one-way Kruskal–Wallis tests were performed. The PASW statistical software was used for all analyses (IBM Corporation, Armonk, NY, USA, v 18.0), except for Fisher’s exact tests, which were performed with the online calculator on the webpage http://www.physics.csbsju.edu/stats/exact_NROW_NCOLUMN_form.html .
4
Comparing Clinical Characteristics in Study Groups
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All statistical analyses were performed using PASW statistical software (Ver. 21 for Windows, SPSS, Inc., Chicago, IL, USA). Fisher’s exact tests were used to compare clinical characteristics between study groups. Mann–Whitney U tests were applied to compare the mean values of clinical variables. Statistical significance was defined as p < 0.05.
5
Quantifying Mitosis Duration in Yeast
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To calculate time spent in mitosis, cells were grown on YG with (for wild type, ∆mad2) or without UU (for ∆nup2, ∆nupA, ∆nup2∆mad2, and ∆nupA∆mad2) at room temperature (21–23°C) overnight (12–16 hr), and GFP-tubulin signal was followed throughout mitosis. Images were captured every 30 s using a spinning-disk confocal microscope (details described earlier). Time in mitosis was measured starting from the time point where the spindle is visible until it disassembled. Statistical analysis was conducted using PASW statistical software (SPSS, Chicago, IL). The p values were calculated using the Student's t test, considering p < 0.05 as significantly different with 95% confidence intervals and p <0.01 as highly significant with 99% confidence.
6
Prognostic Factors for Hepatocellular Carcinoma
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Continuous data were compared using two-sample t tests, and categorical variables were compared using chi-squared tests between the two groups. Cumulative LTP, cumulative IDR, DFS, and OS rates were estimated using the Kaplan-Meier method. Prognostic factors for DFS and OS were assessed using Cox regression models. Proportional hazard (PH) assumption for the Cox proportional hazard model was tested using Schoenfeld’s method. For the variables with violation of PH assumption, the time-dependent Cox regression was applied. When the time dependence was not significant, the Cox proportional hazard model was applied. Possible risk factors with P values of 0.1 or less at univariate analyses were entered into the multivariate Cox proportional hazard models. Subgroup analysis for patients with ≤ 2 cm HCCs was performed with Cox proportional hazard models. All statistical analyses were performed using a software (PASW statistical software, version 18.0; SPSS, Chicago, IL). For all tests, a P value < 0.05 was defined as a significant difference.
7
Histogram Analysis of Tumor Pathological Features
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All statistical analyses were done with PASW statistical software (ver. 23.0, SPSS, IBM, Chicago, IL). A p-value <0.05 was considered to indicate a significant difference. All measurements were correlated, and the concordance of the interobserver variability was tested by calculating Spearman and interclass correlation coefficients (ICCs). Agreement was interpreted according to the ICC as: > 0.8, excellent; 0.6–0.8, good; 0.4–0.6, moderate; and <0.4, poor concordant. We used the Jonkheere-Terpstra test to correlate the histogram parameters with the pathological grades of the tumors (grades 1–3). The Mann-Whitney test was used to compare the histogram parameters between high-grade (grade 3) and low-grade (grades 1 and 2) tumors. We also used the Mann-Whitney test to examine the correlations between the presence or absence of lymphovascular invasion and pleural invasion with each histogram parameter. We performed a receiver-operating characteristics (ROC) curve analysis to assess the diagnostic performance of histogram parameters for distinguishing different pathological features when appropriate.
8
Statistical Analysis of Structural Parameters
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The analyses were conducted with PASW statistical software (version 21.0; SPSS™, Chicago, IL). The two-sided p values <0.05 were considered to indicate statistical significance. The descriptive data were presented as the mean ± standard deviation (SD). After checking the variables of interest for normality (Kolmogorov-Smirnov test), the correlations between the structural parameters were expressed as R Pearson’s coefficient. To compare correlations, Fisher’s transformation was applied.
9
Prognostic Value of SHP Expression in Clinicopathological Factors
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The associations between clinicopathological or hematological factors and SHP levels were analyzed using Pearson's χ2 test and Fisher's exact test, and survival curves were created by the Kaplan-Meier method. The prognostic value of SHP expression was evaluated using the log-rank test for univariate analysis and the Cox proportional hazards model for multivariate analyses. A backward stepwise selection of covariates was used for the Cox proportional hazards model, and P<0.1 was defined as the threshold for covariate inclusion. P<0.05 was considered to indicate a statistically significant difference. All statistical analyses were conducted using PASW statistical software (version 17.0; SPSS, Inc.).
10
Optimizing Confocal Microscopy Staining
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