The data encompassing C mineralization, pH, EC, and available N, P, K contents in control soil (S), BC alone (Q-BC), the arithmetic sum of control soil (S) and BC alone (S + Q-BC), and soil with BC mixture (S-BC) over 290 days underwent one-way ANOVA. Post hoc analysis using Duncan’s multiple range test at a 5% significance level was performed to distinguish the means (conducted in SPSS version 20.0). Additionally, a Pair T-test (p = 0.05) was employed to differentiate the mean values of cumulative C mineralization data between S + Q-BC and S-BC mixtures (conducted in SPSS version 20.0). Principal Component Analysis (PCA) using R software)67 was carried out to discern the contribution of various factors to the priming effects of soil organic matter and to segregate homogeneous treatments based on BC feedstock and pyrolysis temperature.
Spss version
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Lab products found in correlation
Spss statistics for window, by IBM (48 mentions)
Spss statistic, by IBM (43 mentions)
Prism 6, by GraphPad (13 mentions)
Spss statistics for windows version, by IBM (11 mentions)
Prism 5, by GraphPad (10 mentions)
Prism 7, by GraphPad (10 mentions)
Prism 8, by GraphPad (10 mentions)
Epidata version 3, by IBM (7 mentions)
Spss statistics for windows version 26, by IBM (5 mentions)
R software, by R Project for Statistical Computing (5 mentions)
Software, by MedCalc (4 mentions)
Spss software, by IBM (4 mentions)
Sas 9, by SAS Institute (4 mentions)
Excel 2007, by IBM (3 mentions)
Prism 9, by GraphPad (3 mentions)
Spss statistics for windows version 23, by IBM (3 mentions)
R version 4, by R Project for Statistical Computing (3 mentions)
Spss version 25, by IBM (2 mentions)
R version 3, by R Project for Statistical Computing (2 mentions)
Spss statistics 20, by IBM (2 mentions)
3 082 protocols using spss version
1
Biochar's Impact on Soil Properties
2
Forensic Analysis of Jining Han Population
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A modified Powerstats spreadsheet was used to calculate forensic parameters, including allele frequencies for the 16 X-STR loci. Exact tests of Hardy-Weinberg equilibrium (HWE) for each locus were performed using Genepop version 4.2 (http://genepop.curtin.edu.au/). The polymorphism information content (PIC), gene diversity (GD), power of discrimination in males (PDM), power of discrimination in females (PDF), and mean exclusion chance (MEC) in trios and duos (MEC Krüger , MEC Kishida , MEC Desmarais , and MEC Desmarais Duos ) were estimated using StatsX (Statistics for X-STR) version 2.0 (Lang et al. Citation2019) . A principal component analysis (PCA) was implemented in SPSS version 21.0. Phylip 3.695 was used to analyse the genetic distances among the Jining Han and other populations. F ST and pvalues were estimated for 16 X-STR loci to evaluate differentiation between Jining Han populations and other groups using Arlequin 3.5. A phylogenetic tree was reconstructed using Mega 7.0 with the neighbor-joining method. Furthermore, a two multidimensional scaling (MDS) plot was built using SPSS version 21.0. Values of p < 0.05 were considered significant.
3
Analyzing Dairy Cow Reproductive Traits
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The data obtained from the questionnaire was entered into the Microsoft Excel 2007 and transferred to SPSS version, 17.0. Data were statistically analyzed to calculate the effect of reproductive and productive traits of dairy cows. Various standard statistical procedures ((percentages, mean and chi-square test etc.) had been adopted in this study. The mean and standard error of mean for the reproductive traits were calculated with help of a computer package programme (SPSS) version 17.0. Mean of different traits were then tested by using analysis of Variance (ANOVA) by Duncan Multiple Range Test (DMRT). Data were presented as Mean±SE. P<0.05 was considered as significant. Simple ANOVA was performed considering the age of dairy cows and to observe the significant differences among the mean values, Duncan's multiple range test (DMRT) was performed to observer significant differences of reproductive traits in different breed, genotype, age, parity, body weight etc. of dairy cows.
4
Explorative Statistical Analysis of 50 Variables
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The data are presented as means ± SD in the text whereas tables and figures do not list SDs to facilitate their reading. However, all the SDs are duly reported in Supplemental Table 3 .
Due to the small sample size, we could not assess the normal distribution of the data and used the Wilcoxon signed rank test to analyse paired data. However, although not visible in the current dataset, the concerned variables are known to undergo a normal distribution26 (link),27 and were also analyzed by the paired sample T-test. The outcomes from both tests have been reported in the text: “W” marks the Wilcoxon signed rank test P value whereas “T” indicates the P values according to T-test. In figures we only reported the outcome of the T-test. Data analysis was performed using IBM-SPSS® version 26.0.28 (link)
In spite of the multiplicity of the tests performed (50 variables), being this an explorative study, for further investigation, no correction of experiment-wise error rates in multiple comparisons has been applied,28 (link)
and a two-sided P value <.05 was considered significant in all analyses.
Due to the small sample size, we could not assess the normal distribution of the data and used the Wilcoxon signed rank test to analyse paired data. However, although not visible in the current dataset, the concerned variables are known to undergo a normal distribution26 (link),27 and were also analyzed by the paired sample T-test. The outcomes from both tests have been reported in the text: “W” marks the Wilcoxon signed rank test P value whereas “T” indicates the P values according to T-test. In figures we only reported the outcome of the T-test. Data analysis was performed using IBM-SPSS® version 26.0.28 (link)
In spite of the multiplicity of the tests performed (50 variables), being this an explorative study, for further investigation, no correction of experiment-wise error rates in multiple comparisons has been applied,28 (link)
and a two-sided P value <.05 was considered significant in all analyses.
5
Standardized Ionic Diameter Trends
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Distributions of variables were assessed by Shapiro-Wilk test and data were standardized, for each ion, on mean and SD of baseline diameters (z-score). Two-way analysis of variance for repeat measures (RM ANOVA) was used to detect differences among the groups; then “time” (day 0–210) and “group” (kind of ion) were considered as “within-subjects” and “between-subjects” factors with four “time” and four “group” levels, respectively. Polynomial contrasts for trends and post hoc tests were additionally performed; in this instance, a Bonferroni correction was applied in order to take into account multiple comparisons. For “within-subjects” effects the Mauchly criterion was used to determine if the assumption of sphericity was met.
Statistical analyses were performed using IBM-SPSS version 21.0 (IBM Corp., Armonk, NY, USA, 2011). A two-tailed P value < 0.05 was considered significant.
Statistical analyses were performed using IBM-SPSS version 21.0 (IBM Corp., Armonk, NY, USA, 2011). A two-tailed P value < 0.05 was considered significant.
6
Survival Analysis of Immunotherapy Outcomes
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The Kaplan–Meier method was utilised to estimate median follow-up (reverse method), median overall survival, and median progression-free survival, as well as survival rates, with two-sided 95% confidence intervals based on a normal approximation. Survival curves were compared using the log-rank test. The restricted mean survival time (RMST) was calculated as previously described.16 (link) Two-sided 95% confidence intervals for immune-related objective response and immune-related disease control were estimated using an exact probability method. Statistical analyses for TMB and survival rates used GraphPad Prism version 8. All other statistical analyses used IBM-SPSS version 23·0. The study was registered with the European Union Clinical Trials Register, number 2015-001995-23, and ClinicalTrials.gov , number NCT02588131 .
7
Statistical Analysis of Experimental Data
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Data was entered into Microsoft Excel 2013, cleaned and then exported for analyses using IBM-SPSS version 25. Quantitative variables following a normal distribution were presented as mean ± standard deviation; and presented as median (interquartile range) otherwise. Frequencies and percentages (%) were used to describe categorical variables.
8
Anxiety Ratings Influenced by Expectations
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To assess what influenced subjects’ anxiety ratings, we ran an analysis of variance (ANOVA) with factors expectation (high/low) and outcome (shock/no shock). Additionally, we fitted a trial-by-trial linear regression model. To predict anxiety ratings on current trial T, we used 1) probability (high vs. low), 2) outcome type (shock vs. no shock), 3) interaction term, 4) elapsed time between outcome offset and rating onset, and 5) rating time (from ratings onset to offset), while also testing for influence of (items 4 and 5) outcome type of previous trials, i.e., trial T−1 and T−2. Behavioral analyses were conducted in MATLAB version 2014a (The MathWorks, Inc., Natick, MA) and IBM SPSS version 25 (IBM Corp., Armonk, NY).
9
Comparison of Statin Therapy Guideline Algorithms
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The two guideline algorithms were applied to facilitate decision-making on whether to start statin therapy for each of the included patients. The degree of agreement between the two guidelines was analyzed using Cohen’s kappa statistic. A minimum of 133 charts was needed to achieve a power of 80% at 0.05 margin of error for a 2x2 cross-tabulation. The study by Bujang et al.,6 was used as the basis for sample size calculation as well epidemiologic data from the 8th FNRI National Nutrition Survey.7 Descriptive statistics were measured to summarize demographic characteristics. T-test for independent samples and Chi-Square analyses were used to compare diabetic and non-diabetic subsets for parametric and categorical data, respectively. All statistical analyses were performed using IBM SPSS version 26.0 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armok, NY: IBM Corp).
10
Predictors of Research Publication
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