PROBIT analysis was used to calculate the lethal concentration of EE2 causing mortality in 50% of the experimental individuals (LC50) for A. clausi and A. tonsa, following the protocol described in Vincent [34 ]. All replicates were pooled for statistical treatment. Data normality and the homogeneity of variance were tested by a Kolmogorov–Smirnov test and Levene’s test, respectively (IBM SPSS v26). Data that did not follow the normal distribution were analyzed using the non-parametric test of Kruskal–Wallis, followed by Dunn’s post hoc test for multiple comparisons (IBM SPSS v26). Data that did follow normal distribution were analyzed using a one-way ANOVA followed by the Tukey–Kramer post hoc test for multiple comparisons (IBM SPSS v26). The relationship between the number of reproductive females and EE2 concentration was analyzed using Chi-square contingency tables (IBM SPSS v26). Contingency table cell residual scores higher than 1.96 were considered significantly different. The egg hatching success for each female was calculated in percentage (%) as the number of nauplii hatched divided by the total number of eggs produced and multiplied by 100 [29 (link)]. Finally, Z-score was used to determine the significant difference in the IBR/n index values among the different exposure concentrations.
Spss v26
Manufactured by IBM
Sourced in United States, United Kingdom, Spain, China, Germany, Austria
SPSS v26 is a statistical software package developed by IBM. It provides data management, analytical, and visualization capabilities to enable users to identify patterns, trends, and relationships within data sets.
Automatically generated - may contain errors
Lab products found in correlation
Prism v8, by GraphPad (44 mentions)
Prism 8, by GraphPad (23 mentions)
Prism v9, by GraphPad (16 mentions)
Sas v9, by SAS Institute (16 mentions)
Prism 9, by GraphPad (14 mentions)
Prism v7, by GraphPad (7 mentions)
Prism v6, by GraphPad (5 mentions)
Spss statistics for window, by IBM (5 mentions)
Prism 7, by GraphPad (4 mentions)
Jmp v14, by SAS Institute (4 mentions)
Sas software, by SAS Institute (4 mentions)
Microsoft excel, by IBM (3 mentions)
Excel 2019, by IBM (3 mentions)
Spss statistics for windows version 26, by IBM (3 mentions)
Excel v16, by IBM (3 mentions)
Spss statistic, by IBM (3 mentions)
Medcalc v 20, by MedCalc (3 mentions)
Prism 6, by IBM (3 mentions)
R version 3.6.3, by IBM (2 mentions)
Originpro version 2021, by OriginLab (2 mentions)
1 880 protocols using spss v26
1
Lethal Concentration of EE2 in Copepods
2
Statistical Analysis of Experimental Data
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Assumptions of normality of variance were examined by Kolmogorov–Smirnov tests performed in IBM SPSS v26. Linear regression analyses were performed by the lm function in R (v4.2.0), for the approximate p-values derived from lm function, further robustness tests were performed in JASP (v0.16)123 . ANOVA and its post hoc analysis (Tukey HSD) were performed in the IBM SPSS v26. Mann–Whitney test were performed in the IBM SPSS v26. P-values derived from ANOVA and Mann-Whitney test were reported in two-sided tests, and were adjusted by false discovery rate (FDR) correction108 (link) with a cutoff of 0.05.
3
Emotional Regulation, Self-Efficacy, and Academic Performance
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Descriptive statistics were carried out to establish the socio-demographic profile of the sample, including variables as gender, age, course, type of school and course repeats. Subsequently, correlations were carried out between the variables of emotional regulation, self-efficacy and academic performance, analyzed and processed using the statistical program IBM SPSS v26.0. In turn, a K-means cluster analysis was carried out to distribute the students in the sample into three statistically significant groups among themselves according to their standardized values, allowing groups to be created based on the similarity between the variables studied. Finally, a mediation analysis was proposed through the MACRO of SPSS v26.0 to verify the indirect effect of the self-efficacy variable in the relationship between emotional regulation and academic performance, carrying out a bootstrapping procedure with 10,000 repetitions. For all operations, a level of significance p < 0.05 with a confidence level of 95% was considerated.
4
Predicting Leptomeningeal Metastasis Risk
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We used Fisher's exact test or chi-square to compare differences in categorical variables. We incorporated the factors of P<0.05 in univariate logistic regression into multivariate regression and created a nomogram for predicting the risk of LM. By year of diagnosis, we divide 400 patients with LM into training (2012-2015, n=268) and internal validation (2010-2011, n=132) groups. We included the factor of P<0.10 in univariate cox regression into multivariate regression. We created a prognostic chart to forecast LM survival, and employed C-index, ROC curves, and calibration curves to confirm its accuracy. The Kaplan-Meier curve was used to assess the variance in survival duration between patients with LM and those without LM. To find the best cutoffs for tumor size and age, we used the x-tile v3.6.1 (Yale University) program 17 (link). To balance differences in other characteristics between LM and non-LM patients, a 1:1 PSM was done in SPSS v26.0 (SPSS Inc). Statistical analyses were conducted utilizing GraphPad Prism v8.0.2 (GraphPad Software, Inc.), SPSS v26.0 (SPSS Inc.), and R software v4.1.3 (https://www.r-project.org/ ). A significance level of P < 0.05 was considered to indicate statistical significance.
5
Prognostic Nomogram for Endometrial Cancer with Lymph Metastasis
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We transformed continuous variables into categorical variables using X-tile software and compared differences in categorical variables using the chi-square or Fisher exact test. To exclude the effect of other variables on the prognosis of EC patients with or without LM, 1:1 propensity score matching (PSM) was performed in SPSS v26.0 (SPSS Inc.). Finally, 3697 patients with LM were matched with 3697 patients without LM. In the training cohort, we included factors with P < .05 in multivariate Cox regression from univariate Cox regression to identify independent prognostic factors. According to the results of multivariate Cox regression, a nomogram was established for predicting the prognosis of EC patients with LM. We also verified its validity by receiver operating characteristic (ROC) curve, C-index, calibration plots, and Kaplan–Meier curve analyses. All statistical analyses were performed in R software v4.1.3 (https://www.r-project.org/ ), GraphPad Prism v8.0.2 (GraphPad Software, Inc.), and SPSS v26.0 (SPSS Inc.). P values < .05 were considered statistically significant.
6
Socio-demographic Profile and Variable Correlations
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Descriptive statistics were undertaken to establish the socio-demographic profile of the sample, including such variables as gender, age, course, type of school and course repeats, as well as the variables analysed in the study. Following this, correlations between the variables were investigated using statistical software IBM SPSS v26.0. Finally, SPSS v26.0ʹs MACRO tool was used to carry out mediation analyses by bootstrapping (10,000 runs). For all the operations, a p≤0.05 level of significance was adopted, with a 95% confidence level.
7
Nutrient Digestibility and Rumen Microbiome Analysis
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We used a one-way experimental design. Before any statistical analyses were conducted, we used SPSS (SPSS v 26.0, SPSS, Inc., Chicago, IL, USA)’s Kolmogorov–Smirnov test program to check the normality and outliers of all data. Levene’s test was used to test the homogeneity of variance, and the experimental data were analyzed by one-way ANOVA (Analysis of variance) of SPSS (SPSS v 26.0, SPSS, Inc.) software. Duncan’s method was used for multiple comparisons when the difference was significant, p < 0.05 was considered statistically significant and 0.05 < p < 0.10 was considered a trend towards significant difference. The correlations between nutrient digestibility, in vitro fermentation parameters and rumen microbes (relative abundance > 0.5%) were analyzed by Spearman correlation tests.
8
Survival Analysis of Clinicopathological Factors
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Our study consolidated the descriptive characteristics of the training and validation cohorts, respectively. χ2 test or Fisher’s exact test was used to confirm whether significant differences exist in the demographic and clinicopathological features between the training and validation cohorts. The variables were analysed using Kaplan-Meier survival curves and log-rank tests to evaluate their effects on OS. The ROC-AUC calculation was performed by the function of ‘ROC curve’ in SPSS V.26.0. All p values are two-sided, and p values under 0.05 are considered as statistically significant. The SEER data were extracted using SEERStat V.8.4.0, and statistical analyses were performed using SPSS V.26.0.
9
Engagement, Goal Orientation, and Academic Self-Concept
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Descriptive statistics were used to characterize the social and demographic characteristics of the sample and target variables. Correlations between engagement, (task) goal orientation, and academic self-concept were calculated with IBM SPSS v26.0. Finally, the SPSS v26.0’s MACRO tool was used to carry out mediation analyses by bootstrapping (10,000 runs). For all operations, a p ≤ 0.05 level of significance was adopted, with a 95% confidence level.
10
Statistical Analysis of Biological Replicates
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Experiments were performed in three biological replicates unless otherwise indicated, and statistically analyzed using SPSS v26.0 (IBM-SPSS, IL, USA) and GraphPad v8.0 (San Diego, CA, USA) software. Significance was assessed using the Student's t-test and one-way analysis of variance with the Tukey's Honest Significant Difference. Before conducting multiple comparisons, the normality of the data and the homogeneity of variance have been confirmed using SPSS v26.0 software (IBM-SPSS). The correlation coefficient was calculated using Pearson’s test.
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