Statistical regression was used to test the relationship between water DN and sediment TN at each sampling location. No significant relationships were observed in both the two sampling periods. Dissolved nitrogen at each location was also not significantly correlated to sediment δ15N. A two-factor analysis of variance (ANOVA) was used to make δ15N values statistical analysis, with sampling year and tributaries being the main factors. For each analysis, overall F-tests with an alpha <0.05 were used. All statistical analyses were performed on IBM SPSS Statistic software version 21.0 [23 (link), 24 (link)].
Spss statistic software version 21
Manufactured by IBM
Sourced in United States
SPSS Statistics software, version 21, is a comprehensive statistical analysis tool used for data management, analysis, and presentation. It provides a wide range of statistical techniques, including descriptive statistics, regression analysis, and multivariate methods, to help users gain insights from their data.
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Lab products found in correlation
8 protocols using spss statistic software version 21
1
Sediment Nitrogen Dynamics in Tributaries
2
Hormones and Running Performance Analysis
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The statistical analysis was carried out with IBM SPSS Statistic software version 21.0 (IBM Co., Armonk, NY, USA). The results are expressed as x ± s, where x is the mean values and s is the standard deviation. All variables used in the study were checked for normality of distribution before the analyses (Kolmogorov–Smirnov tests). The data were analyzed by repeated measurements analysis of variance (ANOVA) with the Bonferroni post hoc test for moment/period as the categorical variable. Partial eta squared (ηp2) was used as an effect size measure of ANOVA. Threshold values for assessing magnitudes of standardized effects were ηp2 ≥ 0.01, ηp2 ≥ 0.06 and ηp2 ≥ 0.14 for small, medium and large, respectively [31 ]. The equality of variances between the differences was assessed with Mauchly’s test of sphericity. When sphericity was violated, Greenhouse–Geisser corrected p-values were used. Simple linear regression analysis was conducted to examine the associations between hormones and km trained per week. A p ≤ 0.05 was considered statistically significant.
3
Statistical Analysis of Growth and Yield
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Data were performed using SPSS STATISTIC Software Version 21.0 (IBM 2015) for Windows. The growth and yield data were analyzed separately for each experiment and means were compared using student's T-test, and differences were compared at Tukey (P ≤ 0.05).
4
Evaluating Tumor Response and Survival Outcomes
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Statistical analysis was performed using the Kendall coefficient of concordance (k) to assess the level of agreement between pathologists. Results with this test can range from −1, corresponding to complete disagreement to +1 for total agreement; 0 indicates the absence of correlation. All TRG scores were correlated with Overall Survival (OS), OS from Surgery (OSS), Progression-Free Survival (PFS) and Disease-Free Survival (DFS). OS was defined as the time from the first day of chemotherapy until the day of death from any cause, PFS was defined as the time from the first day of chemotherapy until the day of disease progression or death from any cause. Post-surgical OS (OSS) was defined as the time from surgery until the day of death, DFS was defined as the time from surgery until the day of disease progression or death from any cause. Patients alive at the time of analyses were censored at the date of their last follow-up visit, whereas those without disease progression were censored at the time of the last radiologic assessment. Survival analyses were performed using the Kaplan-Meyer method and differences in survival were compared using the log-rank test, setting statistical significance at p < 0.05 for a two-sided test. Statistical and survival analyses were performed using IBM SPSS statistic software, version 21.
5
Statistical Analysis of Research Data
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Statistical analysis was carried out using IBM SPSS Statistic Software Version 21. A p‐value < .05 was considered statistically significant.
6
Comparative Analysis of Experimental Outcomes
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The data obtained from the randomized trials (triplicate tests with threefold determination) were compared by Analysis of Variances (ANOVA) using IBM SPSS statistic software, version 21. The post hoc test chosen was the Bonferroni test. Statistical evaluations were based on a 95% confidence interval (α = 0.05).
7
Comparative Analysis of Transgene Expression
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The normality test, Shapiro-Wilk, was carried out to ensure the normal distribution of all data. Mean comparison between ies expressing transgene and their controls was performed using independent sample t-test. Comparative analysis between different transgenes was done using one-way analysis of variance followed by Tukey HSD posthoc test for pairwise group comparison.
All statistical analyses were conducted by IBM SPSS statistic software version 21 (SPSS, Chicago, IL, US). The minimum level of signi cance was set at p-value of 0.05.
All statistical analyses were conducted by IBM SPSS statistic software version 21 (SPSS, Chicago, IL, US). The minimum level of signi cance was set at p-value of 0.05.
8
Stress-Induced Changes in Sensory Perception
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Data were analyzed using IBM SPSS statistic software version 21. The sample size was estimated a priori based on our previous experience with the psychophysical testing and on the expected difference between baseline and stress condition in the mean values of the main outcome measures: pain threshold and CPM. For > = 0.05 and statistical power of 80%, the calculation yielded a sample size of 18 subjects and for > = 0.01, 24 subjects. Continuous variables were described as means T SD and categorical variables as counts and %. All data underwent the Kolmogorov-Smirnov analysis for normality of distribution. Parametric and nonparametric analyses of variance with corrected post hoc tests were used to evaluate the effect of condition (baseline, prestress, stress, and recovery) on perceived stress, perceived anxiety, HR, HRV, GSR, cortisol, and the sensory tests (pain threshold, pain tolerance, CPM, and TSP). The magnitude of change in the sensory indices after the stress manipulation was calculated by subtracting the level measured during stress from that measured at baseline. The correlations between changes in the sensory indices (delta values) and between the stress and the demographic indices were calculated using Pearson"s correlation coefficients. P G 0.05 was considered significant.
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