In order to test if swimming performance indices and metabolic rates differed between asymmetry morphs while controlling for body and caudal peduncle size, we ran separate ANCOVAs with each performance index or metabolic rate as the dependent, asymmetry morph as a fixed factor and SL and CAres as covariates. There was no remaining correlation between size‐standardized CAres and SL by definition since CAres were saved residuals from a regression with SL, and residual plots of CAres against SL indicated no change in variation with increasing SL. ANCOVAs were run initially with all main effects and higher‐level interaction terms included. In order to select the best model to explain each analysis, we only included interaction terms if they were significant; if interaction terms were nonsignificant, they were removed and the ANCOVA model re‐run with only main effects and significant interaction terms (where present) included. The ANCOVA for prolonged swimming performance also included water velocity as a covariate, and the ANCOVA for metabolic rate included sample location as a random factor. All data were analyzed using SPSS Statistics 22.0 program (© IBM Corporation).
Spss statistics 22.0 program
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SPSS Statistics 22.0 is a data analysis software program developed by IBM. It provides advanced statistical analysis capabilities, including data manipulation, visualization, and modeling tools. The program is designed to help users gain insights from data and make informed decisions.
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Lab products found in correlation
2 protocols using spss statistics 22.0 program
1
Analyzing Morphological Asymmetry and Swimming Performance
2
Orthostatic Hypotension Risk Factors
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The distribution of the data was examined with the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard deviation; nonnormally distributed data are presented as median with interquartile range; and categorical variables are presented as frequency and percentages (%). To compare the differences among groups, an independent samples t test, one-way ANOVA, and Kruskal-Wallis tests were used for continuous variables, while Pearson χ 2 analysis was used for categorical variables. Correlations between parameters were analyzed with Pearson and Spearman correlation tests. Binary logistic regression analyses were performed with diastolic OH (DOH), systolic OH (SOH), and OH as dependent variables to identify and adjust for multiple factors associated with them. Multiple linear regression analyses were performed with changes in SBP and DBP as dependent variables to identify and adjust for multiple factors associated with them. All statistical analyses were done with IBM SPSS statistics 22.0 program (SPSS, Inc., Chicago, IL, USA) and were analyzed and reported at α = 0.05 significance level, and all p values were two-tailed.
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