All values were expressed as means ± standard deviation (SD). Statistical analysis was performed using Statistical Analysis Software (SPSS Inc., Chicago, IL) (SPSS 19.0). The data were analyzed by one-way ANOVA followed by the Dunnett’t test, and the differences were considered statistically significant when P < 0.05.
Statistical analysis software
Manufactured by IBM
Sourced in United States
The statistical analysis software is a specialized tool designed to assist users in conducting various statistical analyses. It provides a comprehensive set of features and functionalities for data manipulation, visualization, and statistical modeling. The software supports a wide range of statistical techniques, including regression analysis, hypothesis testing, and time series analysis, among others. It is optimized for efficiency and accuracy in processing large datasets and generating reliable statistical insights.
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Lab products found in correlation
11 protocols using statistical analysis software
1
Comparative Biochemical Analysis of Treatments
2
Quantitative Data Analysis Protocol
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Measurements were performed in triplicate and results are expressed as a means ± SD. SDs in fold change were calculated using the propagation of error formula. Data were obtained from at least three independent experiments. Analysis of variance (ANOVA) for multiple comparisons was carried out using statistical analysis software (SPSS, Chicago, IL, USA). In all cases, values of P below 0.05 were considered to indicate significant differences [28 (link)].
3
Occlusion Influence on Exercise Velocity
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All variables were expressed as mean and SD. Furthermore, all the dependent variables fulfilled the assumption of normality (i.e., Shapiro-Wilk test, p > 0.05). To analyse the influence of degrees of occlusion on execution velocity, an analysis of variance of repeated measures (RM ANOVA [5 (link)]) was performed. The degrees of occlusion had a total of 5 levels (i.e., CON, 40%, 60%, 80%, and 100%). When the sphericity assumption was not fulfilled, the degrees of freedom were corrected using the Greenhouse–Geisser approximation. The Bonferroni post hoc test was performed to analyse comparisons for each of the levels. This analysis was performed for BP and SQ exercises. The effect size (ES) was expressed using Cohen’s d. The level of significance was established at p < 0.05. All analyses were performed using statistical analysis software (SPSS Inc, Chicago, Illinois, USA).
4
Analyzing Crop Yield and Profitability
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All data were analyzed using Statistical Analysis Software (SPSS 17.0, Inc., Chicago, IL, United States). Analysis of variance (ANOVA) was used to assess the significance of fixed effects, and means of treatments were compared using Fisher’s protected least significant difference (LSD). Year and treatment were considered fixed effects, and replication was considered a random effect. The year × treatment interaction and the main effects of year and treatment were assessed using ANOVA. The significances among treatments were presented at P < 0.05. Redundancy analysis (RDA) was conducted using the “lavaan” package (Rosseel, 2012 (link)) in R version 3.3.3 (R Foundation for Statistical Computing, Vienna, Austria, 2013). Correlation analysis based on Pearson’s correlation coefficients was performed between grain yield (GY), WUE, and net income (NI) and climatic and environmental factors.
5
Longitudinal Analysis of VPA Treatment
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All parameters obtained prior to VPA treatment and at 1 month, 3 months, 6 months, 9 months, and 12 months after treatment initiation were compared using the Wilcoxon signed-rank test with Bonferroni correction. We utilized a Bonferroni adjustment for multiple comparisons, after which P-values ≤0.01 were considered as statistically significant. All statistical analyses were performed using the statistical analysis software (SPSS Inc., Chicago, IL, USA).
6
Statistical Analysis of Experimental Data
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The experiments were repeated three times and the mean values were analyzed by a two-tailed unpaired t-test. The results were expressed as mean ± SD. All statistical tests were performed with statistical analysis software (SPSS, Chicago, IL, USA). The level of p < 0.05 was considered to be statistically significant.
7
Evaluation of Experimental Replicates
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The results are the average of three repeated experiments, except where otherwise specified. Data are expressed as the mean values ± standard errors and were analyzed via statistical analysis software (SPSS Inc., Chicago, IL, USA). Analysis of variance was performed between means to determine the significant differences by using Duncan’s multiple range test (p < 0.05).
8
Mouse Behavior Statistical Analysis
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All results are expressed as the mean ± SEM. The values were processed for statistical analyses by ANOVA followed by t-test assuming unequal variances using statistical analysis software (SPSS, Chicago, IL, USA). Differences were considered statistically significant at P < 0.05. The number of animals is indicated in the figure legends, with a minimum of five mice per group.
9
Ototoxicity Knowledge Among Universities
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Data were entered into Microsoft Excel® spreadsheets and exported to IBM Statistical Analysis Software® (SAS) for analysis. Prior to analysis, universities were de-identified and recoded as ‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘H’, ‘I’, ‘J’, ‘K’, ‘L’ and ‘M’.
Frequencies and percentages were calculated for all variables, including knowledge in relation to pharmacotherapy-induced ototoxicity. This encompassed general knowledge, prevention, ototoxicity monitoring and the prescription of ototoxic drugs.
Fisher’s exact test was used to test for differences in response and the Chi-square test was used to test for associations in responses between the different universities. Statistical significance was set at p ≤ 0.05.
Frequencies and percentages were calculated for all variables, including knowledge in relation to pharmacotherapy-induced ototoxicity. This encompassed general knowledge, prevention, ototoxicity monitoring and the prescription of ototoxic drugs.
Fisher’s exact test was used to test for differences in response and the Chi-square test was used to test for associations in responses between the different universities. Statistical significance was set at p ≤ 0.05.
10
Heat Stress Response Metabolite-Gene Interactions
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Using spss software (version 26; Statistical analysis software; International Business Machines Corporation: Norman H. Nie, C. Hadlai (Tex) Hull and Dale H. Bent, CA, USA, 1968.), Pearson correlation analysis was conducted between DEGs and DAMs. Then, DEGs and DAMs in the same group were mapped onto the KEGG pathway database. Cytoscape software (version 3.8.0; The Cytoscape Consortium: National Institute of General Medical Sciences: NY, USA, 2003) was used to analyze the interaction network of metabolites and genes associated with the heat stress response.
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