Sausages with different modified casings were discriminated by canonical discriminant analysis according to the colour parameters (Statistics 26, IBM, Armonk, NY, USA). One-way ANOVA was used for analysing the effects of different casing treatments on the core colour of the sausages (Statistics 26, IBM, Armonk, NY, USA).
Statistics 26
Manufactured by IBM
Sourced in United States
Statistics 26.0 is a comprehensive software package designed for statistical analysis. It provides a wide range of statistical tools and techniques for data processing, modeling, and visualization. The software is capable of handling large datasets and offers advanced features for complex statistical analysis.
Automatically generated - may contain errors
Lab products found in correlation
56 protocols using statistics 26
1
Sausage Casing Color Analysis
2
Metabolomic and Transcriptomic Analysis of Acylcarnitine
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The experimental data of acylcarnitine (ten samples in each group) were expressed as the Mean ± standard error of Mean (`Mean ± SEM) and analyzed with SPSS Statistics 26.0 software, using Student-t test (two-side). GraphPad Prism5 software was used for statistical analysis and bar plot across all staining counts data. Differences between the two groups were considered to be significant when the p-value < 0.05. The relationship between differential metabolites and differentially expressed genes. Cytoscape v3.9.0 was used to plot the network diagram for the selected parts with a p-value < 0.001. The correlation analysis between DEGs and DEMs was evaluated by the cor function in the R package and the network plot was performed with Cytoscape v3.9.0. The analysis of metabolomics and transcriptomics data, which was performed with four independent samples in each group (transcriptomics control group only with three groups) were generated with MetaboAnalyst 5.0 and OmicStudio (https://www.omicstudio.cn ), showing with the PCA plots, volcano plots, advanced heatmap plots, and enrichment plots. The significant enriched biological pathways performed for DEMs and DEGs were screened with relative counts number, enrich factors and p-value of the selected enriched pathways. Information in detail about OmicStudio was shown elsewhere (44 (link), 45 (link)).
3
Statistical Analysis of Intervention Outcomes
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Data were analyzed by SPSS Statistics 26.0 software and expressed as mean ± standard deviation (±SD). Each index was compared before and after the intervention using paired samples t-test, and between groups using independent samples t-test and one-way ANOVA, with significance level defined as P < 0.05 and very significant level defined as P < 0.01.
4
Statistical Analysis of Textural Data
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SPSS Statistics 26.0 software was used to analyze the data, and one-way analysis of variance (ANOVA) was used to determine the significant difference between each test (p < 0.05). Matlab 2016 software was used to analyze and normalize the textural data statistically and to perform a regression analysis as well as Pearson correlation analysis. The experimental data were processed using Origin 2019 software, and the results were expressed in means ± standard deviation. All experiments were conducted in triplicate.
5
Statistical Analysis of Experimental Data
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Statistical analyses were conducted in SPSS Statistics 26.0. All data shown were determined for three independent experiments unless otherwise stated and presented as the mean ± S.D., * p < 0.05, ** p < 0.01, *** p < 0.001.
6
Statistical Analysis of Experimental Data
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All statistical analyses were performed using SPSS Statistics 26.0. The results of experiments were expressed as the mean ± standard deviation (SD, n = 3). Statistical comparisons were performed by one-way or two-way analysis of variance (ANOVA) for multiple groups.
7
Statistical Analysis of Experimental Data
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One-way analysis of variance (ANOVA) was carried out and means were separated with Duncan’s LSD at 95% using SPSS Statistics 26.0. All figures were generated using the OriginLab software (version 9.0).
8
Statistical Analysis of Research Protocols
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Statistical analysis was performed using SPSS Statistics 26.0. Differences were considered significant at P < 0.05 and extremely significant at P < 0.01.
9
Statistical Analysis of Experimental Data
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The data were processed and analyzed with SPSS Statistics 26.0. In the descriptive statistics, count data were described with frequencies and proportions, and measurement data were expressed using the mean and standard deviation or median (interquartile range [IQR]) depending on the normality of the measurement data, which was evaluated by the kurtosis coefficient and skewness coefficient. Count data were analyzed using the Mann-Whitney U test, while the measurement data were analyzed using Fisher’s exact test. Variables demonstrating statistical significance were included in multivariate logistic regression analyses (P < 0.05). The missing values were dropped from the analysis.
10
Acupoint Clustering Analysis
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The high frequency acupoints were subjected to cluster analysis using SPSS Statistics 26.0, and the clustering relationship between the acupoints was examined by drawing a cluster analysis tree.
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