The results were expressed as mean ± standard deviation (SD) of the mean and statistically analyzed by one-way analysis of variance (ANOVA) using SPSS Statistics Package 25 (Chicago, IL, USA) and differences between experimental groups were considered significant at P < 0.05.
Spss statistics package 25
Manufactured by IBM
Sourced in United States
SPSS Statistics package 25.0 is a comprehensive software solution for statistical analysis. It provides a wide range of analytical tools and techniques to help researchers, analysts, and decision-makers gain insights from data. The software offers a user-friendly interface and a variety of statistical methods, including regression analysis, hypothesis testing, and data visualization.
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Lab products found in correlation
14 protocols using spss statistics package 25
1
Statistical Analysis of Experimental Data
2
SPSS Statistical Analysis of Binding Data
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The data were analyzed statistically using the SPSS statistics package 25 (SPSS Inc. Chicago, USA). One-way analysis of variance (ANOVA) and LSD post hoc test were used to analyze the binding data, with P ≤ 0.05 between groups considered significant.
3
Impacts of Dietary Treatments on Growth and Microbiome
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Performance parameters related to growth were analyzed using ANCOVA. The parameters related to RT and organ weights and lengths were analyzed using a general linear model (GLM) procedure for the one-way ANOVA, followed by Duncan’s multiple range test. Planned contrasts were performed to compare 0% NT vs. 0% HS; 0% NT vs. 0.1% HS; 0% NT vs. 0.4% HS; 0% HS vs. 0.1% HS; 0% HS vs. 0.4% HS; 0% HS vs. 0.1% HS, and 0.4% HS; and 0% NT vs. 0% HS, 0.1% HS, and 0.4% HS. Microbial analyses, such as alpha-diversity and taxonomy (phylum and genus), were performed using the Kruskal–Wallis test and adjusted with Bonferroni correction. Differences were considered statistically significant at p < 0.05, except where otherwise stated. The beta diversity analysis is presented as a principal coordinate analysis (PCoA) based on unweighted and weighted Unifrac differences. PERMANOVA was used to evaluate the significant differences among treatments in the beta diversity. Spearman’s correlation was used to identify the correlations among bacterial phyla. All the data are expressed as means ± standard error of the mean (SEM). Analysis was performed using IBM SPSS Statistics package 25.0 (IBM software, Chicago, IL, USA) and SAS (SAS Institute Inc., Cary, NC, USA) software. Graphs were drawn using Prism-GraphPad (Graphpad Software, San Diego, CA, USA) software.
4
Comprehensive Statistical Analysis of Microbiome
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The data relating to animal studies such as growth performances, RT, absolute and relative organ weights were analyzed using one-way ANOVA, followed by Duncan’s multiple range test. Alpha diversity (community richness and diversity) and taxonomic analysis (phylum and genus) were done using the Kruskal–Wallis test and adjusted with Bonferroni correction. Differences were considered statistically significant at p < 0.05 unless otherwise stated. All the data were expressed as mean ± standard error of the mean (SEM). The beta diversity analysis includes principal coordinate analysis (PCoA), calculated by weighted Unifrac and unweighted Unifrac. Spearman’s correlation was used to evaluate the correlation among the bacteria of the phylum. Analysis was conducted using IBM SPSS Statistics package 25.0 (IBM software, Chicago, IL, USA). Graph pad Prism software was used to draw the graphs.
5
Cerebellar Connectivity and ASD Symptoms
6
Influence of Temperature and GABA on Hatchability
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Data were analyzed using an IBM SPSS Statistics package 25.0 (IBM software, Chicago, IL, USA). Percent hatchability was calculated based on the number of chicks hatched to the fertile eggs following the previous studies (9 (link)). Each bird acted as an experimental unit for BW at hatch. Eight birds from each group were used to collect the organ weight. All the data were expressed as mean ± standard error of the mean (SEM). Shapiro Wilk and Levene's-tests were used to assess the normality of distribution and equality of variances. A two-way ANOVA was used taking temperature and GABA as the main effect. A Duncan's multiple range test was used if an interaction existed. Parametric differences were considered statistically significant at p < 0.05.
7
Transcriptomic Analysis of Immune Response
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The fold changes of gene expression were analysed using ClustVis [32] for visualisation of a heatmap and for Principal Component Analysis (PCA). The arbitrary units of each gene were scaled and log2 transformed to improve the normality of real-time quantitative PCR measurements before statistical analysis as described previously [31] . A paired-samples T test was used to determine the level of difference between the treatments at each time point using the IBM SPSS Statistics package 25.0 (SPSS Inc., Chicago, Illinois), with differences considered significant at p < 0.05. The Spearman's rho correlation analysis of gene expression was performed on selected pro-/anti-inflammatory cytokines, adaptive cytokines, IL-12 family and chemokine receptors. The four time points from each bacterin stimulation was combined for this analysis (n=16).
8
Diagnostic Accuracy of Preoperative Ultrasound
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Statistical analysis was performed using IBM SPSS Statistics 25 package (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY, USA: IBM Corp). Descriptive statistics methods were used to systemize the study data. The accuracy, sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV) of the ultrasound examination were evaluated, and the estimates of these characteristics were given along with their 95% confidence intervals (CIs). The chi-square test for independence (homogeneity) was used to compare the effects of clinicopathological factors and the correlation of outcomes from PAUS and SLNB pathology. The Student t test was used to compare continuous data with normal distribution. The observed differences and associations were considered statistically significant if p < 0.05.
9
Pandemic's Impact on Mental Health
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Statistical analysis was conducted using the IBM SPSS Statistics 25 Package (IBM, Armonk, NY, USA). Non-parametric tests were used due to the non-normal distribution of the variables and because the groups had different numbers of participants. At the beginning, insomnia, depression and alcohol consumption were compared between the two analyzed time periods. Further, we assessed the correlation of factors, such as gender, age, education, living alone vs. with someone, employment status, income, and individuals’ and their relatives’ COVID-19 diagnosis, with insomnia, depression and alcohol consumption increase. The Wilcoxon test was used to check whether there are statistically significant differences between the two time periods for the analyzed variables. The analysis of the Spearman correlation allowed us to check the presence of a statistically significant relationship between the studied variables. Then, a multinomial logistic regression was performed for factors significantly correlated with the increase in depression and insomnia symptoms, and higher alcohol consumption. Significant outcomes from the logistic regression are presented in the results section.
10
Analysis of HCV Infection and Liver Enzymes
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Statistical analysis was conducted using the IBM SPSS Statistics 25 Package. The chi-squared test was used to check whether there is a statistically significant relationship between the nominal variables, e.g., between a group of people and the incidence of HCV infection. To determine the level of this relationship, the Cramér’s V contingency coefficient was used. Two-way ANOVA was used to examine a group of people from the viewpoint of statistically significant influence on the level of liver enzymes, as well as its interaction with HCV infection. Simple main effects analysis was used to examine in detail a statistically significant interaction in the analysis of variance. Logistic regression analysis made it possible to prove whether the risk of HCV infection in the studied group could be predicted. The statistically significant level was p < 0.05.
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