Experimental data were tested for normality using the Kolmogorov–Smirnov test and found to be normally distributed. Data were analyzed with the general linear model (GLM) ‒ general factorial ANOVA procedure using cereal type (maize and wheat) and PFA inclusion level (i.e. 0, 100 and 150 mg/kg diet) as fixed factors. Statistically significant effects were further analyzed and means were compared using Tukey's honestly significant difference multiple comparison procedure. Statistical significance was determined at P ≤ 0.05. All statistical analyses were done using the SPSS for Windows Statistical Package Program (SPSS Inc., Chicago, IL).
Spss for windows statistical package program
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SPSS for Windows is a statistical software package that provides tools for data analysis, data management, and data visualization. It is designed to help users analyze and interpret data effectively. The software offers a wide range of statistical techniques, including regression analysis, factor analysis, and cluster analysis, among others. SPSS for Windows is widely used in various fields, including social sciences, healthcare, and business, to support data-driven decision-making.
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8 protocols using spss for windows statistical package program
1
Cereal-based Diet Effects on Performance
2
Factorial Analysis of Dietary Effects
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Experimental data were tested for normality using the Kolmogorov-Smirnov test and found to be normally distributed. Data were analyzed with the general linear model (GLM )—general factorial ANOVA procedure using diet type (L, H) and PFA addition (− and +) as fixed factors. Statistically significant effects were further analyzed and means were compared using Tukey's honestly significant difference multiple comparison procedure. Statistical significance was determined at P ≤ 0.05. All statistical analyses were done using the SPSS for Windows Statistical Package Program (SPSS 17.0, Inc., Chicago, IL).
3
Dietary YAWP Effects on Experimental Data
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The experimental data per treatment (WO, WA, WB and WC) were analyzed by using the general linear model (GLM)–ANOVA procedure of the SPSS for Windows statistical package program, version 27 (SPSS 17.0, Inc., Chicago, IL, USA). The statistically significant effects were further analyzed, and means were compared using Tukey’s honestly significant difference (HSD) multiple-comparison procedure. The linear and quadratic effects of dietary YAWP inclusion level were determined by using polynomial contrasts. Statistical significance was determined at p ≤ 0.05.
4
Evaluating Dietary Effects with ANOVA
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Experimental data were tested for normality using the Kolmogorov–Smirnov test and found to be normally distributed. Data were analyzed with the general linear model (GLM)–general factorial ANOVA procedure using diet type (L, H) and PFA addition (NO and YES) as fixed factors. Statistically significant effects were further analyzed, and means were compared using Tukey’s honestly significant difference multiple comparison procedure. Statistical significance was determined at p ≤ 0.05. All statistical analyses were performed using the SPSS for Windows Statistical Package Program (SPSS 17.0, Inc., Chicago, IL, USA).
5
Broiler Gut Microbiome and Metabolism
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Experimental data on luminal- and mucosa-associated microbiota, microbial glycolytic enzyme activity, VFA and the relative quantification of genes of interest were based on individual broilers. All data were tested for normality using the Kolmogorov–Smirnov test and found to be normally distributed. Subsequently, data were analyzed with the general linear model (GLM) – general factorial ANOVA procedure using OABF (No/Yes) and avilamycin (No/Yes) as fixed factors. Probability values of equal or less than 0.05 (P ≤ 0.05) were considered significant. Statistical significant effects were further analyzed and treatment means were compared using Tukey HSD test using the SPSS for Windows statistical package program, version 8.0.0 (SPSS, Chicago, IL, USA).
6
Effect of Dietary Polyphenol Inclusion on Layer Performance and Ovarian Gene Expression
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Experimental data on layer performance were based on a cage basis, whereas ovarian gene expressions were based on individual layers. All data were initially checked for normality and subsequently analyzed with the general linear model (GLM )–ANOVA procedure using the SPSS for Windows statistical package program, version 27 (SPSS Inc., Chicago, IL). Statistically significant effects were further analyzed, and means were compared using Tukey's honest significant difference (HSD ) multiple comparison procedure. Statistical significance was determined at P ≤ 0.05. Linear (lin ) and quadratic (quad ) response patterns to dietary PP inclusion level were studied using polynomial contrasts.
7
Statistical Analysis of Biological Data
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The experimental results are reported as means ± the standard error of means (SEMs) of at least two biological replicates. All data were tested for normality using the Kolmogorov–Smirnov test and transformed in logarithmic or normalized form [49 (link)] where necessary until the data were normally distributed. Subsequently all data generated were compared using one-way ANOVA followed by Duncan’s post hoc test. Differences between means were considered significant at p < 0.05. The statistical analysis was performed using the SPSS for Windows statistical package program, version 22.0.0. Graphs were generated using the GraphPad Prism 8 program.
8
Broiler Growth and Gene Expression
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Growth performance responses were analyzed on a pen basis and relative gene expression data evaluation was based on individual broilers. Data were analyzed with t test procedure. Statistical significance was determined at P < 0.05. All statistical analyses were done using the SPSS for Windows Statistical Package Program (SPSS Inc., Chicago, IL).
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