Bio400FinalPresentation

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Biology 400 Bentou McNeil 11/26/2023
A table or correlogram Coefficient Std. Error t-Value Pr(>|t|) Intercepts 1.208 .222 5.439 1.99e-.06 Proteins 0.019 .005 3.425 .0013 Residual standard error: 0.06428 on 46 degrees of freedom Multiple R-squared: 0.2032, Adjusted R-squared: 0.1859 F-statistic: 11.73 on 1 and 46 DF, p-value: 0.001303
A table for mean, CV, variance, standard deviation, range, and standard error
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Boxplots and Histograms to visualize the numerical variables. Boxplots Histograms
Correlogram for the correlation on all variables.
Heatmap using the dataset
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Q-Q plots to show the correlation between the theoretical normal distribution on protein and oil, respectively
Pearson's correlation coefficient between variables • The correlation coefficient between Protein and Oil is 0.597. This positive correlation suggests a moderate and statistically significant linear association between the two variables. The blue background is used for visual emphasis. The strength of the correlation, falling between 0.5 and 0.7, indicates a moderate positive relationship. As Protein levels increase, there is a tendency for Oil levels to also increase. It's crucial to consider the context of the study and potential contributing factors to better understand the nature of this correlation.
Comparing protein content
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ANOVA table from two-way ANOVA analysis • In summary, the table provides information on the significance of the Line, Year, and Interaction effects on Protein content. The significant p-values for Line and Line:Year interactions suggest that these factors play a role in explaining the variability in Protein content. The non- significant p-value for Year indicates that this factor may not have a significant impact.
Graphs to display the distribution of residuals of the model
A table results of PCA and shows a graph for PC1 and PC2 and results Recult Table Graph of PC1 and PC2
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Conclusion of the analysis • There is a significant relationship between protein content and amino acid content based on a low p-value from the analysis.
References • Bernal-Vasquez, A., Utz, H. -., & Piepho, H. (2016). Outlier detection methods for generalized lattices: A case study on the transition from ANOVA to REML. Theoretical and Applied Genetics, 129 (4), 787-804. https://doi.org/10.1007/s00122-016-2666-6 • Cheung, C. H. Y., Khaw, M. L., Tam, V. C. W., Ying, M. T. C., & Lee, S. W. Y. (2020). Performance evaluation of a portable bioimpedance cardiac output monitor for measuring hemodynamic changes in athletes during a head-up tilt test. Journal of Applied Physiology (1985), 128 (5), 1146-1152. https://doi.org/10.1152/japplphysiol.00822.2019
Acknowledgement • Dr. Yuan • Classmates of bio 400 fall 2023 FSU • Lab assistance in the library and computer lab
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