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- https://bio.libretexts.org/Courses/Evergreen_Valley_College/BIO_Majors_4A_4B_Lab_Manual_2023/36%3A_Appendix_B_-_Brief_Introduction_to_Statistics/36.02%3A_Reading_-_Statistical_AnalysisWith this dihybrid cross example, we expect a ratio of 9:3:3:1 in phenotypes where 1/16th of the population are recessive for both texture and color while 916 of the population display bo...With this dihybrid cross example, we expect a ratio of 9:3:3:1 in phenotypes where 1/16th of the population are recessive for both texture and color while 916 of the population display both color and texture as the dominant. 316 will be dominant for one phenotype while recessive for the other and the remaining 316 will be the opposite combination.
- https://bio.libretexts.org/Courses/Gettysburg_College/01%3A_Ecology_for_All/01%3A_Introduction_to_Ecology/1.05%3A_An_Overview_of_Essential_Mathematics_Used_in_Science/1.5.03%3A_Testing_hypotheses--Inferential_statisticsThis section reviews inferential statistics are, the difference between scientific and statistical hypotheses, and how conclusions are made with data at hand.
- https://bio.libretexts.org/Bookshelves/Agriculture_and_Horticulture/Quantitative_Methods_for_Plant_Breeding_(Suza_and_Lamkey)/01%3A_Chapters/1.07%3A_Linear_Correlation_Regression_and_PredictionThis page explores the relationship between continuous variables through correlation and regression analysis, including the use of Pearson Correlation Coefficient and scatter plots for visualization. ...This page explores the relationship between continuous variables through correlation and regression analysis, including the use of Pearson Correlation Coefficient and scatter plots for visualization. It discusses methods to create regression lines, interpret coefficients, and assess statistical significance through techniques like ANOVA and F-tests. The importance of recognizing variability, calculating confidence limits, and addressing outliers is emphasized.
- https://bio.libretexts.org/Workbench/General_Ecology_Ecology/Chapter_1%3A_Introduction_to_Ecology/1.6%3A_Testing_Hypotheses_-_Inferential_StatisticsThis section reviews inferential statistics are, the difference between scientific and statistical hypotheses, and how conclusions are made with data at hand.
- https://bio.libretexts.org/Bookshelves/Agriculture_and_Horticulture/Quantitative_Methods_for_Plant_Breeding_(Suza_and_Lamkey)/01%3A_Chapters/1.15%3A_Multivariate_AnalysisThis page covers multivariate data analysis, emphasizing grouping, variable interactions, similarity measures, and dataset simplification using R. It discusses dissimilarity coefficients for categoric...This page covers multivariate data analysis, emphasizing grouping, variable interactions, similarity measures, and dataset simplification using R. It discusses dissimilarity coefficients for categorical and quantitative data, data cleaning for statistical analysis, and clustering methods like k-means and hierarchical clustering. Principal Component Analysis (PCA) is highlighted for dimensionality reduction, with over 96% variance explained by three components.
- https://bio.libretexts.org/Bookshelves/Agriculture_and_Horticulture/Quantitative_Methods_for_Plant_Breeding_(Suza_and_Lamkey)/01%3A_Chapters/1.01%3A_Basic_PrinciplesThis page emphasizes the critical role of experimentation in agricultural research, particularly in seed science and plant breeding, outlining the scientific method and the importance of replication, ...This page emphasizes the critical role of experimentation in agricultural research, particularly in seed science and plant breeding, outlining the scientific method and the importance of replication, randomization, and controlled design to enhance data accuracy. It discusses statistical measures for analyzing crop data, proper reporting of measurements, and the significance of using consistent units.
- https://bio.libretexts.org/Bookshelves/Agriculture_and_Horticulture/Quantitative_Methods_for_Plant_Breeding_(Suza_and_Lamkey)/01%3A_Chapters/1.02%3A_Distributions_and_ProbabilityThis page covers the scientific method and experimental design principles, emphasizing the importance of proper sampling and data representation. It explains data visualization with histograms, introd...This page covers the scientific method and experimental design principles, emphasizing the importance of proper sampling and data representation. It explains data visualization with histograms, introduces statistical concepts such as quartiles, and discusses probability measures and distributions. The normal distribution is highlighted for its properties and use in probability calculations, including Z-scores.
- https://bio.libretexts.org/Bookshelves/Agriculture_and_Horticulture/Quantitative_Methods_for_Plant_Breeding_(Suza_and_Lamkey)/01%3A_Chapters/1.08%3A_The_Analysis_of_Variance_(ANOVA)This page introduces ANOVA for comparing multiple treatments, emphasizing its advantages over the t-test. It covers constructing ANOVA tables, calculating sums and mean squares, and determining the F-...This page introduces ANOVA for comparing multiple treatments, emphasizing its advantages over the t-test. It covers constructing ANOVA tables, calculating sums and mean squares, and determining the F-ratio for significance testing, along with a focus on corn yield examples. Critical F-values are provided for statistical reference.
- https://bio.libretexts.org/Bookshelves/Agriculture_and_Horticulture/Quantitative_Methods_for_Plant_Breeding_(Suza_and_Lamkey)/01%3A_Chapters/1.09%3A_Two_Factor_ANOVAsThis page covers various aspects of experimental design and analysis in agricultural research, particularly focused on two-factor ANOVA. It compares factorial designs that consider interactions betwee...This page covers various aspects of experimental design and analysis in agricultural research, particularly focused on two-factor ANOVA. It compares factorial designs that consider interactions between treatment factors (like plant populations and nitrogen levels) with simpler experimental setups.
- https://bio.libretexts.org/Bookshelves/Agriculture_and_Horticulture/Quantitative_Genetics_for_Plant_Breeding_(Suza_and_Lamkey)/01%3A_New_Page/1.12%3A_Multi_Environment_Trials-_Linear_Mixed_ModelsThis page explores the limitations of general linear models in predicting outcomes from replicated trials in multi-environment contexts. It introduces mixed linear models (LMM) and discusses BLUEs and...This page explores the limitations of general linear models in predicting outcomes from replicated trials in multi-environment contexts. It introduces mixed linear models (LMM) and discusses BLUEs and BLUPs for enhancing prediction accuracy, particularly in plant breeding. C.R. Henderson's shrinkage estimators are featured for improving genotypic predictions, along with a distinction between fixed and random effects.
- https://bio.libretexts.org/Bookshelves/Agriculture_and_Horticulture/Quantitative_Methods_for_Plant_Breeding_(Suza_and_Lamkey)/01%3A_Chapters/1.12%3A_Data_TransformationThis page covers the key assumptions of ANOVA, emphasizing the importance of normality of error terms, homogeneity of variances, and additivity of effects. It discusses methods for testing variances l...This page covers the key assumptions of ANOVA, emphasizing the importance of normality of error terms, homogeneity of variances, and additivity of effects. It discusses methods for testing variances like Bartlett's Test, highlights data transformation options, and provides practical examples related to gamagrass seed treatments.
