آمار کاربردی در علوم کشاورزی، زیستی و محیط‌زیستی (کتاب‌های ASA، CSSA و SSSA)

Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors cover fundamental concepts and methodologies of experimental design and analysis, and also delve into advanced statistical topics, all explored by analyzing real agronomic data with practical and creative approaches using available software tools. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed. Capa_digital_art_x4_colored_toned_light_ai.pdf (p.1) Ch0.pdf (p.2-21) Ch1.pdf (p.22-39) CHAPTER 1: ERRORS IN STATISTICAL DECISION MAKING Statistical Decisions in Agronomic and Environmental Research Are Often Framed as Null Hypothesis Tests Effect Size The Noncentral Distribution Errors in Statistical Decision Making Example Rethinking Standard Operating Procedure: Relative Importance of α and β Errors Reasons for Lack of Significance Not Reported Complicated Experimental Designs with Multiple Possible Hypotheses Are Frequently Under-Powered Errors in Split Plot Experiments Recommendations—So What Is a Person to Do? Step-by-Step Recommendations Summary Key Learning Points Review Questions True or False: Exercises References Ch2.pdf (p.40-73) CHAPTER 2: ANALYSIS OF VARIANCE AND HYPOTHESIS TESTING The ANOVA Process History of Analysis of Variance Planning an Experiment The Linear Model Fixed and Random Effects Fixed, Mixed, and Random Models ANOVA COMPONENTS Sources of Variation Degrees of Freedom Sums of Squares Mean Squares F-values P-value Contrasts and Multiple Comparison Procedures Case Study: The Story of Statbean: From Discovery to Field Testing Introduction Research Objectives Experimental Description and Design– Randomization and Replication of Treatments Sampling Description and Design–Measuring Dependent Variables Preparing, Correcting, and Knowing the Data Descriptive Summary of the Data ANOVA by Location ANOVA by Location SAS code for PROC MIXED Residual Plots Results Planned and Multiple Pairwise Comparisons ANOVA Combined Over Fixed Locations SAS Code– PROC MIXED- pH Combined Over Locations Checking for Heterogeneity of Variance ANOVA Results– Combined over Locations Planned and Multiple Pairwise Comparison Presentation of ANOVA Results Conclusions Key Learning Points Review Questions (T/F) Exercises References Ch3.pdf (p.74-93) CHAPTER 3: BLOCKING PRINCIPLES FOR BIOLOGICAL EXPERIMENTS Randomization Blocking Concepts of Blocking Complete Block Designs Incomplete Block Designs Split-Plot Blocking Patterns Change-Over (Crossover) Designs The Special Needs of Glasshouse and Growth Chamber Experimentation Fixed vs. Random Effects To Pool or Not to Pool The Value of Retrospective Analyses Conclusions Key Learning Points Exercises References Ch4.pdf (p.94-104) CHAPTER 4: POWER AND REPLICATION—DESIGNING POWERFUL EXPERIMENTS Concepts of Replication What is an Experimental Unit? Replication on Multiple Scales Replication and Power Conclusions Key Learning Points Exercises References Ch5.pdf (p.105-125) CHAPTER 5: MULTIPLE COMPARISON PROCEDURES: THE INS AND OUTS Types of Statistical Error Traditional Hypothesis Testing Scenario How To Get The Statistical Analysis Wrong Garlic Example Alfalfa Example Ideas, Hypotheses, and Contrasts of Various Types What If There Are No Prior Ideas? Multiple Comparison Procedures Ordering of Multiple Comparison Procedures by Level of Conservatism What Is the Natural Unit? Inconsistency of Multiple Comparison Procedures Goldilocks and the Four Bears The Inconsistency of Other Multiple Comparison Procedures The Significance Level and Power of Some Well-Known Multiple Comparison Procedures Power Analysis The Practical Solution Advantages of Using the Unrestricted LSD Procedure General Contrasts and Report Writing Example of Usage of the Practical Solution in Research Conclusions Key Learning Points Exercises Acknowledgments References Ch6.pdf (p.126-195) CHAPTER 6: LINEAR REGRESSION TECHNIQUES Historical Background Some Aspects of Planning Experiments for Linear Regression The Basic Idea Linear vs. Nonlinear Regression The Simple Linear Regression The Model Statistical Inferences Results of Examples 1 to 4 Diagnostics of the Residuals The Problem of Regressand Transformation—Reconsideration of Example 4 Multiple Linear Regression Specifics of the Multiple Linear Regression Model Compared to the Simple Linear Regression Model Sequential and Partial Evaluation of Regressors Techniques for Model Selection The No-Intercept Problem in Simple and Multiple Linear Regression Extensions of the Linear Regression Models The Linear Mixed Model Example 6 Continued. Example 7 (SAS Code in Appendix) Example 3 (Modified) (SAS Code in Appendix) Example 8 (SAS Code in Appendix) Concluding Remarks Key Learning Points Review Questions Exercises QUESTIONS ACKNOWLEDGMENTS References Ch7.pdf (p.196-218) CHAPTER 7: ANALYSIS AND INTERPRETATION OF INTERACTIONS OF FIXED AND RANDOM EFFECTS Looking for the Best Model Experimental Data The Complete Model: Analyzing Main Effects and Their Two-Way, Three-Way, and Four-Way Interactions Interpreting the Year × Soil × N interaction Interpreting Year × Soil × P Interaction Interpreting Soil × N × P interaction Interpreting the Adjusted Means Mixed Models Practical Recommendations Related to the Fixed vs. Random Effects Debate Example 1 Example 2 Conclusions Key Learning Points Review Questions Acknowledgments References Ch8.pdf (p.219-252) CHAPTER 8: THE ANALYSIS OF COMBINED EXPERIMENTS A Linear Model for Qualitative Treatments in a Combined Experiment The Choice of Fixed or Random Effect in General Example: Oat Cultivar Trial Choosing Whether to Subdivide the Treatment by Environment Interaction by Environment Choosing Whether to Subdivide the Treatment by Environment Interaction by Treatment Choosing Whether or Not to Assume Equal Residual Variances for All Environments Summary Key Learning Points: Review Questions (T/F) Exercises References Ch9.pdf (p.253-295) CHAPTER 9: ANALYSIS OF COVARIANCE Abstract Introduction Description of the ANCOVA Model Summary Key Learning Points Review Questions Questions To Be Considered For Each Data Analysis Exercise Acknowledgments References Ch10.pdf (p.296-314) CHAPTER 10: ANALYSIS OF REPEATED MEASURES FOR THE BIOLOGICAL AND AGRICULTURAL SCIENCES Abstract Linear Mixed Models Variance–Covariance Structures Conclusions Key Learning Points Review Questions Exercises References Ch11.pdf (p.315-333) CHAPTER 11: THE DESIGN AND ANALYSIS OF LONG-TERM ROTATION EXPERIMENTS Analysis Summary Acknowledgments Key Learning Points Review Questions Exercises References Ch12.pdf (p.334-359) CHAPTER 12: SPATIAL ANALYSIS OF FIELD EXPERIMENTS Practical Considerations Experimental Design Spatial Model Example 1 Summary Key Learning Points Review Questions Exercises References Ch13.pdf (p.360-384) CHAPTER 13: AUGMENTED DESIGNS-EXPERIMENTAL DESIGNS IN WHICH ALL TREATMENTS ARE NOT REPLICATED INTRODUCTION Precision Spatial Adjustment Analysis Example 2: Augmented Design with Systematic Checks in a Latin-Square Arrangement Summary Key Learning Points Review Questions Exercises References Ch14.pdf (p.385-413) CHAPTER 14: MULTIVARIATE METHODS FOR AGRICULTURAL RESEARCH Questions and Methods: A Field Guide Begin Data Analysis by Understanding the Nature of the Data Example Data, Exploration, and Multivariate Applications Data Exploration Summary and Final Remarks Key Learning Points Review Questions Exercises References Ch15.pdf (p.414-460) CHAPTER 15: NONLINEAR REGRESSION MODELS AND APPLICATIONS Nonlinear Regression Model Why Should We Use Nonlinear Models? Example 1: Maize Biomass Accumulation Comparing Alternative Models Example 2: Extended Application Summary Key Learning Points Review Questions Exercises References Ch16.pdf (p.461-521) CHAPTER 16: ANALYSIS OF NON-GAUSSIAN DATA Abstract A Brief History of Non-Gaussian Data Analysis in the Plant Sciences Introduction to GLMM – Key Concepts and Requirements for Implementation Some Things You Need to Know About Probability Distributions What Would Fisher Do? Gentle Introduction to the GLMM Key Issues – Data Versus Model Scale; Conditional Versus Marginal Model Model Versus Data Scale What to Include in the Linear Predictor Conditional and Marginal GLIMMIX Syntax Basics The Examples Example 1 – A Randomized Complete Block Design with Binomial Data Relevant Output Example 2 – Split-Plot experiment with Count Data Example 3 – A Multi-Location (Blocked) Design with Categorical (Multinomial) Data Relevant Output I: Solution Example 4 – A Repeated Measures Experiment with Count Data Description of the Study and Objectives Key Learning Points Review Questions REFERENCES AppendixA.pdf (p.522-650) AppendixB_onlineonly.pdf (p.651-1207) AppendixC_onlineonly.pdf (p.1208-1440)