Natural Resources Biometrics begins with a review of descriptive statistics, estimation, and hypothesis testing. The following chapters cover one- and two-way analysis of variance (ANOVA), including multiple comparison methods and interaction assessment, with a strong emphasis on application and interpretation. Simple and multiple linear regressions in a natural resource setting are covered in the next chapters, focusing on correlation, model fitting, residual analysis, and confidence and prediction intervals. The final chapters cover growth and yield models, volume and biomass equations, site index curves, competition indices, importance values, and measures of species diversity, association, and community similarity.
Chapter 1: Descriptive Statistics and the Normal Distribution
Chapter 2: Sampling Distributions and Confidence Intervals
Chapter 3: Hypothesis Testing
Chapter 4: Inferences about the Differences of Two Populations
Chapter 5: One-way Analysis of Variance
Chapter 6: Two-way Analysis of Variance
Chapter 7: Correlation and Simple Linear Regression
Chapter 8: Multiple Linear Regression
Chapter 9: Modeling Growth, Yield, and Site Index
Chapter 10: Quantitative Measures of Diversity, Site Similarity, and Habitat Suitability
Appendix: Biometrics Labs #1-5
About the Author: Diane KiernanDiane Kiernan completed her Ph.D. in quantitative methods in forest science at SUNY ESF in 2007. She is currently teaching Introduction to Probability and Statistics and Forest Biometrics at SUNY ESF and Advanced Statistics at LeMoyne College in Syracuse, New York. She is employed as a biometrician analyzing long-term re-measurement data for the SUNY ESF forest properties and is involved with additional research projects at SUNY ESF. Diane has authored and co-authored two previous books on statistics currently being used in her classes.