+44 1803 865913
By: Antal Kozak, Robert A Kozak, Christina L Staudhammer and Susan B Watts
408 pages, b/w illustrations, tables
With interest growing in areas of forestry, conservation and other natural sciences, the need to organize and tabulate large amounts of forestry and natural science information has become a necessary skill. Previous attempts of applying statistical methods to these areas tend to be over-specialized and of limited use; an elementary text using methods, examples and exercises that are relevant to forestry and the natural sciences is long overdue. This book utilises basic descriptive statistics and probability, as well as commonly used statistical inferential tools to introduce topics that are commonplace in a forestry context such as hypothesis texting, design of experiments, sampling methods, nonparametric tests and statistical quality control. It also contains examples and exercises drawn from the fields of forestry, wood science, and conservation. It is suitable for undergraduate students studying forestry and natural sciences, practitioners and researchers.
1. STATISTICS AND DATA: What do Numbers have to do with Trees?
2. DESCRIPTIVE STATISTICS: Making Sense of Data
3. PROBABILITY: The Foundation of Statistics
4. RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS: Outcomes of Random Experiments
5. SOME DISCRETE PROBABILITY DISTRIBUTIONS: Describing Data that is Counted
6. CONTINUOUS DISTRIBUTIONS AND THE NORMAL DISTRIBUTION: Describing Data that is Measured
7. SAMPLING DISTRIBUTIONS: The Foundation of Inference
8. ESTIMATION: Determining the Value of Population Parameters
9. TESTS OF HYPOTHESES: Making Claims about Population Parameters
10. GOODNESS-OF-FIT AND TEST FOR INDEPENDENCE: Testing Distributions
11. REGRESSION AND CORRELATION: Relationships between Variables
12. ANALYSIS OF VARIANCE: Testing Differences between Several Means
13. SAMPLING METHODS AND DESIGN OF EXPERIMENTS: Collecting Data
14. NONPARAMETRIC TESTS: Testing when Distributions are Unknown
15. QUALITY CONTROL: Statistics for Production and Processing
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