"Practical Statistics and Experimental Design for Plant and Crop Science" by Alan G. Clewer, David H. Scarisbrick

ISBN: 0471899089

Category: Study

Posted on 2013-09-13. By anonymous.


"Practical Statistics and Experimental Design for Plant and Crop Science" by Alan G. Clewer, David H. Scarisbrick
John Wiley & Sons | 2001 | ISBN: 0471899089 0471899097 9780471899082 9780471899099 | 348 pages | PDF | 75 MB

This book provides an introduction to the principles of plant and crop experimentation. Avoiding mathematical jargon, this text explains how to plan and design an experiment, analyse results, interpret computer output and present findings; suitable for a practical course to science students wishing to appreciate statistical methods in agricultural and environmental research.

Written by experienced lecturers, this text will be invaluable to undergraduate and postgraduate students studying plant sciences, including plant and crop physiology, biotechnology, plant pathology and agronomy, plus ecology and environmental science students and those wanting a refresher or reference book in statistics.
Presents readers with a user-friendly, non-technical introduction to statistics and the principles of plant and crop experimentation.
Avoiding mathematical jargon, it explains how to plan and design an experiment, analyse results, interpret computer output and present findings.

Chapter 1 Basic Principles of Experimentation
1.1 Introduction
1.2 Field and glasshouse experiments
1.3 Choice of site
1.4 Soil testing
1.5 Satellite mapping
1.6 Sampling
Chapter 2 Basic Statistical Calculations
2.1 Introduction
2.2 Measurements and type of variable
2.3 Samples and populations
Chapter 3 Basic Data Summary
3.1 Introduction
3.2 Frequency distributions (discrete data)
3.3 Frequency distributions (continuous data)
3.4 Descriptive statistics
Chapter 4 The Normal Distribution, the t-Distribution and Confidence Intervals
4.1 Introduction to the normal distribution
4.2 The standard normal distribution
4.3 Further use of the normal tables
4.4 Use of the percentage points table (Appendix 2)
4.5 The normal distribution in practice
4.6 Introduction to confidence intervals
4.7 Estimation of the population mean. |j
4.8 The sampling distribution of the mean
4.9 Confidence limits for |j when o is known
4.10 Confidence limits for |j when o is unknownuse—of the t-distribution
4.11 Determination of sample size
4.12 Estimation of total crop yield
Chapter 5 Introduction to Hypothesis Testing
5.1 The standard normal distribution and the t-distribution
5.2 The single sample t-test
5.3 The P-value
5.4 Type I and Type II errors
5.5 Choice of level of significance
5.6 The usefulness of a test
5.7 Estimation versus hypothesis testing
5.8 The paired samples t-test
Chapter 6 Comparison of Two Independent Sample Means
6.1 Introduction
6.2 The Independent Samples t-test
6.3 Confidence intervals
6.4 The theory behind the t-test
6.5 The F-test
6.6 Unequal sample variances
6.7 Determination of sample size for a given precision
Chapter 7 Linear Regression and Correlation
7.1 Basic principles of Simple Linear Regression (SLR)
7.2 Experimental versus observational studies
7.3 The correlation coefficient
7.4 The least squares regression line and its estimation
7.5 Calculation of residuals
7.6 The goodness of fit
7.7 Calculation of the correlation coefficient
7.8 Assumptions, hypothesis tests and confidence intervals for simple linear regression
7.9 Testing the significance of a correlation coefficient
Chapter 8 Curve Fitting
8.1 Introduction
8.2 Polynomial fitting
8.3 Quadratic regression
8.4 Other types of curve
8.5 Multiple linear regression
Chapter 9 The Completely Randomised Design
9.1 Introduction
9.2 Design construction
9.3 Preliminary analysis
9.4 The one-way analysis of variance model
9.5 Analysis of variance
9.6 After ANOVA
9.7 Reporting results
9.8 The completely randomised design—unequal replication
9.9 Determination of number of replicates per treatment
Chapter 10 The Randomised Block Design
10.1 Introduction
10.2 The analysis ignoring blocks
10.3 The analysis including blocks
10.4 Using the computer
10.5 The effect of blocking
10.6 The randomised blocks model
10.7 Using a hand calculator to find the sums of squares
10.8 Comparison of treatment means
10.9 Reporting the results
10.10 Deciding how many blocks to use
10.11 Plot sampling
Chapter 11 The Latin Square Design
11.1 Introduction
11.2 Randomisation
11.3 Interpretation of computer output
11.4 The Latin square model
11.5 Using your calculator
Chapter 12 Factorial Experiments
12.1 Introduction
12.2 Advantages of factorial experiments
12.3 Main effects and interactions
12.4 Varieties as factors
12.5 Analysis of a randomised blocks factorial experiment with two factors
12.6 General advice on presentation
12.7 Experiments with more than two factors
12.8 Confounding
12.9 Fractional replication
Chapter 13 Comparison of Treatment Means
13.1 Introduction
13.2 Treatments with no structure
13.3 Treatments with structure (factorial structure)
13.4 Treatments with structure (levels of a quantitative factor)
13.5 Treatments with structure (contrasts)
Chapter 14 Checking the Assumptions and Transformation of Data
14.1 The assumptions
14.2 Transformations
Chapter 15 Missing Values and Incomplete Blocks
15.1 Introduction
15.2 Missing values in a completely randomised design
15.3 Missing values in a randomised block design
15.4 Other types of experiment
15.5 Incomplete block designs
Chapter 16 Split Plot Designs
16.1 Introduction
16.2 Uses of this design
16.3 The skeleton analysis of variance tables
16.4 An example with interpretation of computer output
16.5 The growth cabinet problem
16.6 Other types of split plot experiment
16.7 Repeated measures
Chapter 17 Comparison of Regression Lines and Analysis of Covariance
17.1 Introduction
17.2 Comparison of two regression lines
17.3 Analysis of covariance
17.4 Analysis of covariance applied to a completely randomised design
17.5 Comparing several regression lines
17.6 Conclusion
Chapter 18 Analysis of Counts
Chapter 19 Some Non-parametric Methods
Appendix 1: The normal distribution function
Appendix 2: Percentage points of the normal distribution
Appendix 3: Percentage points of the t-distribution
Appendix 4a: 5 per cent points of the F-distribution
Appendix 4b: 2.5 per cent points of the F-distribution
Appendix 4c: 1 per cent points of the F-distribution
Appendix 4d: 0.1 per cent points of the F-distribution
Appendix 5: Percentage points of the sample correlation coefficient (r) when the population correlation coefficient is 0 and n is the number of X, Y pairs
Appendix 6: 5 per cent points of the Studentised range, for use in Tukey and SNK tests
Appendix 7: Percentage points of the chi-square distribution
Appendix 8: Probabilities of S or fewer successes in the binomial distribution with n 'trials' and p = 0.5
Appendix 9: Critical values of Tin the Wilcoxon signed rank or matched pairs test
Appendix 10: Critical values of U in the Mann-Whitney test
Further reading
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