# "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.

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**"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.

**Contents**

Preface

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

References

Further reading

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

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