Practical Statistics and Experimental Design for Plant and Crop Science

ISBN-10: 0471899097

ISBN-13: 9780471899099

Edition: 2001

List price: $103.95
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Introducing the principles of plant and crop experimentation and the statistics used in the design and analysis of experiments, this work provides the plant and crop scientist with an understanding of the commonly used techniques.
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Book details

List price: $103.95
Copyright year: 2001
Publisher: John Wiley & Sons, Incorporated
Publication date: 2/8/2001
Binding: Paperback
Pages: 346
Size: 6.50" wide x 9.50" long x 1.00" tall
Weight: 1.342
Language: English

Basic Principles of Experimentation
Field and glasshouse experiments
Choice of site
Soil testing
Satellite mapping
Basic Statistical Calculations
Measurements and type of variable
Samples and populations
Basic Data Summary
Frequency distributions (discrete data)
Frequency distributions (continuous data)
Descriptive statistics
The Normal Distribution, the t-Distribution and Confidence Intervals
Introduction to the normal distribution
The standard normal distribution
Further use of the normal tables
Use of the percentage points table (Appendix 2)
The normal distribution in practice
Introduction to confidence intervals
Estimation of the population mean, [mu]
The sampling distribution of the mean
Confidence limits for [mu] when [sigma] is known
Confidence limits for [mu] when [sigma] is unknown--use of the t-distribution
Determination of sample size
Estimation of total crop yield
Introduction to Hypothesis Testing
The standard normal distribution and the t-distribution
The single sample t-test
The P-value
Type I and Type II errors
Choice of level of significance
The usefulness of a test
Estimation versus hypothesis testing
The paired samples t-test
Comparison of Two Independent Sample Means
The Independent Samples t-test
Confidence intervals
The theory behind the t-test
The F-test
Unequal sample variances
Determination of sample size for a given precision
Linear Regression and Correlation
Basic principles of Simple Linear Regression (SLR)
Experimental versus observational studies
The correlation coefficient
The least squares regression line and its estimation
Calculation of residuals
The goodness of fit
Calculation of the correlation coefficient
Assumptions, hypothesis tests and confidence intervals for simple linear regression
Testing the significance of a correlation coefficient
Curve Fitting
Polynomial fitting
Quadratic regression
Other types of curve
Multiple linear regression
The Completely Randomised Design
Design construction
Preliminary analysis
The one-way analysis of variance model
Analysis of variance
Reporting results
The completely randomised design--unequal replication
Determination of number of replicates per treatment
The Randomised Block Design
The analysis ignoring blocks
The analysis including blocks
Using the computer
The effect of blocking
The randomised blocks model
Using a hand calculator to find the sums of squares
Comparison of treatment means
Reporting the results
Deciding how many blocks to use
Plot sampling
The Latin Square Design
Interpretation of computer output
The Latin square model
Using your calculator
Factorial Experiments
Advantages of factorial experiments
Main effects and interactions
Varieties as factors
Analysis of a randomised blocks factorial experiment with two factors
General advice on presentation
Experiments with more than two factors
Fractional replication
Comparison of Treatment Means
Treatments with no structure
Treatments with structure (factorial structure)
Treatments with structure (levels of a quantitative factor)
Treatments with structure (contrasts)
Checking the Assumptions and Transformation of Data
The assumptions
Missing Values and Incomplete Blocks
Missing values in a completely randomised design
Missing values in a randomised block design
Other types of experiment
Incomplete block designs
Split Plot Designs
Uses of this design
The skeleton analysis of variance tables
An example with interpretation of computer output
The growth cabinet problem
Other types of split plot experiment
Repeated measures
Comparison of Regression Lines and Analysis of Covariance
Comparison of two regression lines
Analysis of covariance
Analysis of covariance applied to a completely randomised design
Comparing several regression lines
Analysis of Counts
The binomial distribution
Confidence intervals for a proportion
Hypothesis test of a proportion
Comparing two proportions
The chi-square goodness of fit test
r [times] c contingency tables
2 [times] c contingency tables: comparison of several proportions
2 [times] 2 contingency tables: comparison of two proportions
Association of plant species
Heterogeneity chi-square
Some Non-parametric Methods
The Sign test
The Wilcoxon single-sample test
The Wilcoxon matched pairs test
The Mann-Whitney U test
The Kruskal-Wallis test
Friedman's test
The normal distribution function
Percentage points of the normal distribution
Percentage points of the t-distribution
5 per cent points of the F-distribution
2.5 per cent points of the F-distribution
1 per cent points of the F-distribution
0.1 per cent points of the F-distribution
Percentage points of the sample correlation coefficient (r) when the population correlation coefficient is 0 and n is the number of X, Y pairs
5 per cent points of the Studentised range, for use in Tukey and SNK tests
Percentage points of the chi-square distribution
Probabilities of S or fewer successes in the binomial distribution with n 'trials' and p = 0.5
Critical values of T in the Wilcoxon signed rank or matched pairs test
Critical values of U in the Mann-Whitney test
Further reading
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