Statistical methods in agriculture and experimental biology / R. Mead, R.N. Curnow & A.M. Hasted.

Contents;

1 INTRODUCTION
1.1 The need for Statistics
1.2 The use of Computers in Statistics

2 PROBABILITY AND DISTRIBUTIONS
2.1 Probability
2.2 Populations and Samples
2.3 Means and Variances
2.4 The Normal Distribution
2.5 Sampling Distribution

3 ESTIMATION AND HYPOTHESIS TESTING
3.1 Estimation of the population mean
3.2 Testing hypotheses about the population mean
3.3 Population Variance Unknown
3.4 Comparison of Samples
3.5 A Pooled Estimate of Variance

4 A SIMPLE EXPERIMENT
4.1 Randomization and Replication
4.2 Analysis of a Completely Randomized design with two treatments
4.3 A Completely Randomized design with several treatment
4.4 Testing overall variation between the treatments
4.5 Analysis using a statistical package

5 CONTROL OF RANDOM VARIATION BY BLOCKING
5.1 Local control of variation
5.2 Analysis of a Randomized block design
5.3 Meaning of the error mean square
5.4 Latin Square designs
5.5 Analysis of structured experimental data using a computer package, etc.

6 PARTICULAR QUESTIONS ABOUT TREATMENTS
6.1 Treatment structure
6.2 Treatment contrasts
6.3 Factorial treatment structure
6.4 Main effects and Interactions
6.5 Analysis of variance for a two-factor experiment

7 MORE ON FACTORIAL TREATMENT STRUCTURE
7.1 More than two factors
7.2 Factors with two levels
7.3 The double benefit of factorial structure
7.4 Many factors and small blocks
7.5 The analysis of confounded experiment, etc.

8 THE ASSUMPTIONS BEHIND THE ANALYSIS
8.1 Our assumptions
8.2 Normality
8.3 Variance homogeneity
8.4 Additivity
8.5 Transformations data for theoretical reasons, etc.

9 STUDYING LINEAR RELATIONSHIPS
9.1 Linear regression
9.2 Assessing the regression line
9.3 Inferences about the slope of a line
9.4 Prediction using
9.5 Correlation

10 MORE COMPLEX RELATIONSHIPS
10.1 Making the crooked straight
10.2 Two Independent variables
10.3 Testing the components of a multiple relationship
10.4 Multiple Regression
10.5 Possible problems in computer multiple Regression

11 LINEAR MODELS
11.1 The use of models
11.2 Models for factors and variables
11.3 Comparison of Regressions
11.4 Fitting parallel Lines
11.5 Covariance analysis, etc.

12 NONLINEAR MODELS
12.1 Advantages of Linear and non-Linear Models
12.2 Fitting non-linear models to data
12.3 Inferences about non-linear parameters
12.4 Exponential Models
12.5 Inverse Polynomial Models, etc.

13 THE ANALYSIS OF PROPORTIONS
13.1 Data in the form of Frequencies
13.2 The 2x2 contingency tables
13.3 More than two situations or more two outcomes
13.4 General contingency tables
13.5 Estimation of proportions, etc.

14 MODELS AND DISTRIBUTIONS FOR FREQUENCY DATA
14.1 Models for frequency data
14.2 Testing the agreement of frequency data with simple models
14.3 Investigating More complex models
14.4 The binomial distribution
14.5 The Poisson distribution, etc.

15 MAKING AND ANALYZING SEVERAL EXPERIMENTAL MEASUREMENTS
15.1 Different measurements on the same units
15.2 Interdependence of different variables
15.3 Repeated measurement
15.4 Joint {Bivariate} Analysis
15.5 Investigating Relationships with Experimental data

16 CHOOSING THE MOST APPROPRIATE EXPERIMENTAL DESIGN
16.1 The components of design: Units and Treatments
16.2 Replication and Precision
16.3 Different Levels of Variation and within-Unit Replication
16.4 Variance components and split plot design
16.5 Randomization, etc.

17 SAMPLING FINITE POPULATIONS
17.1 Experiments and sample surveys
17.2 Simple Random sampling
17.3 Stratified Random Sampling
17.4 Cluster Sampling, Multistage Sampling and sampling proportional to size
17.5 Ratio and Regression estimates

References : p. 403-404 . _ Index : p. 413-415