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In the previous Chapter we have seen models with more than one explanatory factor, that are used to describe data from Randomised Complete Block Designs and Latin Square designs, where we have one treatment factor and, respectively, one or two blocking factors. 16.8 Expressions, functions and argumentsĬhapter 11 Multi-way ANOVA models with interactions.16.2 Installing R and moving the first steps.16 APPENDIX: A very gentle introduction to R.14.5.3 The coefficient of determination (R 2).14.5.2 Approximate F test for lack of fit.14.3.2 Approximation with a polynomial function.13.5.4 F test for goodness of fit and coefficient of determination.13.5.2 Standard errors for parameter estimates.13.1 Case-study: N fertilisation in wheat.12.1 Example 1: a split-plot experiment.11 Multi-way ANOVA models with interactions.10.5 Another example: comparing working protocols.10.1 Motivating example: a genotype experiment in blocks.9.6 Multiple comparisons with transformed data.9 Contrasts and multiple comparison testing.8.2.1 Plot of residuals against expected values.7.1 Comparing herbicides in a pot-experiment.6.3 Correct interpretation of the P-value.6.2 Comparing proportions: the \(\chi^2\) test.6.1 Comparing sample means: the Student’s t-test.5.1.3 The frequentist confidence interval.5.1.2 A theoretical sampling distribution.5.1.1 The empirical sampling distribution.4.5 Monte Carlo methods to simulate an experiment.3.2.2 Descriptive stats for distributions of frequencies.3.1.4 Relationship between quantitative variables.2.7.2 Selecting the units within the field.1.8 How can we assess whether the data is valid?.1.7.3 Lack of true-replicates or careless randomisation.1.7.2 ‘Confounding’ and spurious correlation.1.6 The basic principles of experimental design.1.3 Good data is based on good ‘methods’.