196 results on '"A. Brereton"'
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2. Introduction to residuals and estimation by principal components analysis
3. Principal components analysis with several objects and variables
4. Numerical introduction to principal components analysis
5. Graphical introduction to principal components analysis
6. Introduction to residuals and estimation by principal components analysis
7. Principal components analysis with several objects and variables
8. Numerical introduction to principal components analysis
9. Introduction to statistical, algorithmic and theoretical basis of principal components analysis
10. Graphical introduction to principal components analysis
11. Multivariate classification models
12. Introduction to Bayesian methods
13. Contingency tables, confusion matrices, classifiers and quality of prediction
14. Empirical and statistical <scp> p </scp> values and Type 1 error rates: Putting it all together
15. Contingency tables, confusion matrices, classifiers and quality of prediction
16. Multivariate classification models
17. Introduction to Bayesian methods
18. Empirical and statistical p values and Type 1 error rates: Putting it all together
19. False discovery rates, power and related concepts
20. Alpha, beta, type 1 and 2 errors, Ergon Pearson and Jerzy Neyman
21. P values and Ronald Fisher
22. Why we should be interested in P values and hypothesis tests
23. False discovery rates, power and related concepts
24. Alpha, beta, type 1 and 2 errors, Ergon Pearson and Jerzy Neyman
25. P values and Ronald Fisher
26. Why we should be interested in P values and hypothesis tests
27. Basic vector algebra
28. How F and P values are influenced by centring
29. <scp> P </scp> values and residuals using non‐orthogonal <scp> X </scp> matrices and the relationship between t and <scp> F </scp> statistics for studying individual factors
30. Re-evaluating the role of the Mahalanobis distance measure
31. Hotelling'sTsquared distribution, its relationship to theFdistribution and its use in multivariate space
32. False discovery rates, power and related concepts.
33. Determining the significance of individual factors for orthogonal designs
34. P values and residuals using non‐orthogonal X matrices and the relationship between t and F statistics for studying individual factors
35. How F and P values are influenced by centring
36. TheFdistribution and its relationship to the chi squared andtdistributions
37. Populations and samples
38. Alpha, beta, type 1 and 2 errors, Ergon Pearson and Jerzy Neyman.
39. The chi squared and multinormal distributions
40. Orthogonality, uncorrelatedness, and linear independence of vectors
41. Points, vectors, linear independence and some introductory linear algebra
42. Statistically independent events and distributions
43. The normal distribution
44. A short history of chemometrics: a personal view
45. Partial least squares discriminant analysis: taking the magic away
46. Partial least squares discriminant analysis for chemometrics and metabolomics: How scores, loadings, and weights differ according to two common algorithms
47. Introduction to analysis of variance
48. ANOVA tables and statistical significance of models
49. Sources of error
50. Degrees-of-freedom, errors, and replicates
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