Showing papers in "The Statistician in 1967"
TL;DR: The theory of least squares and analysis of variance has been studied in the literature for a long time, see as mentioned in this paper for a review of some of the most relevant works. But the main focus of this paper is on the analysis of variance.
Abstract: Algebra of Vectors and Matrices. Probability Theory, Tools and Techniques. Continuous Probability Models. The Theory of Least Squares and Analysis of Variance. Criteria and Methods of Estimation. Large Sample Theory and Methods. Theory of Statistical Inference. Multivariate Analysis. Publications of the Author. Author Index. Subject Index.
5,182 citations
543 citations
TL;DR: This book discusses the growth of modern Statistics, the nature of Statistical Data, and the Design of Experiments: Further Considerations, which aims to provide a guide to the design of experiments and their results.
Abstract: Preface Chapter 1: Introduction 1.1 The Growth of Modern Statistics 1.2 Sources of Statistical Data 1.3 The Nature of Statistical Data Chapter 2: Summarizing Data: Listing and Grouping 2.1 Listing Numerical Data 2.2 Stem-and-Leaf Displays 2.3 Frequency Distribution 2.4 Graphical Presentations 2.5 Summarizing Two-Variable Data Chapter 3: Summarizing Data: Measures of Location 3.1 Population and Samples 3.2 The Mean 3.3 The Weighted Mean 3.4 The Median 3.5 Other Fractiles 3.6 The Mode 3.7 The Description of Grouped Data 3.8 Technical Note (Summations) Chapter 4: Summarizing Data: Measures of Variation 4.1 The Range 4.2 The Standard Deviation and the Variance 4.3 Applications of the Standard Deviation 4.4 The Description of Grouped Data 4.5 Some Further Descriptions Chapter 5: Possibilities and Probabilities 5.1 Counting 5.2 Permutations 5.3 Combinations 5.4 Probability Chapter 6: Some Rules of Probability 6.1 Samples Spaces and Events 6.2 The Postulates of Probability 6.3 Probabilities and Odds 6.4 Addition Rules 6.5 Conditional Probability 6.6 Multiplication Rules 6.7 Bayes' Theorem Chapter 7: Expectations and Decisions 7.1 Mathematical Expectation 7.2 Decision Making 7.3 Statistical Decision Problems Chapter 8: Probability Distributions 8.1 Random Variables 8.2 Probability Distributions 8.3 The Binomial Distribution 8.4 The Hypergeometric Distribution 8.5 The Poisson Distribution 8.6 The Multinomial Distribution 8.7 The Mean of a Probability Distribution 8.8 The Standard Deviation of a Probability Distribution Chapter 9: The Normal Distribution 9.1 Continuous Distributions 9.2 The Normal Distribution 9.3 A Check for Normality 9.4 Applications of the Normal Distribution 9.5 The Normal Approximation to the Binomial Chapter 10: Sampling and Sampling Distributions 10.1 Random Sampling 10.2 Sample Designs 10.3 Systematic Sampling 10.4 Stratified Sampling 10.5 Cluster Sampling 10.6 Sampling Distributions 10.7 The Standard Error of the Mean 10.8 The Central Limit Theorem 10.9 Some Further Considerations 10.10 Technical Note (Simulation) Chapter 11: Problems of Estimation 11.1 The Estimation of Means 11.2 The Estimation of Means 11.3 The Estimation of Standard Deviations 11.4 The Estimation of Proportions Chapter 12: Tests of Hypotheses: Means 12.1 Tests of Hypotheses 12.2 Significance Tests 12.3 Tests Concerning Means 12.4 Tests Concerning Means ( unknown) 12.5 Differences Between Means 12.6 Differences Between Means ( unknown) 12.7 Difference Between Means (Paired data) Chapter 13: Tests of Hypotheses: Standard Deviations 13.1 Tests Concerning Standard Deviations 13.2 Tests Concerning Two Standard Deviations Chapter 14: Tests of Hypotheses Based on Count Data 14.1 Tests Concerning Proportions 14.2 Tests Concerning Proportions (Large Samples) 14.3 Differences Between Proportions 14.4 The Analysis of r x c Table 14.5 Goodness of Fit Chapter 15: Analysis of Variance 15.1 Difference among k Means: An Example 15.2 The Design of Experiments: Randomization 15.3 One-Way Analysis of Variance 15.4 Multiple Comparisons 15.5 The Design of Experiments: Blocking 15.6 Two-Way Analysis of Variance 15.7 Two-Way Analysis of Variance Without Interaction 15.8 The Design of Experiments: Replication 15.9 Two-Way Analysis of Variance with Interaction 15.10 The Design of Experiments: Further Considerations Chapter 16: Regression 16.1 Curve Fitting 16.2 The Method of Least Squares 16.3 Regression Analysis 16.4 Multiple Regression 16.5 Nonlinear Regression Chapter 17: Correlation 17.1 The Coefficient of Correlation 17.2 The Interpretation of r 17.3 Correlation Analysis 17.4 Multiple and Partial Correlation Chapter 18: Nonparametric Tests 18.1 The Sign Test 18.2 The Sign Test (Large Samples) 18.3 The Signed-Rank Test 18.4 The Signed-Rank Test (Large Samples) 18.5 The U Test 18.6 The U Test (Large Samples) 18.7 The H Test 18.8 Tests of Randomness: Runs 18.9 Tests of Randomness: Runs (Large Samples) 18.10 Tests of Randomness: Runs Above and Below the Median 18.11 Rank Correlation 18.12 Some Further Considerations
410 citations
TL;DR: In this paper, some aspects of the geometrical interpretation of Principal Component, Principal Coordinates, Canonical Variate and Factor Analysis are discussed, with stress given to implicit assumptions not always realized; some special cases of principal components analysis are included.
Abstract: Summary MANY multivariate statistical methods may be regarded as techniques for investigating a sample space in which each sample member is represented by a point. Some aspects of the geometrical interpretation of Principal Components, Principal Coordinates, Canonical Variate and Factor Analysis are discussed, with stress given to implicit assumptions not always realized; some special cases of principal components analysis are included. The angular representation of samples is discussed and the inter-relationship between the hierarchical representation of a sample and its spatial representation briefly described.
221 citations
154 citations
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TL;DR: The evaluation of total error in survey findings has been largely neglected as discussed by the authors, and confidence intervals and other common tools deal only with random and occasionally other limited errors, such as limited errors.
Abstract: The evaluation of total error in survey findings is largely neglected. Confidence intervals and other common tools deal only with random and occasionally other limited errors. This article presents...
10 citations
10 citations
TL;DR: In this paper, the authors outline some of the statistical and computer techniques which are available for the study of variation in taxonomic research, and suggest their purposes, and evolve a practical strategy for taxonomic analysis which is based on measured variables rather than upon qualitative attributes.
Abstract: Summary IN this paper an attempt is made to outline some of the statistical and computer techniques which are available for the study of variation in taxonomic research. It is not intended to discuss the mathematics of the techniques, or their derivation, but rather to suggest their purposes, and to evolve a practical strategy for taxonomic research which is based on measured variables rather than upon qualitative attributes.