Principles and procedures of statistics : a biometrical approach
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書誌事項
Principles and procedures of statistics : a biometrical approach
（McGraw-Hill series in probability and statistics / David Blackwell and Herbert Solomon, consulting editors）
McGraw-Hill, c1997
3rd ed
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
This textbook provides students with a basic knowledge of the principles and procedures of applied statistics. Maintaining its clarity and comprehensive coverage from previous editions, the third edition of Principles and Procedures of Statistics A Biometrical Approach includes modern topics, the use of computer output and analysis from statistical software, and updated real-world data sets. This text assumes no knowledge of calculus.
目次
- PrefaceSelected SymbolsCHAPTER 1: Introduction1.1 Statistics Defined1.2 Some History of Statistics1.3 Statistics and the Scientific Method1.4 Studying Statistics1.5 Statistical Computing ReferencesCHAPTER 2: Observations2.1 Introduction2.2 Variables2.3 Distributions2.4 Populations and Samples2.5 Random Samples: The Collection of Data2.6 Presentation, Summarization, and Characterization of Data2.7 Measures of Central Tendency2.8 Measures of Dispersion2.9 Standard Error of the Mean2.10 Coefficient of Variability or of Variation2.11 An Example2.12 Selecting and Using Statistical Packages2.13 The Linear Additive Model2.14 An ExampleA Proposed Laboratory Exercise in Connection with Chapter 2 ReferencesCHAPTER 3: Probability3.1 Introduction3.2 Some Elementary Probability3.3 The Binomial Distribution3.4 Probability Functions for Continuous Variables3.5 The Normal Distribution3.6 Probabilities for a Normal Distribution
- The Use of a Probability Table3.7 The Normal Distribution with Mean u and Variance o23.8 The Distribution of Means3.9 The X2 Distribution3.10 The Distribution of Student's t3.11 Estimation and Inference3.12 Prediction of Sample Results3.13 Computing ProbabilitiesA Proposed Laboratory Exercise in Connection with Chapter 3 ReferenceCHAPTER 4: Sampling from a Normal Distribution4.1 Introduction4.2 A Normally Distributed Population4.3 Random Samples from a Normal Distribution4.4 The Distribution of Sample Means4.5 The Distribution of Sample Variances and Standard Deviations4.6 The Unbiasedness of s24.7 The Standard Deviation of the Mean, or the Standard Error4.8 The Distribution of Student's t4.9 The Confidence Statement4.10 The Sampling of Differences4.11 Summary of SamplingA Proposed Laboratory Exercise in Connection with Chapter 4 referencesCHAPTER 5: Comparisons Involving Two Sample Means5.1 Introduction5.2 Tests of Significance5.3 Testing the Hypothesis that a Population Mean is a Specified Value5.4 Tests for Two or More Means5.5 Comparison of two sample Means, Independent Samples and Equal Variances5.6 The Linear Additive Model5.7 Comparison of Sample Means: Meaningfully Paired Observations5.8 The Linear Additive Model5.9 Independent Samples and Unequal Variances5.10 The Mean and Variance of a Linear Equation5.11 Testing the Hypothesis of Equality of Variances5.12 Power, Sample Size, and the Detection of Differences5.13 Stein's Two-Stage SampleReferencesCHAPTER 6: Principles of Experimental Design6.1 Introduction6.2 What is an experiment?6.3 Objectives of and Experiment6.4 Experimental Unit and Treatment6.5 Experimental Error6.6 Replication and Its Functions6.7 Factors Affecting the Number of Replicates6.8 Relative Precision of Designs Involving few Treatments6.9 Error Control6.10 Choice of Treatments6.11 Refinement of Technique6.12 Randomization6.13 Statistical Inference ReferencesCHAPTER 7: Analysis of Variance I: The One-Way Classification7.1 Introduction7.2 The Completely Random Design7.3 Data with a Single Criterion of Classification: The Analysis of Variance for Any Number of Groups with Equal Replication7.5 The Linear Additive Model7.6 Analysis of Variance with Subsamples: Equal Subsample Numbers7.7 The Linear Model for Subsampling7.8 Analysis of Variance with Subsamples--Unequal Subsample Numbers7.9 Variance Components in Planning Experiments Involving Subsamples7.10 Assumptions Underlying the Analysis of Variance ReferencesCHAPTER 8: Multiple Comparisons8.1 Introduction8.2 The Least Significant Difference8.3 Contrasts8.4 Testing Effects Suggested by the Data8.5 Scheffe's Test8.6 Tukey's w Procedure8.7 The Student-Newman-Keuls, or S-N-K, Test8.8 Duncan's New Multiple-Range Test8.9 Comparing all Means with a Control8.10 Waller-Duncan's Bayesian k-ratio t Test8.11 Testing Unequally Replicated Means8.12 Comparison of Procedures ReferencesCHAPTER 9: Analysis of Variance II: Multiway Classification9.1 Introduction9.2 The Randomized Complete Block Design9.3 Analysis of Variance for Any Number of Treatments
- Randomized9.4 The Nature of the Error Team9.5 Partitioning Experimental Error9.6 Missing Values9.7 Estimation of Gain in Efficiency9.8 The Randomized Complete Block Design: More Than One Observation per Treatment per Block9.9 Linear Models and the Analysis of Variance9.10 Double Grouping: Latin Square9.11 Analysis of Variance of the Latin Square9.12 Missing Plots in the Latin Square9.13 Estimation of Gain in Efficiency9.14 The Linear Model for the Latin Square9.15 The Size of an Experiment9.16 TransformationsReferencesCHAPTER 10: Linear Regression10.1 Introduction10.2 The Linear Regression of Y on X10.3 The Linear Regression Model and Equation10.4 Sources of Variation of Linear Regression10.5 Regressed and Adjusted Values10.6 Standard Deviations, Confidence Intervals, and Tests of Hypotheses10.7 Prediction and Estimation10.8 Analysis of Variance as Regression10.9 Prediction of X, Model 110.10 Bivariate Distributions, Model II10.11 Regression through the Origin10.12 Weighted Regression AnalysisReferencesCHAPTER 11: Linear Correlation11.1 Introduction11.2 Correlation and the Correlation Coefficient11.3 Correlation and Regression11.4 Sampling Distributions, Confidence Intervals, and Tests of Hypotheses11.5 Homogeneity of Correlation Coefficients11.6 Intraclass CorrelationReferencesCHAPTER 12: Matrix Notation12.1 Introduction12.2 Matrices12.3 Matrix Operations12.4 Inverses, Linear Dependence, and Rank ReferenceReferencesCHAPTER 13: Linear Regression in Matrix Notation13.1 Introduction13.2 The Model and Least-Squares Estimates13.3 The Analysis of Variance13.4 Standard Deviations, Confidence Intervals, and Tests of Hypotheses13.5 Estimation and Prediction13.6 Indicator or Binary VariablesCHAPTER 14: Multiple and Partial Regression and Correlation 14.1 Introduction14.2 The Linear Equation and Its Interpretation in More than Two Dimensions14.3 Partial, Total, and Multiple Linear Regression14.4 The Sample Multiple Linear Regression Equation14.5 Multiple Linear Regression
- Two Independent Variables14.6 Partial and Multiple Correlations14.7 Multiple Linear Regression: A Further Example14.8 Miscellaneous14.9 Standard Partial Regression CoefficientsReferencesCHAPTER 15: Analysis of Variance III: Factorial Experiments15.1 Introduction15.2 Factorial Experiments15.3 The 2 X 2 Factorial Experiment: An Example15.4 The 3 X 2 or 32 X 2 Factorial: An Example15.5 Linear Models for Factorial Experiments15.6 n-Way Classifications and Factorial Experiments
- Response Surfaces15.7 Polynomial Responses15.8 A Single Degree of Freedom for NonadditivityReferencesCHAPTER 16: Analysis of Variance IV: Split-Plot Designs and Analysis16.1 Introduction16.2 Split-Plot Designs16.3 An Example of a Split-Plot Design16.4 Missing Data in Split-Plot Design16.5 Split-Block Design16.6 Split-Plot and Split-Block Models16.7 Split Plots in Space and Time16.8 Series of Similar ExperimentsReferencesCHAPTER 17: Analysis of Covariance17.1 Introduction17.2 Uses of Covariance Analysis17.3 The Model and Assumptions for Covariance17.4 Fitting a Covariance Model17.5 Adjusted Treatment Means17.6 Increase in Precision Due to Covariance17.7 Treatment versus Error Regression17.8 Homogeneity of regression Coefficients17.9 Covariance Where the Treatment Sum of Squares is Partitioned17.10 Estimation of Missing Values by Covariance17.11 Two or More CovariatesReferencesCHAPTER 18: Analysis of Variance V: Unequal Subclass Numbers18.1 Introduction 18.2 Multiple Observations within Subclasses18.3 The Analysis of Seriously Unbalanced Data18.4 Further Discussion and Example18.5 Other Analytical TechniquesReferencesCHAPTER 19: Some Uses of Chi-Square19.1 Introduction19.2 Confidence Interval for o219.3 Homogeneity of Variance19.4 Goodness of Fit for Continuous Distributions19.5 Combining Probabilities from Tests of SignificanceReferencesCHAPTER 20: Enumeration Data I: One-Way Classifications20.1 Introduction20.2 The X2 Test Criterion20.3 Two-Cell Tables: Confidence Limits for a Proportion or Percentage20.4 Two-Cell Tables: Tests of Hypotheses20.5 Test of Hypotheses for a limited Set of Alternatives20.6 Sample Size20.7 One-Way Tables with n cellsReferencesCHAPTER 21: Enumeration Data II: Contingency Tables21.1 Introduction21.2 The Random Sampling Model21.3 The Stratified Random Sampling Model21.4 The 2 X 2 or Fourfold Table21.5 Fisher's Exact Test 21.6 Nonindependent Samples in 2 X 2 Tables 21.7 Homogeneity of Two-Cell Samples 21.8 Additivity of X2 21.9 Linear Regression in r X 2 Tables 21.10 Sample Size in 2 X 2 Tables 21.11 n-Way Classification References CHAPTER 22: Categorical Models 22.1 Introduction 22.2 Introducing the Likelihood Function 23.3 Likelihood for Categorical Data 22.4 Logit and Loglinear Models 22.5 Interpretation of Model Results References CHAPTER 23: Some Dis crete Distributions 23.1 Introduction 23.2 The Hypergeometric Distribution 23.3 The Binomial Distribution 23.4 Fitting a Binomial Distribution 23.5 Transformation for the Binomial Distribution 23.6 The Poisson Distribution 23.7 Other Tests with Poisson Distributions References CHAPTER 24: Nonparametric Statistics 24.1 Introduction 24.2 The X2 test of Goodness of Fit 24.3 The Kolmogorov-Smirnov One-Sample test and the Normal Probability Plot 24.4 The Sign Test 24.5 Wilcoxon's Signed Rank Test 24.6 The Kolmogorov-Smirnov Two Sample Test 24.7 The Wilcoxon-Mann-Whitney Two-Sample Test 24.8 The Median Test 24.9 Kruskal-Wallis k-Sample Test 24.10 The Median Test for k Samples 24.11 Friedman's Test for the two Way Classification 24.12 A Median Test for the Two-Way Classification 24.13 Chebyshev's Inequality 24.14 Spearman's Coefficient of rank Correlation 24.15 The Olmstead-Tukey Corner Test of Association 24.16 A Randomization Test for Regression References CHAPTER 25: Sampling Finite Populations 25.1 Introduction 25.2 Organizing the Survey 25.3 Probability Sampling 25.4 Simple Random Sampling 25.5 Stratified Sampling 25.6 Optimum Allocation 25.7 Multistage or Cluster Sampling References Appendix Tables Index
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