Elsevier S & T

Statistical Methods

Freund, Rudolf J.; Wilson, William J.

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Table of contents
  • Front CoverCover
  • Contentsv
  • Prefacexvii
  • Chapter 1. Data and Statistics1
  • 1.1 Introduction1
  • 1.2 Observations and Variables6
  • 1.3 Types of Measurements for Variables10
  • 1.4 Distributions12
  • 1.5 Numerical Descriptive Statistics19
  • 1.6 Exploratory Data Analysis32
  • 1.7 Bivariate Data38
  • 1.8 Populations, Samples, and Statistical Inference „ A Preview42
  • 1.9 Chapter Summary44
  • 1.10 Chapter Exercises49
  • Chapter 2. Probability and Sampling Distributions62
  • 2.1 Introduction63
  • 2.2 Probability66
  • 2.3 Discrete Probability Distributions73
  • 2.4 Continuous Probability Distributions81
  • 2.5 Sampling Distributions91
  • 2.6 Other Sampling Distributions101
  • 2.7 Chapter Summary108
  • 2.8 Chapter Exercises109
  • Chapter 3. Principles of Inference117
  • 3.1 Introduction117
  • 3.2 Hypothesis Testing118
  • 3.3 Estimation139
  • 3.4 Sample Size144
  • 3.5 Assumptions147
  • 3.6 Chapter Summary150
  • 3.7 Chapter Exercises152
  • Chapter 4. Inferences on a Single Population159
  • 4.1 Introduction159
  • 4.2 Inferences on the Population Mean161
  • 4.3 Inferences on a Proportion166
  • 4.4 Inferences on the Variance of One Population169
  • 4.5 Assumptions172
  • 4.6 Chapter Summary179
  • 4.7 Chapter Exercises180
  • Chapter 5. Inferences for Two Populations185
  • 5.1 Introduction185
  • 5.2 Inferences on the Difference between Means Using Independent Samples188
  • 5.3 Inferences on Variances197
  • 5.4 Inferences on Means for Dependent Samples200
  • 5.5 Inferences on Proportions205
  • 5.6 Assumptions and Remedial Methods208
  • 5.7 Chapter Summary211
  • 5.8 Chapter Exercises213
  • Chapter 6. Inferences for Two or More Means219
  • 6.1 Introduction219
  • 6.2 The Analysis of Variance221
  • 6.3 The Linear Model232
  • 6.4 Assumptions236
  • 6.5 Specific Comparisons242
  • 6.6 Random Models267
  • 6.7 Unequal Sample Sizes270
  • 6.8 Analysis of Means270
  • 6.9 Chapter Summary277
  • 6.10 Chapter Exercises279
  • Chapter 7. Linear Regression287
  • 7.1 Introduction287
  • 7.2 The Regression Model290
  • 7.3 Estimation of Parameters β0 and β1294
  • 7.4 Estimation of σ2 and the Partitioning of Sums of Squares297
  • 7.5 Inferences for Regression301
  • 7.6 Using the Computer312
  • 7.7 Correlation316
  • 7.8 Regression Diagnostics319
  • 7.9 Chapter Summary324
  • 7.10 Chapter Exercises326
  • Chapter 8. Multiple Regression333
  • 8.1 The Multiple Regression Model336
  • 8.2 Estimation of Coefficients338
  • 8.3 Inferential Procedures351
  • 8.4 Correlations362
  • 8.5 Using the Computer366
  • 8.6 Special Models370
  • 8.7 Multicollinearity379
  • 8.8 Variable Selection384
  • 8.9 Detection of Outliers, Row Diagnostics388
  • 8.10 Chapter Summary395
  • 8.11 Chapter Exercises399
  • Chapter 9. Factorial Experiments417
  • 9.1 Introduction417
  • 9.2 Concepts and Definitions419
  • 9.3 The Two-Factor Factorial Experiment422
  • 9.4 Specific Comparisons431
  • 9.5 No Replications448
  • 9.6 Three or More Factors448
  • 9.7 Chapter Summary451
  • 9.8 Chapter Exercises454
  • Chapter 10. Design of Experiments461
  • 10.1 Introduction462
  • 10.2 The Randomized Block Design464
  • 10.3 Randomized Blocks with Sampling471
  • 10.4 Latin Square Design476
  • 10.5 Other Designs480
  • 10.6 Chapter Summary492
  • 10.7 Chapter Exercises498
  • Chapter 11. Other Linear Models508
  • 11.1 Introduction508
  • 11.2 The Dummy Variable Model510
  • 11.3 Unbalanced Data514
  • 11.4 Computer Implementation of the Dummy Variable Model516
  • 11.5 Models with Dummy and Interval Variables517
  • 11.6 Extensions to Other Models526
  • 11.7 Binary Response Variables527
  • 11.8 Chapter Summary542
  • 11.9 Chapter Exercises547
  • Chapter 12. Categorical Data557
  • 12.1 Introduction557
  • 12.2 Hypothesis Tests for a Multinomial Population558
  • 12.3 Goodness of Fit Using the χ2 Test561
  • 12.4 Contingency Tables564
  • 12.5 Log linear Model571
  • 12.6 Chapter Summary575
  • 12.7 Chapter Exercises576
  • Chapter 13. Nonparametric Methods581
  • 13.1 Introduction581
  • 13.2 One Sample586
  • 13.3 Two Independent Samples588
  • 13.4 More Than Two Samples590
  • 13.5 Randomized Block Design593
  • 13.6 Rank Correlation595
  • 13.7 Chapter Summary597
  • 13.8 Chapter Exercises599
  • Chapter 14. Sampling and Sample Surveys602
  • 14.1 Introduction602
  • 14.2 Some Practical Considerations604
  • 14.3 Simple Random Sampling606
  • 14.4 Stratified Sampling609
  • 14.5 Other Topics616
  • 14.6 Chapter Summary617
  • Appendix A.618
  • A.1 The Normal Distribution„Probabilities Exceeding Z618
  • A.1A Selected Probability Values for the Normal Distribution„ Values of Z Exceeded with Given Prob622
  • A.2 The t Distribution„Values of t Exceeded with Given Probability623
  • A.3 χ2 Distribution—χ2 Values Exceeded with Given Probability624
  • A.4 The F Distribution, p= 0.1625
  • A.4A The F Distribution, p = 0.05627
  • A.4B The F Distribution, p = 0.025629
  • A.4C The F Distribution, p = 0.01631
  • A.4D The F Distribution, p = 0.005633
  • A.5 The Fmax Distribution„Percentage Points of Fmax = s2 max/s2 min635
  • A.6 Orthogonal Polynomials (Tables of Coefficients for Polynomial Trends)636
  • A.7 Percentage Points of the Studentized Range637
  • A.8 Percentage Points of the Duncan Multiple Range Test639
  • A.9 Critical Values for the Wilcoxon Signed Rank Test N= 5(1)50641
  • A.10 The Mann–Whitney Two-Sample Test642
  • A.11 Exact Critical Values for Use with the Analysis of Means643
  • Appendix B. A Brief Introduction to Matrices645
  • Matrix Algebra646
  • Solving Linear Equations649
  • References651
  • Solutions to Selected Exercises656
  • Index668
Book details
  • Vendor Elsevier S & T
  • SKU 9780122676512
  • ISBN-13 9780080498225
  • Author Freund, Rudolf J.; Wilson, William J.
  • Edition 2nd
  • Category Mathematics
  • Subject General

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Description eBook delivery & access Reviews
This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection.

* Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions
* Examples contain graphical illustration for ease of interpretation
* Intended for use with almost any statistical software
* Examples are worked to a logical conclusion, including interpretation of results
* A complete Instructor's Manual is available to adopters