| Autore |
Heiberger Richard M
|
| Edizione | [2nd ed. 2015.] |
| Pubbl/distr/stampa |
New York, NY : , : Springer New York : , : Imprint : Springer, , 2015
|
| Descrizione fisica |
1 online resource (XXXI, 898 p. 341 illus., 326 illus. in color.)
|
| Disciplina |
519.50285
|
| Collana |
Springer Texts in Statistics
|
| Soggetto topico |
Statistics
R (Computer program language)
Statistical Theory and Methods
Statistics and Computing/Statistics Programs
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
|
| ISBN |
1-4939-2122-3
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Intro -- Preface -- 1 Audience -- 2 Motivation -- 3 Structure -- 4 Computation -- 4.1 R -- 4.2 The HH Package in R -- 4.3 S-Plus, now called S+ -- 4.4 SAS -- 5 Chapters in the Second Edition -- 5.1 Revised Chapters -- 5.2 Revised Appendices -- 6 Exercises -- Acknowledgments: First Edition -- Acknowledgments -- Contents -- Author Bios -- 1 Introduction and Motivation -- 1.1 Statistics in Context -- 1.2 Examples of Uses of Statistics -- 1.2.1 Investigation of Salary Discrimination -- 1.2.2 Measuring Body Fat -- 1.2.3 Minimizing Film Thickness -- 1.2.4 Surveys -- 1.2.5 Bringing Pharmaceutical Products to Market -- 1.3 The Rest of the Book -- 1.3.1 Fundamentals -- 1.3.2 Linear Models -- 1.3.3 Other Techniques -- 1.3.4 New Graphical Display Techniques -- 1.3.5 Appendices on Software -- 1.3.6 Appendices on Mathematics and Probability -- 1.3.7 Appendices on Statistical Analysis and Writing -- 2 Data and Statistics -- 2.1 Types of Data -- 2.2 Data Display and Calculation -- 2.2.1 Presentation -- 2.2.2 Rounding -- 2.3 Importing Data -- 2.3.1 Datasets for This Book -- 2.3.2 Other Data sources -- 2.4 Analysis with Missing Values -- 2.5 Data Rearrangement -- 2.6 Tables and Graphs -- 2.7 R Code Files for Statistical Analysis and Data Display (HH) -- 2.A Appendix: Missing Values in R -- 3 Statistics Concepts -- 3.1 A Brief Introduction to Probability -- 3.2 Random Variables and Probability Distributions -- 3.2.1 Discrete Versus Continuous Probability Distributions -- 3.2.2 Displaying Probability Distributions-Discrete Distributions -- 3.2.3 Displaying Probability Distributions-Continuous Distributions -- 3.3 Concepts That Are Used When Discussing Distributions -- 3.3.1 Expectation and Variance of Random Variables -- 3.3.2 Median of Random Variables -- 3.3.3 Symmetric and Skewed Distributions -- 3.3.4 Displays of Univariate Data -- 3.3.4.1 Histogram.
3.3.4.2 Stem-and-Leaf Display -- 3.3.4.3 Boxplots -- 3.3.5 Multivariate Distributions-Covarianceand Correlation -- 3.4 Three Probability Distributions -- 3.4.1 The Binomial Distribution -- 3.4.2 The Normal Distribution -- 3.4.3 The (Student's) t Distribution -- 3.5 Sampling Distributions -- 3.6 Estimation -- 3.6.1 Statistical Models -- 3.6.2 Point and Interval Estimators -- 3.6.3 Criteria for Point Estimators -- 3.6.4 Confidence Interval Estimation -- 3.6.5 Example-Confidence Interval on the Mean μ of a Population Having Known Standard Deviation -- 3.6.6 Example-One-Sided Confidence Intervals -- 3.7 Hypothesis Testing -- 3.8 Examples of Statistical Tests -- 3.9 Power and Operating Characteristic (O.C.) (Beta) Curves -- 3.10 Efficiency -- 3.11 Sampling -- 3.11.1 Simple Random Sampling -- 3.11.2 Stratified Random Sampling -- 3.11.3 Cluster Random Sampling -- 3.11.4 Systematic Random Sampling -- 3.11.5 Standard Errors of Sample Means -- 3.11.6 Sources of Bias in Samples -- 3.12 Exercises -- 4 Graphs -- 4.1 What Is a Graph? -- 4.2 Example-Ecological Correlation -- 4.3 Scatterplots -- 4.4 Scatterplot Matrix -- 4.5 Array of Scatterplots -- 4.6 Example-Life Expectancy -- 4.6.1 Study Objectives -- 4.6.2 Data Description -- 4.6.3 Initial Graphs -- 4.7 Scatterplot Matrices-Continued -- 4.8 Data Transformations -- 4.9 Life Expectancy Example-Continued -- 4.10 Color Vision -- 4.11 Exercises -- 4.A Appendix: R Graphics -- 4.A.1 Cartesian Products -- 4.A.2 Trellis Paradigm -- 4.A.3 Implementation of Trellis Graphics -- 4.A.4 Coordinating Sets of Related Graphs -- 4.A.5 Cartesian Product of Model Parameters -- 4.A.6 Examples of Cartesian Products -- 4.A.7 latticeExtra-Extra Graphical Utilities Basedon Lattice -- 4.B Appendix: Graphs Used in This Book -- 4.B.1 Structured Sets of Graphs -- 4.B.2 Combining Panels -- 4.B.3 Regression Diagnostics.
4.B.4 Graphs Requiring Multiple Calls to xyplot -- 4.B.5 Asymmetric Roles for the Row and Column Sets -- 4.B.6 Rotated Plots -- 4.B.7 Squared Residual Plots -- 4.B.8 Adverse Events Dotplot -- 4.B.9 Microplots -- 4.B.10 Alternate Presentations -- 5 Introductory Inference -- 5.1 Normal (z) Intervals and Tests -- 5.1.1 Test of a Hypothesis Concerning the Mean of a Population Having Known Standard Deviation -- 5.1.2 Confidence Intervals for Unknown Population Proportion p -- 5.1.3 Tests on an Unknown Population Proportion p -- 5.1.4 Example-One-Sided Hypothesis Test Concerning a Population Proportion -- 5.2 t-Intervals and Tests for the Mean of a Population Having Unknown Standard Deviation -- 5.2.1 Example-Inference on a Population Mean μ -- 5.3 Confidence Interval on the Variance or Standard Deviation of a Normal Population -- 5.4 Comparisons of Two Populations Based on IndependentSamples -- 5.4.1 Confidence Intervals on the Difference Between Two Population Proportions -- 5.4.2 Confidence Interval on the Difference Between Two Means -- 5.4.3 Tests Comparing Two Population Means When the Samples Are Independent -- 5.4.4 Comparing the Variances of Two Normal Populations -- 5.5 Paired Data -- 5.5.1 Example-t-test on Matched Pairs of Means -- 5.6 Sample Size Determination -- 5.6.1 Sample Size for Estimation -- 5.6.2 Sample Size for Hypothesis Testing -- 5.7 Goodness of Fit -- 5.7.1 Chi-Square Goodness-of-Fit Test -- 5.7.2 Example-Test of Goodness-of-Fit to a Discrete Uniform Distribution -- 5.7.3 Example-Test of Goodness-of-Fit to a Binomial Distribution -- 5.8 Normal Probability Plots and Quantile Plots -- 5.8.1 Normal Probability Plots -- 5.8.2 Example-Comparing t-Distributions -- 5.9 Kolmogorov-Smirnov Goodness-of-Fit Tests -- 5.9.1 Example-Kolmogorov-Smirnov Goodness-of-Fit Test -- 5.10 Maximum Likelihood -- 5.10.1 Maximum Likelihood Estimation.
5.10.2 Likelihood Ratio Tests -- 5.11 Exercises -- 6 One-Way Analysis of Variance -- 6.1 Example-Catalyst Data -- 6.2 Fixed Effects -- 6.3 Multiple Comparisons-Tukey Procedure for Comparing All Pairs of Means -- 6.4 Random Effects -- 6.5 Expected Mean Squares (EMS) -- 6.6 Example-Catalyst Data-Continued -- 6.7 Example-Batch Data -- 6.8 Example-Turkey Data -- 6.8.1 Study Objectives -- 6.8.2 Data Description -- 6.8.3 Analysis -- 6.8.4 Interpretation -- 6.8.5 Specification of Analysis -- 6.9 Contrasts -- 6.9.1 Mathematics of Contrasts -- 6.9.2 Scaling -- 6.9.2.1 Absolute-Sum-2 Scaling -- 6.9.2.2 Normalized Scaling -- 6.9.2.3 Integer Scaling -- 6.10 Tests of Homogeneity of Variance -- 6.11 Exercises -- 6.A Appendix: Computation for the Analysis of Variance -- 6.B Object Oriented Programming -- 7 Multiple Comparisons -- 7.1 Multiple Comparison Procedures -- 7.1.1 Bonferroni Method -- 7.1.2 Tukey Procedure for All Pairwise Comparisons -- 7.1.3 The Dunnett Procedure for Comparing One Mean with All Others -- 7.1.3.1 Computing Note-Specifying the Alternative Hypothesis -- 7.1.4 Simultaneously Comparing All Possible Contrasts Scheffé and Extended Tukey -- 7.1.4.1 The Scheffé Procedure -- 7.1.4.2 Scheffé Intervals with the Turkey Data -- 7.1.4.3 The Extended Tukey Procedure -- 7.2 The Mean-Mean Multiple Comparisons Display (MMC Plot) -- 7.2.1 Difficulties with Standard Displays -- 7.2.2 Hsu and Peruggia's Mean-Mean Scatterplot -- 7.2.2.1 Construction of the Mean-Mean Scatterplot -- 7.2.2.2 Interpretation of the Mean-Mean Scatterplot -- 7.2.3 Extensions of the Mean-Mean Display to Arbitrary Contrasts -- 7.2.3.1 Scaling -- 7.2.3.2 Contrasts -- 7.2.3.3 Labeling -- 7.2.3.4 q Multipliers -- 7.2.4 Display of an Orthogonal Basis Set of Contrasts -- 7.2.5 Hsu and Peruggia's Pulmonary Example -- 7.3 Exercises -- 8 Linear Regression by Least Squares -- 8.1 Introduction.
8.2 Example-Body Fat Data -- 8.2.1 Study Objectives -- 8.2.2 Data Description -- 8.2.3 Data Input -- 8.2.4 One-X Analysis -- 8.3 Simple Linear Regression -- 8.3.1 Algebra -- 8.3.2 Normal Distribution Theory -- 8.3.3 Calculations -- 8.3.4 Residual Mean Square in Regression Printout -- 8.3.5 New Observations -- 8.4 Diagnostics -- 8.5 ECDF of Centered Fitted Values and Residuals -- 8.6 Graphics -- 8.7 Exercises -- 9 Multiple Regression-More Than One Predictor -- 9.1 Regression with Two Predictors-Least-Squares Geometry -- 9.2 Multiple Regression-Two-X Analysis -- 9.3 Multiple Regression-Algebra -- 9.3.1 The Hat Matrix and Leverage -- 9.3.2 Geometry of Multiple Regression -- 9.4 Programming -- 9.4.1 Model Specification -- 9.4.2 Printout Idiosyncrasies -- 9.5 Example-Albuquerque Home Price Data -- 9.5.1 Study Objectives -- 9.5.2 Data Description -- 9.5.3 Data Input -- 9.6 Partial F-Tests -- 9.7 Polynomial Models -- 9.8 Models Without a Constant Term -- 9.9 Prediction -- 9.10 Example-Longley Data -- 9.10.1 Study Objectives -- 9.10.2 Data Description -- 9.10.3 Discussion -- 9.11 Collinearity -- 9.12 Variable Selection -- 9.12.1 Manual Use of the Stepwise Philosophy -- 9.12.2 Automated Stepwise Regression -- 9.12.3 Automated Stepwise Modeling of the Longley Data -- 9.13 Residual Plots -- 9.13.1 Partial Residuals -- 9.13.2 Partial Residual Plots -- 9.13.3 Partial Correlation -- 9.13.4 Added Variable Plots -- 9.13.5 Interpretation of Residual Plots -- 9.13.5.1 Response Variable Against Each of the Predictors -- 9.13.5.2 Residuals Against Each of the Predictors -- 9.13.5.3 Partial Residuals -- 9.13.5.4 Partial Residual Plots -- 9.13.5.5 Added Variable Plots -- 9.14 Example-U.S. Air Pollution Data -- 9.15 Exercises -- 9.A Appendix: Computation for Regression Analysis -- 10 Multiple Regression-Dummy Variables, Contrasts, and Analysis of Covariance.
10.1 Dummy (Indicator) Variables.
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| Record Nr. | UNINA-9910300246303321 |
Info
SAS for finance : forecasting and data analysis techniques with real-world examples to build powerful financial models / / Harish Gulati
