Graphical Methods for Data Analysis (Statistics)

"This book present graphical methods for analysing data. Some methods are new and some are old, some require a computer and others only paper and pencil; but they are all powerful data analysis tools. In many situations, a set of data? even a large set- can be adequately analysed through graphical methods alone. In most other situations, a few well-chosen graphical displays can significantly enhance numerical statistical analyses."--Provided by publisher Cover -- Title Page -- Copyright Page -- Preface -- Contents -- 1: Introduction -- 1.1: Why Graphics? -- 1.2: What is a Graphical Method for Analyzing Data? -- 1.3: A Summary of the Contents -- 1.4: The Selection and Presentation of Materials -- 1.5: Data Sets -- 1.6: Quality of Graphical Displays -- 1.7: How Should This Book Be Used? -- 2: Portraying the Distribution of a Set of Data -- 2.1: Introduction -- 2.2: Quantile Plots -- 2.3: Symmetry -- 2.4: One-Dimensional Scatter Plots -- 2.5: Box Plots -- 2.6: Histograms -- 2.7: Stem-and-Leaf Diagrams -- 2.8: Symmetry Plots and Transformations -- 2.9: Density Traces -- 2.10: Summary and Discussion -- 2.11: Further Reading -- Exercises -- 3: Comparing Data Distributions -- 3.1: Introduction -- 3.2: Empirical Quantile-Quantile Plots -- 3.3: Collections of Single-Data-Set Displays -- 3.4: Notched Box Plots -- 3.5: Multiple Density Traces -- 3.6: Plotting Ratios and Differences -- 3.7: Summary and Discussion -- 3.8: Further Reading -- Exercises -- 4: Studying Two-Dimensional Data -- 4.1: Introduction -- 4.2: Numerical Summaries are not Enough -- 4.3: Examples -- 4.4: Looking at the Scatter Plots -- 4.5: Studying the Dependence of y on x by Summaries in Vertical Strips -- 4.6: Studying the Dependence of y on x by Smoothing -- 4.7: Studying the Dependence of the Spread of y on x by Smoothing Absolute Values of Residuals -- 4.8: Fighting Repeated Values with Jitter and Sunflowers -- 4.9: Showing Counts with Cellulation and Sunflowers -- 4.10: Two-Dimensional Local Densities and Sharpening -- 4.11: Mathematical Details of Lowess -- 4.12: Summary and Discussion -- 4.13: Further Reading -- Exercises -- 5: Plotting Multivariate Data -- 5.1: Introduction -- 5.2: One-Dimensional and Two-Dimensional Views -- 5.3: Plotting Three Dimensions at Once -- 5.4: Plotting Four and More Dimensions 5.5: Combinations of Basic Methods -- 5.6: First Aid and Transformation -- 5.7: Coding Schemes for Plotting Symbols -- 5.8: Summary and Discussion -- 5.9: Further Reading -- Exercises -- 6 Assessing Distributional Assumptions About Data -- 6.1: Introduction -- 6.2: Theoretical Quantile-Quantile Plots -- 6.3: More on Empirical Quantiles and Theoretical Quantiles -- 6.4: Properties of the Theoretical Quantile-Quantile Plot -- 6.5: Deviations from Straight-Line Patterns -- 6.6: Two Cautions for Interpreting Theoretical Quantile-Quantile Plots -- 6.7: Distributions with Unknown Shape Parameters -- 6.8: Constructing Quantile-Quantile Plots -- 6.9: Adding Variability Information to a Quantile-Quantile Plot -- 6.10: Censored and Grouped Data -- 6.11: Summary and Discussion -- 6.12: Further Reading -- Exercises -- 7: Developing and Assessing Regression Models -- 7.1: Introduction -- 7.2: The Linear Model -- 7.3: Simple Regression -- 7.4: Preliminary Plots -- 7.5: Plots During Regression Fitting -- 7.6: Plots After the Model is Fitted -- 7.7: A Case Study -- 7.8: Some Special Regression Situations -- 7.9: Summary and Discussion -- 7.10: Further Reading -- Exercises -- 8: General Principles and Techniques -- 8.1: Introduction -- 8.2: Overall Strategy and Thought -- 8.3: Visual Perception -- 8.4: General Techniques of Plot Construction -- 8.5: Scales -- References -- Appendix: Tables of Data Sets -- Index Content: Introduction. Portraying the distribution of a set of data. Comparing data distributions. Studying two-dimensional data. Studying multi-dimensional data. Plotting multivariate data. Assessing distributional assumptions data. Developing and assessing regression models. General principles and techniques. References. Appendix: tables of data sets. Index. Graphical methods for analysing data are presented in this encompassing text. Some methods are new and some are old, some require a computer and others only paper and pencil; but they are all powerful data analysis tools. In many situations, a set of data even a large set - can be adequately analysed through graphical methods alone. This book should be of interest to those working in physics, chemistry, business, economics, psychology, sociology, medicine, biology, quality control, engineering, education and any other area in which there is data to be analyzed.