Kitchen & bath design principles : elements, form, styles / / Nancy L. Wolford, Ellen Cheever

• Record Nr.: UNINA9910460281503321
• Autore: Wolford Nancy L.
• Pubbl/distr/stampa: Hoboken, New Jersey : , : Wiley, , 2015; ©2015
• ISBN: 1-118-71564-0; 1-118-71565-9
• Edizione: [Second edition.]
• Descrizione fisica: 1 online resource (241 p.)
• Collana: NKBA Professional Resource Library
• Disciplina: 747.7/8
• Soggetti: Kitchens; Bathrooms; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Kitchen & Bath Design Principles; Sponsors; About the National Kitchen & Bath Association; Contents; PREFACE; ACKNOWLEDGMENTS; CHAPTER 1 A BRIEF HISTORY OF KITCHEN AND BATHROOM DESIGN; A Brief History of the Kitchen; A Brief History of the Bathroom; Summary; Review Questions; CHAPTER 2 THE BASICS OF DESIGN: CREATING CUSTOMIZED DESIGN; Skill Is the Executor of a Program; Developing Design Style; Creativity: Is It a Natural Gift, or Can It Be Learned?; Definition of Good Design; Personalizing the Design; Questions to Ask the Client; Product Specifications; Existing Construction; Design InformationDocument the Information Gathered; Final Design Consideration; Summary; Review Questions; CHAPTER 3 THE ELEMENTS OF DESIGN; Line; Horizontal Line; Vertical Line; Curved Line; Diagonal Line; Combining Lines; Shape; Rectangular Shape; Square Shape; Diagonal or Angled Shape; Triangular Shape; Curved or Circular Shapes; Identifying Pattern Preferences; Structural Pattern; Naturalistic Pattern; Stylized Pattern; Geometric Pattern; Abstract Pattern; Combining Patterns; Space; Form; Texture; Color; Hue; Color Categories; Color Attributes; Color Intensity (Chroma); Color Value; Color SchemesColor and Its Emotional Impact; A Final Comment on the ; Importance of Color; Light; Summary; Review Questions; CHAPTER 4 THE PRINCIPLES OF DESIGN; Balance; Symmetrical Balance; Asymmetrical Balance; Radial Balance; Rhythm; Repetition; Alternation; Progression/Gradation; Scale and Proportion; Scale; Proportion; Emphasis: The Focal Point; Area Emphasis; Theme Emphasis; Harmony/Unity; Summary; Review Questions; CHAPTER 5 HISTORIC STYLE INFLUENCES TO THE LATE NINETEENTH CENTURY; Establishing a Historic Theme for a Kitchen or Bathroom Project; Ancient/Classical Period; Midcentury Modern
• Sommario/riassunto: The industry standard, full-color guide to practical kitchen and bath design Kitchen and Bath Design Principles is The National Kitchen and Bath Association's complete guide to uniting function and style in these important rooms. This full-color guide is heavily illustrated, with a revised layout and graphic design that presents information more clearly for visual learners. The updated second edition has been expanded to adhere more closely to the standards of interior design, including new information on unity, proportion, scale, and variety, plus a new glossary to clarify unfamiliar terms. S

Mathematical Statistics with Resampling and R

• Record Nr.: UNINA9910795981403321
• Autore: Chihara Laura M
• Pubbl/distr/stampa: Somerset : , : John Wiley & Sons, Incorporated, , 2011; ©2011
• ISBN: 9781118518953; 9781118029855
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (434 pages)
• Altri autori (Persone): HesterbergTim C
• Disciplina: 310
• Soggetti: Mathematics; Resampling (Statistics); Statistics; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- 1: Data and Case Studies -- 1.1 Case Study: Flight Delays -- 1.2 Case Study: Birth Weights of Babies -- 1.3 Case Study: Verizon Repair Times -- 1.4 Sampling -- 1.5 Parameters and Statistics -- 1.6 Case Study: General Social Survey -- 1.7 Sample Surveys -- 1.8 Case Study: Beer and Hot Wings -- 1.9 Case Study: Black Spruce Seedlings -- 1.10 Studies -- 1.11 Exercises -- 2: Exploratory Data Analysis -- 2.1 Basic Plots -- 2.2 Numeric Summaries -- 2.2.1 Center -- 2.2.2 Spread -- 2.2.3 Shape -- 2.3 Boxplots -- 2.4 Quantiles and Normal Quantile Plots -- 2.5 Empirical Cumulative Distribution Functions -- 2.6 Scatter Plots -- 2.7 Skewness and Kurtosis -- 2.8 Exercises -- 3: Hypothesis Testing -- 3.1 Introduction to Hypothesis Testing -- 3.2 Hypotheses -- 3.3 Permutation Tests -- 3.3.1 Implementation Issues -- 3.3.2 One-sided and Two-sided Tests -- 3.3.3 Other Statistics -- 3.3.4 Assumptions -- 3.4 Contingency Tables -- 3.4.1 Permutation Test for Independence -- 3.4.2 Chi-square Reference Distribution -- 3.5 Chi-square Test of Independence -- 3.6 Test of Homogeneity -- 3.7 Goodness-of-fit: All Parameters Known -- 3.8 Goodness-of-fit: Some Parameters Estimated -- 3.9 Exercises -- 4: ; Sampling Distributions -- 4.1 Sampling Distributions -- 4.2 Calculating Sampling Distributions -- 4.3 The Central Limit Theorem -- 4.3.1 Clt for Binomial Data -- 4.3.2 Continuity Correction for Discrete Random Variables -- 4.3.3 Accuracy of the Central Limit Theorem -- 4.3.4 Clt for Samplingwithout Replacement -- 4.4 Exercises -- 5: The Bootstrap -- 5.1 Introduction to the Bootstrap -- 5.2 The Plug-in Principle -- 5.2.1 Estimating the Population Distribution -- 5.2.2 How Useful Is the Bootstrap Distribution? -- 5.3 Bootstrap Percentile Intervals -- 5.4 Two Sample Bootstrap.; 5.4.1 The Two Independent Populations Assumption -- 5.5 Other Statistics -- 5.6 Bias -- 5.7 Monte Carlo Sampling: the "second Bootstrap Principle" -- 5.8 Accuracy of Bootstrap Distributions -- 5.8.1 Sample Mean: Large Sample Size -- 5.8.2 Sample Mean: Small Sample Size -- 5.8.3 Sample Median -- 5.9 How Many Bootstrap Samples Are Needed? -- 5.10 Exercises -- 6: Estimation -- 6.1 Maximum Likelihood Estimation -- 6.1.1 Maximum Likelihood for Discrete Distributions -- 6.1.2 Maximum Likelihood for Continuous Distributions -- 6.1.3 Maximum Likelihood for Multiple Parameters -- 6.2 Method of Moments -- 6.3 Properties of Estimators -- 6.3.1 Unbiasedness -- 6.3.2 Efficiency -- 6.3.3 Mean Square Error -- 6.3.4 Consistency -- 6.3.5 Transformation Invariance -- 6.4 Exercises -- 7: Classical Inference: Confidence Intervals -- 7.1 Confidence Intervals for Means -- 7.1.1 Confidence Intervals for a Mean, σ Known -- 7.1.2 Confidence Intervals for a Mean, σ Unknown -- 7.1.3 Confidence Intervals for a Difference in Means -- 7.2 Confidence Intervals in General -- 7.2.1 Location and Scale Parameters -- 7.3 One-sided Confidence Intervals -- 7.4 Confidence Intervals for Proportions -- 7.4.1 The Agresti-Coull Interval for a Proportion -- 7.4.2 Confidence Interval for the Difference of Proportions -- 7.5 Bootstrap t Confidence Intervals -- 7.5.1 Comparing Bootstrap t and Formula t Confidence Intervals -- 7.6 Exercises -- 8: Classical Inference: Hypothesis Testing -- 8.1 Hypothesis Tests for Means and Proportions -- 8.1.1 One Population -- 8.1.2 Comparing Two Populations -- 8.2 Type I and Type Ii Errors -- 8.2.1 Type I Errors -- 8.2.2 Type II Errors and Power -- 8.3 More on Testing -- 8.3.1 on Significance -- 8.3.2 Adjustments for Multiple Testing -- 8.3.3 P-values Versus Critical Regions -- 8.4 Likelihood Ratio Tests -- 8.4.1 Simple Hypotheses and the Neyman-pearson Lemma.; 8.4.2 Generalized Likelihood Ratio Tests -- 8.5 Exercises -- 9: Regression -- 9.1 Covariance -- 9.2 Correlation -- 9.3 Least-squares Regression -- 9.3.1 Regression Toward the Mean -- 9.3.2 Variation -- 9.3.3 Diagnostics -- 9.3.4 Multiple Regression -- 9.4 The Simple Linear Model -- 9.4.1 Inference for α and ß -- 9.4.2 Inference for the Response -- 9.4.3 Comments About Assumptions for the Linear Model -- 9.5 Resampling Correlation and Regression -- 9.5.1 Permutation Tests -- 9.5.2 Bootstrap Case Study: Bushmeat -- 9.6 Logistic Regression -- 9.6.1 Inference for Logistic Regression -- 9.7 Exercises -- 10: Bayesian Methods -- 10.1 Bayes' Theorem -- 10.2 Binomial Data, Discrete Prior Distributions -- 10.3 Binomial Data, Continuous Prior Distributions -- 10.4 Continuous Data -- 10.5 Sequential Data -- 10.6 Exercises -- 11: Additional Topics -- 11.1 Smoothed Bootstrap -- 11.1.1 Kernel Density Estimate -- 11.2 Parametric Bootstrap -- 11.3 The Delta Method -- 11.4 Stratified Sampling -- 11.5 Computational Issues in Bayesian Analysis -- 11.6 Monte Carlo Integration -- 11.7 Importance Sampling -- 11.7.1 Ratio Estimate for Importance Sampling -- 11.7.2 Importance Sampling in Bayesian Applications -- 11.8 Exercises -- Appendix A: Review of Probability -- A.1 Basic Probability -- A.2 Mean and Variance -- A.3 The Mean of a Sample of Random ; Variables -- A.4 The Law of Averages -- A.5 The Normal Distribution -- A.6 Sums of Normal Random Variables -- A.7 Higher Moments and the Moment Generating Function -- Appendix B: Probability Distributions -- B.1 The Bernoulli and Binomial Distributions -- B.2 The Multinomial Distribution -- B.3 The Geometric Distribution -- B.4 The Negative Binomial Distribution -- B.5 The Hypergeometric Distribution -- B.6 The Poisson Distribution -- B.7 The Uniform Distribution -- B.8 The Exponential Distribution -- B.9 The Gamma Distribution.; B.10 The Chi-square Distribution -- B.11 The Student's t Distribution -- B.12 The Beta Distribution -- B.13 The f Distribution -- B.14 Exercises -- Appendix C: Distributions Quick Reference -- Solutions to Odd-numbered Exercises -- Bibliography -- Index.
• Sommario/riassunto: This book bridges the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis Calculation of sampling distributions The Central Limit Theorem Monte Carlo sampling Maximum likelihood estimation and properties of estimators Confidence intervals and hypothesis tests Regression Bayesian methods Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.