{"product_id":"introduction-to-biostatistical-applications-in-health-research-with-microsoft-office-excel-and-r-isbn-9781119722595","title":"Introduction to Biostatistical Applications in Health Research with Microsoft Office Excel and R","description":"\u003cp\u003eThe second edition of \u003ci\u003eIntroduction to Biostatistical Applications in Health Research\u003c\/i\u003e delivers a thorough examination of the basic techniques and most commonly used statistical methods in health research. Retaining much of what was popular with the well-received first edition, the thoroughly revised second edition includes a new chapter on testing assumptions and how to evaluate whether those assumptions are satisfied and what to do if they are not.\u003c\/p\u003e \u003cp\u003eThe newest edition contains brand-new code examples for using the popular computer language R to perform the statistical analyses described in the chapters within. You'll learn how to use Excel to generate datasets for R, which can then be used to conduct statistical calculations on your data.\u003c\/p\u003e \u003cp\u003eThe book also includes a companion website with a new version of BAHR add-in programs for Excel. This new version contains new programs for nonparametric analyses, Student-Newman-Keuls tests, and stratified analyses. Readers will also benefit from coverage of topics like:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExtensive discussions of basic and foundational concepts in statistical methods, including Bayes' Theorem, populations, and samples\u003c\/li\u003e \u003cli\u003eA treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables\u003c\/li\u003e \u003cli\u003eAn examination of bivariable analysis, including regression analysis and correlation analysis\u003c\/li\u003e \u003cli\u003eAn analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for health researchers of all kinds, Introduction to Biostatistical Applications in Health Research also belongs on the bookshelves of anyone who wishes to better understand health research literature. Even those without a great deal of mathematical background will benefit greatly from this text.\u003c\/p\u003e \u003cp\u003ePreface to First Edition xiii\u003c\/p\u003e \u003cp\u003ePreface to Second Edition xv\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Basic Concepts 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Thinking About Chance 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Properties of Probability 4\u003c\/p\u003e \u003cp\u003e1.2 Combinations of Event 8\u003c\/p\u003e \u003cp\u003e1.2.1 Intersections 8\u003c\/p\u003e \u003cp\u003e1.2.2 Unions 13\u003c\/p\u003e \u003cp\u003e1.3 Bayes’ Theorem 16\u003c\/p\u003e \u003cp\u003eChapter Summary 19\u003c\/p\u003e \u003cp\u003eExercises 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Describing Distributions 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Types of Data 26\u003c\/p\u003e \u003cp\u003e2.2 Describing Distributions Graphically 27\u003c\/p\u003e \u003cp\u003e2.2.1 Graphing Discrete Data 27\u003c\/p\u003e \u003cp\u003e2.2.2 Graphing Continuous Data 30\u003c\/p\u003e \u003cp\u003e2.3 Describing Distributions Mathematically 36\u003c\/p\u003e \u003cp\u003e2.3.1 Parameter of Location 37\u003c\/p\u003e \u003cp\u003e2.3.2 Parameter of Dispersion 41\u003c\/p\u003e \u003cp\u003e2.4 Taking Chance into Account 48\u003c\/p\u003e \u003cp\u003e2.4.1 Standard Normal Distribution 49\u003c\/p\u003e \u003cp\u003eChapter Summary 59\u003c\/p\u003e \u003cp\u003eExercises 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Examining Samples 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Nature of Samples 66\u003c\/p\u003e \u003cp\u003e3.2 Estimation 67\u003c\/p\u003e \u003cp\u003e3.2.1 Point Estimates 67\u003c\/p\u003e \u003cp\u003e3.2.2 The Sampling Distribution 73\u003c\/p\u003e \u003cp\u003e3.2.3 Interval Estimates 78\u003c\/p\u003e \u003cp\u003e3.3 Hypothesis Testing 82\u003c\/p\u003e \u003cp\u003e3.3.1 Relationship Between Interval Estimation and Hypothesis Testing 89\u003c\/p\u003e \u003cp\u003eChapter Summary 91\u003c\/p\u003e \u003cp\u003eExercises 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Univariable Analyses 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Univariable Analysis of A Continuous Dependent Variable 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Student’s \u003ci\u003et\u003c\/i\u003e-Distribution 103\u003c\/p\u003e \u003cp\u003e4.2 Interval Estimation 106\u003c\/p\u003e \u003cp\u003e4.3 Hypothesis Testing 109\u003c\/p\u003e \u003cp\u003eChapter Summary 113\u003c\/p\u003e \u003cp\u003eExercises 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Univariable Analysis of An Ordinal Dependent Variable 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Nonparametric Methods 120\u003c\/p\u003e \u003cp\u003e5.2 Estimation 123\u003c\/p\u003e \u003cp\u003e5.3 Wilcoxon Signed-Rank Test 124\u003c\/p\u003e \u003cp\u003e5.4 Statistical Power of Nonparametric Tests 128\u003c\/p\u003e \u003cp\u003eChapter Summary 128\u003c\/p\u003e \u003cp\u003eExercises 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Univariable Analysis of A Nominal Dependent Variable 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Distribution of Nominal Data 134\u003c\/p\u003e \u003cp\u003e6.2 Point Estimates 135\u003c\/p\u003e \u003cp\u003e6.2.1 Probabilities 136\u003c\/p\u003e \u003cp\u003e6.2.2 Rates 138\u003c\/p\u003e \u003cp\u003e6.3 Sampling Distributions 142\u003c\/p\u003e \u003cp\u003e6.3.1 Binomial Distribution 143\u003c\/p\u003e \u003cp\u003e6.3.2 Poisson Distribution 146\u003c\/p\u003e \u003cp\u003e6.4 Interval Estimation 149\u003c\/p\u003e \u003cp\u003e6.5 Hypothesis Testing 151\u003c\/p\u003e \u003cp\u003eChapter Summary 155\u003c\/p\u003e \u003cp\u003eExercises 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Bivariable Analyses 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Bivariable Analysis of A Continuous Dependent Variable 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Continuous Independent Variable 163\u003c\/p\u003e \u003cp\u003e7.1.1 Regression Analysis 165\u003c\/p\u003e \u003cp\u003e7.1.2 Correlation Analysis 189\u003c\/p\u003e \u003cp\u003e7.2 Ordinal Independent Variable 207\u003c\/p\u003e \u003cp\u003e7.3 Nominal Independent Variable 207\u003c\/p\u003e \u003cp\u003e7.3.1 Estimating the Difference between the Groups 208\u003c\/p\u003e \u003cp\u003e7.3.2 Taking Chance into Account 209\u003c\/p\u003e \u003cp\u003eChapter Summary 218\u003c\/p\u003e \u003cp\u003eExercises 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Bivariable Analysis of An Ordinal Dependent Variable 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Ordinal Independent Variable 228\u003c\/p\u003e \u003cp\u003e8.2 Nominal Independent Variable 236\u003c\/p\u003e \u003cp\u003eChapter Summary 241\u003c\/p\u003e \u003cp\u003eExercises 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Bivariable Analysis of A Nominal Dependent Variable 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Continuous Independent Variable 246\u003c\/p\u003e \u003cp\u003e9.1.1 Estimation 247\u003c\/p\u003e \u003cp\u003e9.1.2 Hypothesis Testing 255\u003c\/p\u003e \u003cp\u003e9.2 Nominal Independent Variable 258\u003c\/p\u003e \u003cp\u003e9.2.1 Dependent Variable Not Affected by Time: Unpaired Design 259\u003c\/p\u003e \u003cp\u003e9.2.2 Hypothesis Testing 266\u003c\/p\u003e \u003cp\u003e9.2.3 Dependent Variable Not Affected by Time: Paired Design 277\u003c\/p\u003e \u003cp\u003e9.2.4 Dependent Variable Affected by Time 283\u003c\/p\u003e \u003cp\u003eChapter Summary 286\u003c\/p\u003e \u003cp\u003eExercises 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four Multivariable Analyses 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multivariable Analysis of A Continuous Dependent Variable 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Continuous Independent Variables 296\u003c\/p\u003e \u003cp\u003e10.1.1 Multiple Regression Analysis 297\u003c\/p\u003e \u003cp\u003e10.1.2 Multiple Correlation Analysis 317\u003c\/p\u003e \u003cp\u003e10.2 Nominal Independent Variables 319\u003c\/p\u003e \u003cp\u003e10.2.1 Analysis of Variance 320\u003c\/p\u003e \u003cp\u003e10.2.2 Posterior Testing 331\u003c\/p\u003e \u003cp\u003e10.3 Both Continuous and Nominal Independent Variables 340\u003c\/p\u003e \u003cp\u003e10.3.1 Indicator (Dummy) Variables 341\u003c\/p\u003e \u003cp\u003e10.3.2 Interaction Variables 343\u003c\/p\u003e \u003cp\u003e10.3.3 General Linear Model 348\u003c\/p\u003e \u003cp\u003eChapter Summary 355\u003c\/p\u003e \u003cp\u003eExercises 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Multivariable Analysis of An Ordinal Dependent Variable 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Nonparametric Anova 369\u003c\/p\u003e \u003cp\u003e11.2 Posterior Testing 375\u003c\/p\u003e \u003cp\u003eChapter Summary 380\u003c\/p\u003e \u003cp\u003eExercises 381\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Multivariable Analysis of A Nominal Dependent Variable 385\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Continuous and\/or Nominal Independent Variables 387\u003c\/p\u003e \u003cp\u003e12.1.1 Maximum Likelihood Estimation 387\u003c\/p\u003e \u003cp\u003e12.1.2 Logistic Regression Analysis 389\u003c\/p\u003e \u003cp\u003e12.1.3 Cox Regression Analysis 399\u003c\/p\u003e \u003cp\u003e12.2 Nominal Independent Variables 401\u003c\/p\u003e \u003cp\u003e12.2.1 Stratified Analysis 402\u003c\/p\u003e \u003cp\u003e12.2.2 Relationship Between Stratified Analysis and Logistic Regression 410\u003c\/p\u003e \u003cp\u003e12.2.3 Life Table Analysis 414\u003c\/p\u003e \u003cp\u003eChapter Summary 424\u003c\/p\u003e \u003cp\u003eExercises 427\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Testing Assumptions 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Continuous Dependent Variables 436\u003c\/p\u003e \u003cp\u003e13.1.1 Assuming A Gaussian Distribution 437\u003c\/p\u003e \u003cp\u003e13.1.2 Transforming Dependent Variables 477\u003c\/p\u003e \u003cp\u003e13.1.3 Assuming Equal Variances 485\u003c\/p\u003e \u003cp\u003e13.1.4 Assuming Additive Relationships 494\u003c\/p\u003e \u003cp\u003e13.1.5 Dealing With Outliers 506\u003c\/p\u003e \u003cp\u003e13.2 Nominal Dependent Variables 507\u003c\/p\u003e \u003cp\u003e13.2.1 Assuming a Gaussian Distribution 507\u003c\/p\u003e \u003cp\u003e13.2.2 Assuming Equal Variances 510\u003c\/p\u003e \u003cp\u003e13.2.3 Assuming Additive Relationships 511\u003c\/p\u003e \u003cp\u003e13.3 Independent Variables 511\u003c\/p\u003e \u003cp\u003eChapter Summary 513\u003c\/p\u003e \u003cp\u003eExercises 516\u003c\/p\u003e \u003cp\u003eAppendix A: Flowcharts 521\u003c\/p\u003e \u003cp\u003eAppendix B: Statistical Tables 527\u003c\/p\u003e \u003cp\u003eAppendix C: Standard Distributions 597\u003c\/p\u003e \u003cp\u003eAppendix D: Excel Primer 601\u003c\/p\u003e \u003cp\u003eAppendix E: R Primer 605\u003c\/p\u003e \u003cp\u003eAppendix F: Answers To Odd Exercises 609\u003c\/p\u003e \u003cp\u003eIndex 611\u003c\/p\u003e \"This book provides a good introduction to biostatistics with a lot of medical examples and exercises. It is perfect for those that have basic notions on mathematics, explaining the main formulas necessary for describing, testing and finding out the relationships between data ... the manuscript is very good, comprehensive in information, the chapters are well structured, it includes a great arsenal of examples analysed and described in the field of biostatistics at a basic level. The reader should achieve a solid first step knowledge in the area, both for the statistical concepts and also practical applications.\" \u003cb\u003e– International Society for Clinical Biostatistics News\u003c\/b\u003e \u003cp\u003e\u003cb\u003eROBERT P. HIRSCH, PHD,\u003c\/b\u003e is on the faculty at the Foundation for Advanced Education in the Sciences as well as a Medical Research Consultant with over thirty years of experience. He received his doctorate in Biology at Kansas State University. He was formerly Professor at the George Washington University - Columbian College of Arts \u0026amp; Science where he helped to develop the Epidemiology and Biostatistics Programs.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eLearn more about the foundations of statistical methods in health research with this authoritative new resource\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe second edition of \u003ci\u003eIntroduction to Biostatistical Applications in Health Research\u003c\/i\u003e delivers a thorough examination of the basic techniques and most commonly used statistical methods in health research. Retaining much of what was popular with the well-received first edition, the thoroughly revised second edition includes a new chapter on testing assumptions and how to evaluate whether those assumptions are satisfied and what to do if they are not.\u003c\/p\u003e \u003cp\u003eThe newest edition contains brand-new code examples for using the popular computer language R to perform the statistical analyses described in the chapters within. You'll learn how to use Excel to generate datasets for R, which can then be used to conduct statistical calculations on your data.\u003c\/p\u003e \u003cp\u003eThe book also includes a companion website with a new version of BAHR add-in programs for Excel. This new version contains new programs for nonparametric analyses, Student-Newman-Keuls tests, and stratified analyses. Readers will also benefit from coverage of topics like:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExtensive discussions of basic and foundational concepts in statistical methods, including Bayes??? Theorem, populations, and samples\u003c\/li\u003e \u003cli\u003eA treatment of univariable analysis, covering topics like continuous dependent variables and ordinal dependent variables\u003c\/li\u003e \u003cli\u003eAn examination of bivariable analysis, including regression analysis and correlation analysis\u003c\/li\u003e \u003cli\u003eAn analysis of multivariate calculations in statistics and how testing assumptions, like assuming Gaussian distributions or equal variances, affect statistical outcomes\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for health researchers of all kinds, \u003ci\u003eIntroduction to Biostatistical Applications in Health Research\u003c\/i\u003e also belongs on the bookshelves of anyone who wishes to better understand health research literature. Even those without a great deal of mathematical background will benefit greatly from this text.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989456437477,"sku":"NP9781119722595","price":136.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119722595.jpg?v=1761784172","url":"https:\/\/k12savings.com\/es\/products\/introduction-to-biostatistical-applications-in-health-research-with-microsoft-office-excel-and-r-isbn-9781119722595","provider":"K12savings","version":"1.0","type":"link"}