Biostatistics with R

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Wayne W. Daniel's Biostatistics (ISBN: 9780471456544) is a standard textbook for a lot of Biostatistics courses around the world. Similar to other books on similar topics, the examples of statistical computing given in Daniel's book haven't cover R. (Even it gives the examples in MINITAB, SAS and SPSS) As biostatistics is the trade and the statistical packages are only tools, one should believe all examples in Daniel's book can be rewritten in R. R, as a free and open source interpreter of S language, deserved some spots in Daniel's book and in the field of Biostatistics as a whole.

This wikibook is a companion to the Tenth Edition of Daniels book. The data sets are available for download from the Wiley Web Site. The instruction for importing these data sets into R will be introduced in chapter 2.

From Chapter 3 to Chapter 17, this companion book content exactly follows Wayne W. Daniel's book. Each chapter starts with summary for formular in Wayne W. Daniel's Biostatistics (ISBN: 9780471456544) and their expression in R programming. The sections per the chapter also follows Daniel's book content and includes Examples and Exercise in Daniel's book and direction in R programming. The sections which do not have examples and exercise will not be discussed here.

Volunteer Editor

  • This is a wiki project - an open source, free access reference book. Anyone would be welcome and encouraged to contribute anytime, anything (as long as it relates to Biostatistics with R).
  • Hanjin Deviasse Toronto,ON,Canada

Table of Content[edit]

Authors

  1. A Brief Introduction To R
    1. The First Step in R
    2. Data types and basic functions in R
    3. R programming for writing functions
    4. Basic graphics in R
  2. Data Import in R
  3. Introduction to Biostatistics
  4. Descriptive Statistics:Summary of formulas with R
    1. The Ordered Array
    2. Grouped data:The frequency distribution
    3. Descriptive Statistics:Measures of Central Tendency
    4. Descriptive Statistics:Measures of Dispersion
    5. Review Questions and Exercises
  5. Some Basic Probability Concepts:Summary of formulas with R
    1. Calculating the probability of an event
    2. Bayes’ Theorem, Screening Tests Sensitivity, Specificity, and Predictive Value Positive and Negative
    3. Review Questions and Exercises
  6. Probability Distributions:Summary of formulas with R
    1. Probability Distributions of Discrete Variables
    2. The Binomial Distribution
    3. The Poisson Distribution
    4. Continuous Probability Distributions
    5. The Normal Distributions
    6. Normal Distribution Applications
    7. Review Questions and Exercises
  7. Some Important Sampling Distributions:Summary of formulas with R
    1. Distribution of the Sample Mean
    2. Distribution of the Difference Between Two Sample Means
    3. Distribution of the Sample Proportion
    4. Distribution of the Difference Between Two Sample Proportions
    5. Review Questions and Exercises
  8. Estimation:Summary of formulas with R
    1. Confidence Interval for a Population Mean
    2. The t Distribution
    3. Confidence Interval for the Difference Between Two Population Means
    4. Confidence Interval for a Population Proportion
    5. Confidence Interval for the Difference Between Two Population Proportions
    6. Determination of Sample Size for Estimating Means
    7. Determination of Sample Size for Estimating Proportions
    8. Confidence Interval for the Variance of a Normally Distributed Population
    9. Confidence Interval for the Ratio of the Variances of Two Normally Distributed Populations
    10. Review Questions and Exercises
  9. Hypothesis Testing:Summary of formulas with R
    1. Hypothesis Testing: A Single Population Mean
    2. Hypothesis Testing: The Difference Between Two Population Means
    3. Paired Comparisons
    4. Hypothesis Testing: A Single Population Proportion
    5. Hypothesis Testing: The Difference Between Two Population Proportions
    6. Hypothesis Testing: A Single Population Variance
    7. Hypothesis Testing: The Ratio of Two Population Variances
    8. The Type II Error and the Power of a Test
    9. Determining Sample Size to Control Type II Errors
    10. Review Questions and Exercises
  10. Analysis of Variance:Summary of formulas with R
    1. The Completely Randomized Design
    2. The Randomized Complete Block Design
    3. The Repeated Measures Design
    4. The Factorial Experiment
    5. Review Questions and Exercises
  11. Simple Linear Regression and Correlation:Summary for formula with R
    1. The Regression Model
    2. The Sample Regression Equation
    3. Evaluating the Regression Equation
    4. Using the Regression Equation
    5. The Correlation Model
    6. The Correlation Coefficient
    7. Some Precautions
    8. Review Questions and Exercises
  12. Multiple Regression and Correlation:Summary of formulas with R
    1. The Multiple Linear Regression Model
    2. Obtaining the Multiple Regression Equation
    3. Evaluating the Multiple Regression Equation
    4. Using the Multiple Regression Equation
    5. The Multiple Correlation Model
    6. Review Questions and Exercises
  13. Regression Analysis: Some Additional Techniques:Summary of formulas with R
    1. Qualitative Independent Variables
    2. Variable Selection Procedures
    3. Logistic Regression
    4. Review Questions and Exercises
  14. The Chi-Square Distribution and the Analysis of Frequencies:Summary of formulas with R
    1. The Mathematical Properties of the Chi-Square Distribution
    2. Tests of Goodness-of-Fit
    3. Tests of Independence
    4. Tests of Homogeneity
    5. The Fisher Exact Test
    6. Relative Risk, Odds Ratio, and the Mantel–Haenszel Statistic
    7. Review Questions and Exercises
  15. Nonparametric and Distribution-Free Statistics:Summary of formulas with R
    1. Measurement Scales
    2. The Sign Test
    3. The Wilcoxon Signed-Rank Test for Location
    4. The Median Test
    5. The Mann–Whitney Test
    6. The Kolmogorov–Smirnov Goodness-of-Fit Test
    7. The Kruskal–Wallis One-Way Analysis of Variance by Ranks
    8. The Friedman Two-Way Analysis of Variance by Ranks
    9. The Spearman Rank Correlation Coefficient
    10. Nonparametric Regression Analysis
    11. Review Questions and Exercises
  16. Survival Analysis:Summary of formulas with R
    1. Time-to-Event Data and Censoring
    2. The Kaplan–Meier Procedure
    3. Comparing Survival Curves
    4. Cox Regression: The Proportional Hazards Model
    5. Review Questions and Exercises
  17. Vital Statistics:Summary of formulas with R
    1. Death Rates and Ratios
    2. Measures of Fertility
    3. Measures of Morbidity
    4. Review Questions and Exercises
  18. Further reading