Biostatistics for Oral Healthcare -- Contents -- Preface -- 1 Introduction -- 1.1 What Is Biostatistics? -- 1.2 Why Do I Need Statistics? -- 1.3 How Much Mathematics Do I Need? -- 1.4 How Do I Study Statistics? -- 1.5 Reference -- 2 Summarizing Data and Clinical Trials -- 2.1 Raw Data and Basic Terminology -- 2.2 The Levels of Measurements -- 2.3 Frequency Distributions -- 2.3.1 Frequency Tables -- 2.3.2 Relative Frequency -- 2.4 Graphs -- 2.4.1 Bar Graphs -- 2.4.2 Pie Charts -- 2.4.3 Line Graph -- 2.4.4 Histograms -- 2.4.5 Stem and Leaf Plots -- 2.5 Clinical Trials and Designs -- 2.6 Confounding Variables -- 2.7 Exercises -- 2.8 References -- 3 Measures of Central Tendency, Dispersion, and Skewness -- 3.1 Introduction -- 3.2 Mean -- 3.3 Weighted Mean -- 3.4 Median -- 3.5 Mode -- 3.6 Geometric Mean -- 3.7 Harmonic Mean -- 3.8 Mean and Median of Grouped Data -- 3.9 Mean of Two or More Means -- 3.10 Range -- 3.11 Percentiles and Interquartile Range -- 3.12 Box-Whisker Plot -- 3.13 Variance and Standard Deviation -- 3.14 Coefficient of Variation -- 3.15 Variance of Grouped Data -- 3.16 Skewness -- 3.17 Exercises -- 3.18 References -- 4 Probability -- 4.1 Introduction -- 4.2 Sample Space and Events -- 4.3 Basic Properties of Probability -- 4.4 Independence and Mutually Exclusive Events -- 4.5 Conditional Probability -- 4.6 Bayes Theorem -- 4.7 Rates and Proportions -- 4.7.1 Prevalence and Incidence -- 4.7.2 Sensitivity and Specificity -- 4.7.3 Relative Risk and Odds Ratio -- 4.8 Exercises -- 4.9 References -- 5 Probability Distributions -- 5.1 Introduction -- 5.2 Binomial Distribution -- 5.3 Poisson Distribution -- 5.4 Poisson Approximation to Binomial Distribution -- 5.5 Normal Distribution -- 5.5.1 Properties of Normal 5.5 NORMAL DISTRIBUTION Distribution -- 5.5.2 Standard Normal Distribution -- 5.5.3 Using Normal Probability Table.

5.5.4 Further Applications of Normal Probability -- 5.5.5 Finding the (1-a) 100th Percentiles -- 5.5.6 Normal Approximation to the Binomial Distribution -- 5.6 Exercises -- 5.7 References -- 6 Sampling Distributions -- 6.1 Introduction -- 6.2 Sampling Distribution of the Mean -- 6.2.1 Standard Error of the Sample Mean -- 6.2.2 Central Limit Theorem -- 6.3 Student t Distribution -- 6.4 Exercises -- 6.5 References -- 7 Confidence Intervals and Sample Size -- 7.1 Introduction -- 7.2 Confidence Intervals for the Mean µ and Sample Size n When σ Is Known -- 7.3 Confidence Intervals for the Mean µ and Sample Size n When σ Is Not Known -- 7.4 Confidence Intervals for the Binomial Parameter p -- 7.5 Confidence Intervals for the Variances and Standard Deviations -- 7.6 Exercises -- 7.7 References -- 8 Hypothesis Testing: One-Sample Case -- 8.1 Introduction -- 8.2 Concepts of Hypothesis Testing -- 8.3 One-Tailed Z Test of the Mean of a Normal Distribution When σ2 Is Known -- 8.4 Two-Tailed Z Test of the Mean of a Normal Distribution When σ2 Is Known -- 8.5 t Test of the Mean of a Normal Distribution -- 8.6 The Power of a Test and Sample Size -- 8.7 One-Sample Test for a Binomial Proportion -- 8.8 One-Sample Х2 Test for the Variance of a Normal Distribution -- 8.9 Exercises -- 8.10 References -- 9 Hypothesis Testing: Two-Sample Case -- 9.1 Introduction -- 9.2 Two-Sample Z Test for Comparing Two Means -- 9.3 Two-Sample t Test for Comparing Two Means with Equal Variances -- 9.4 Two-Sample t Test for Comparing Two Means with Unequal Variances -- 9.5 The Paired t Test -- 9.6 Z Test for Comparing Two Binomial Proportions -- 9.7 The Sample Size and Power of a Two-Sample Test -- 9.7.1 Estimation of Sample Size -- 9.7.2 The Power of a Two-Sample Test -- 9.8 The F Test for the Equality of Two Variances -- 9.9 Exercises -- 9.10 References -- 10 Categorical Data Analysis.

10.1 Introduction -- 10.2 2 × 2 Contingency Table -- 10.3 r × c Contingency Table -- 10.4 The Cochran-Mantel-Haenszel Test -- 10.5 The McNemar Test -- 10.6 The Kappa Statistic -- 10.7 Х2 Goodness-of-Fit Test -- 10.8 Exercises -- 10.9 References -- 11 Regression Analysis and Correlation -- 11.1 Introduction -- 11.2 Simple Linear Regression -- 11.2.1 Description of Regression Model -- 11.2.2 Estimation of Regression Function -- 11.2.3 Aptness of a Model -- 11.3 Correlation Coefficient -- 11.3.1 Significance Test of Correlation Coefficient -- 11.4 Coefficient of Determination -- 11.5 Multiple Regression -- 11.6 Logistic Regression -- 11.6.1 The Logistic Regression Model -- 11.6.2 Fitting the Logistic Regression Model -- 11.7 Multiple Logistic Regression Model -- 11.8 Exercises -- 11.9 References -- 12 One-way Analysis of Variance -- 12.1 Introduction -- 12.2 Factors and Factor Levels -- 12.3 Statement of the Problem and Model Assumptions -- 12.4 Basic Concepts in ANOVA -- 12.5 F Test for Comparison of k Population Means -- 12.6 Multiple Comparison Procedures -- 12.6.1 Least Significant Difference Method -- 12.6.2 Bonferroni Approach -- 12.6.3 Scheff'é's Method -- 12.6.4 Tukey's Procedure -- 12.7 One-Way ANOVA Random Effects Model -- 12.8 Test for Equality of k Variances -- 12.8.1 Bartlett's Test -- 12.8.2 Hartley's Test -- 12.9 Exercises -- 12.10 References -- 13 Two-way Analysis of Variance -- 13.1 Introduction -- 13.2 General Model -- 13.3 Sum of Squares and Degrees of Freedom -- 13.4 F Tests -- 13.5 Repeated Measures Design -- 13.5.1 Advantages and Disadvantages -- 13.6 Exercises -- 13.7 References -- 14 Non-parametric Statistics -- 14.1 Introduction -- 14.2 The Sign Test -- 14.3 The Wilcoxon Rank Sum Test -- 14.4 The Wilcoxon Signed Rank Test -- 14.5 The Median Test -- 14.6 The Kruskal-Wallis Rank Tes -- 14.7 The Friedman Test.

14.8 The Permutation Test -- 14.9 The Cochran Test -- 14.10 The Squared Rank Test for Variance -- 14.11 Spearman's Rank Correlation Coefficient -- 14.12 Exercises -- 14.13 References -- 15 Survival Analysis -- 15.1 Introduction -- 15.2 Person-Time Method and Mortality Rate -- 15.3 Life Table Analysis -- 15.4 Hazard Function -- 15.5 Kaplan-Meier Product Limit Estimator -- 15.6 Comparing Survival Functions -- 15.6.1 Gehan Generalized Wilcoxon Test -- 15.6.2 The Log Rank Test -- 15.6.3 The Mantel-Haenszel Test -- 15.7 Piecewise Exponential Estimator (PEXE) -- 15.7.1 Small Sample Illustration -- 15.7.2 General Description of PEXE -- 15.7.3 An Example -- 15.7.4 Properties of PEXE and Comparisons with Kaplan-Meier Estimator -- 15.8 Exercises -- 15.9 References -- Appendix -- Solutions to Selected Exercises -- Table A Table of Random Numbers -- Table B Binomial Probabilities -- Table C Poisson Probabilities -- Table D Standard Normal Probabilities -- Table E Percentiles of the t Distribution -- Table F Percentiles of the χ2 Distribution -- Table G Percentiles of the F Distribution -- Table H A Guide to Methods of Statistical Inference -- Index.