Biostatistics for Oral Healthcare offers students, practitioners and instructors alike a comprehensive guide to mastering biostatistics and their application to oral healthcare.

Biostatistics for Oral Healthcare provides a thorough treatment of statistical concepts in order to promote in-depth and correct comprehension, supported throughout by technical discussion and a multitude of practical examples.

Contents

Introduction

1. What Is Biostatistics?
2. Why Do I Need Statistics?
3. How Much Mathematics Do I Need?
4. How to Study Statistics?

2. Summarizing Data

1. Raw Data and Basic Terminology
2. The Levels of Measurements
3. Frequency Distributions
4. Graphs
5. Clinical Trials
6. Confounding Variables

3. Measures of Central Tendency, Dispersion, and Skewness

1. Mean
2. Weighted Mean
3. Median
4. Mode
5. Geometric Mean
6. Harmonic Mean
7. Mean and Median of Grouped Data
8. Mean of Two or More Means
9. Range
10. Percentiles and Interquartile Range
11. Box-whisker Plot
12. Variance and Standard Deviation
13. Coefficient of Variation
14. Variance of the Grouped Data
15. Skewness

4. Probability

1. Sample Space and Events
2. Basic Properties of Probability
3. Independence and Mutually Exclusive Events
4. Conditional Probability
5. Bayes Theorem
6. Rates and Proportions

5. Probability Distributions

1. Binomial Distribution
2. Poison Distribution
3. Poison Approximation to Binomial Distribution
4. Normal Distribution

6. Sampling Distributions

1. Sampling Distribution of the Mean
2. Student's Distribution

7. Confidence Intervals and Sample Size

1. Confidence Intervals for the Mean and Sample Size n when Is Known
2. Confidence Intervals for the Mean when is Not Known
3. Confidence Intervals for the Binomial Parameter
4. Confidence Intervals for the Variances and Standard Deviations

8. Hypothesis Testing: One Sample Case

1. Concept of Hypothesis Testing.
2. One-tailed Z Test of the Mean of a Normal Distribution When Is Known
3. Two-tailed Z Test of the Mean of a Normal Distribution When Is Known
4. Test of the Mean of a Normal Distribution
5. The Power of a Test and Sample Size
6. One-Sample Test for a Binomial Proportion
7. One-Sample Test for the Variance of a Normal Distribution

9. Hypothesis Testing: Two-Sample Case

1. Two Sample Z Test for Comparing Two Means
2. Two Sample t Test for Comparing Two Means with Equal Variances
3. Two Sample t Test for Comparing Two Means with Unequal Variances
4. The Paired t Test
5. Z Test for Comparing Two Binomial Proportions
6. The Sample Size and Power of a Two Sample Test
7. The F Test for the Equality of Two Variances

10. Categorical Data Analysis

1. r x c Contingency Table
2. The Cochran-Mantel-Haenszel Test
3. The McNemar Test
4. The Kappa Statistic
5. Goodness of Fit Test

11. Regression Analysis and Correlation

1. Simple Linear Regression
2. Correlation Coefficient
3. Coefficient of Determination
4. Multiple Regression
5. Logistic Regression
6. Multiple Logistic Regression Model

12. One-Way Analysis of Variance

1. Factors and Factor Levels
2. Statement of the Problem and Model Assumptions
3. Basic Concepts in ANOVA
4. F-test for Comparison of k Population Means
5. Multiple Comparisons Procedures
6. One-way ANOVA Random Effects Model
7. Test for Equality of k Variances

13. Two-Way Analysis of Variance

1. General Model
2. Sum of Squares and Degrees of Freedom
3. F Test

14. Non-Parametric Statistics

1. The Sign Test
2. The Wilcoxon Rank Sum Test
3. The Wilcoxon Signed Rank Test
4. The Median Test
5. The Kruskal-Wallis Test
6. The Friedman Test
7. The Permutation Test
8. The Cochran Test
9. The Squared Rank Test For Variances
10. Spearman's Rank Correlation Coefficient

15. Survival Analysis

1. Person-Time Method and Mortality Rate
2. Life Table Analysis
3. Hazard Function
4. Kaplan-Meier Product Limit Estimator
5. Comparing Survival Functions
6. Piecewise Exponential Estimator (PEXE)

Index

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