by Jay S. Kim
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
- What Is Biostatistics?
- Why Do I Need Statistics?
- How Much Mathematics Do I Need?
- How to Study Statistics?
2. Summarizing Data
- Raw Data and Basic Terminology
- The Levels of Measurements
- Frequency Distributions
- Graphs
- Clinical Trials
- Confounding Variables
3. Measures of Central Tendency, Dispersion, and Skewness
- Mean
- Weighted Mean
- Median
- Mode
- Geometric Mean
- Harmonic Mean
- Mean and Median of Grouped Data
- Mean of Two or More Means
- Range
- Percentiles and Interquartile Range
- Box-whisker Plot
- Variance and Standard Deviation
- Coefficient of Variation
- Variance of the Grouped Data
- Skewness
4. Probability
- Sample Space and Events
- Basic Properties of Probability
- Independence and Mutually Exclusive Events
- Conditional Probability
- Bayes Theorem
- Rates and Proportions
5. Probability Distributions
- Binomial Distribution
- Poison Distribution
- Poison Approximation to Binomial Distribution
- Normal Distribution
6. Sampling Distributions
- Sampling Distribution of the Mean
- Student's Distribution
7. Confidence Intervals and Sample Size
- Confidence Intervals for the Mean and Sample Size n when Is Known
- Confidence Intervals for the Mean when is Not Known
- Confidence Intervals for the Binomial Parameter
- Confidence Intervals for the Variances and Standard Deviations
8. Hypothesis Testing: One Sample Case
- Concept of Hypothesis Testing.
- One-tailed Z Test of the Mean of a Normal Distribution When Is Known
- Two-tailed Z Test of the Mean of a Normal Distribution When Is Known
- Test of the Mean of a Normal Distribution
- The Power of a Test and Sample Size
- One-Sample Test for a Binomial Proportion
- One-Sample Test for the Variance of a Normal Distribution
9. Hypothesis Testing: Two-Sample Case
- Two Sample Z Test for Comparing Two Means
- Two Sample t Test for Comparing Two Means with Equal Variances
- Two Sample t Test for Comparing Two Means with Unequal Variances
- The Paired t Test
- Z Test for Comparing Two Binomial Proportions
- The Sample Size and Power of a Two Sample Test
- The F Test for the Equality of Two Variances
10. Categorical Data Analysis
- r x c Contingency Table
- The Cochran-Mantel-Haenszel Test
- The McNemar Test
- The Kappa Statistic
- Goodness of Fit Test
11. Regression Analysis and Correlation
- Simple Linear Regression
- Correlation Coefficient
- Coefficient of Determination
- Multiple Regression
- Logistic Regression
- Multiple Logistic Regression Model
12. One-Way Analysis of Variance
- Factors and Factor Levels
- Statement of the Problem and Model Assumptions
- Basic Concepts in ANOVA
- F-test for Comparison of k Population Means
- Multiple Comparisons Procedures
- One-way ANOVA Random Effects Model
- Test for Equality of k Variances
13. Two-Way Analysis of Variance
- General Model
- Sum of Squares and Degrees of Freedom
- F Test
14. Non-Parametric Statistics
- The Sign Test
- The Wilcoxon Rank Sum Test
- The Wilcoxon Signed Rank Test
- The Median Test
- The Kruskal-Wallis Test
- The Friedman Test
- The Permutation Test
- The Cochran Test
- The Squared Rank Test For Variances
- Spearman's Rank Correlation Coefficient
15. Survival Analysis
- Person-Time Method and Mortality Rate
- Life Table Analysis
- Hazard Function
- Kaplan-Meier Product Limit Estimator
- Comparing Survival Functions
- Piecewise Exponential Estimator (PEXE)
Index