Biostatistics for Oral Healthcare

Biostatistics for Oral Healthcare offers students, practitioners and instructors alike a comprehensive guide to mastering biostatistics and their application to oral healthcare. Drawing on situations and methods from dentistry and oral healthcare, this book will provide 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.

Jay S. Kim, Ph.D., is Professor of Biostatistics at Loma Linda University, CA. A specialist in this area, he has been teaching biostatistics since 1977 to students in public health, medical school, and dental school. Currently his primary responsibility is teaching biostatistics courses to dental hygiene students, pre-doctoral dental students and students in advanced dental education. He also collaborates with the faculty and students on a variety of research projects.

Ronald J. Dailey, Ph.D., M.A., is the Associate Dean for Academic Affairs at Loma Linda University School of Dentistry. He has taught dental, dental hygiene and college students for the past 32 years.

Introduction
What Is Biostatistics?
Why Do I Need Statistics?
How Much Mathematics Do I Need?
How to Study Statistics?
Reference
Summarizing Data
Raw Data and Basic Terminology
The Levels of Measurements
Frequency DistributionsFrequency TablesRelative Frequency
GraphsBar GraphsPie ChartsLine GraphsHistogramsStem and Leaf Plots
Clinical Trials
Confounding Variables
Exercises
References
Measures of Central Tendency, Dispersion, and Skewness
Introduction
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
Exercises
References
Probability
Introduction
Sample Space and Events
Basic Properties of Probability
Independence and Mutually Exclusive Events
Conditional Probability
Bayes Theorem
Rates and ProportionsPrevalence and IncidenceSensitivity and SpecificityRelative Risk and Odds Ratio
Exercises
References
Probability Distributions
Introduction
Binomial Distribution
Poisson Distribution
Poisson Approximation to Binomial Distribution
Normal DistributionProperties of Normal DistributionsStandard Normal DistributionUsing Normal Probability TableFurther Applications of Normal ProbabilityNormal Approximation to the Binomial Distribution
Exercises
References
Sampling Distributions
Introduction
Sampling Distribution of the MeanStandard Error of the Sample MeanCentral Limit Theorem
Student''s t Distribution
Exercises
References
Confidence Intervals and Sample Size
Introduction
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 p
Confidence Intervals for the Variances and Standard Deviations
Exercises
References
Hypothesis Testing: One Sample Case
Introduction
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
t 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
Exercises
References
Hypothesis Testing: Two-Sample Case
Introduction
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 TestEstimation of a Sample SizeThe Power of a Two Sample Test
The F Test for the Equality of Two Variances
Exercises
References
Categorical Data Analysis
Introduction
2 x 2 Contingency Table
r x c Contingency Table
The Cochran-Mantel-Haenszel Test
The McNemar Test
The Kappa Statistic
Goodness of Fit Test
Exercises
References
Regression Analysis and Correlation
Introduction
Simple Linear RegressionDescription of Regression ModelEstimation of Regression FunctionAptness of a Model
Correlation CoefficientSignificance of Correlation Coefficient
Coefficient of Determination
Multiple Regression
Logistic RegressionThe Logistic Regression ModelFitting the Logistic Regression Model
Multiple Logistic Regression Model
Exercises
References
One-Way Analysis of Variance
Introduction
Factors and Factor Levels
Statement of the Problem and Model Assumptions
Basic Concepts in ANOVA5. F-test for Comparison of k Population Means
Multiple Comparisons Procedures
Least Significant Difference Method
Bonferroni Approach
Scheffe''s Method
Tukey''s Procedure
One-way ANOVA Ran
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Biostatistics for Oral Healthcare

Summary

Author Biography

Table of Contents

Supplemental Materials