Statistical Methods in the Biological and Health Sciences
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Milton's Statistical Methods in the Biological and Health Sciences offers comprehensive coverage for the applied statistics course for health and bio-related majors. This course focuses primarily on developing basic statistical techniques and relevant applications within a framework that addresses the needs of these specific audiences.Table of contents:1 Descriptive Methods 1.1 Distribution Tables: Discrete Data Bar Graphs Bivariate Data: Two-Way Tables 1.2 A Quick Look at Distribution: Stem and Leaf Constructing a Simple Stem-and-Leaf Diagram 1.3 Frequency Distributions: Histograms Rules for ...
Milton's Statistical Methods in the Biological and Health Sciences offers comprehensive coverage for the applied statistics course for health and bio-related majors. This course focuses primarily on developing basic statistical techniques and relevant applications within a framework that addresses the needs of these specific audiences.
Table of contents:
1 Descriptive Methods 1.1 Distribution Tables: Discrete Data Bar Graphs Bivariate Data: Two-Way Tables 1.2 A Quick Look at Distribution: Stem and Leaf Constructing a Simple Stem-and-Leaf Diagram 1.3 Frequency Distributions: Histograms Rules for Breaking Data into Classes Cumulative Distribution 1.4 Measures of Location or Central Tendency Sample Mean Sample Median 1.5 Measures of Variability or Dispersion Sample Variance Sample Standard Deviation Sample Range Interquartile Range Finding the Sample Interquartile Range Multiple Data Sets 1.6 Box Plots Constructing a Box Plot 1.7 Handling Grouped Data 2 Introduction to Probability and Counting 2.1 Interpreting Probablilities 2.2 Tree Diagrams and Elementary Genetics Elementary Genetics 2.3 Permutations and Combinations 2.4 Multiplication Principle Guidelines for Using the Multiplication Principle 2.5 Permutations of Indistinguishable Objects 2.6 Combinations 3 Probability and Problem Solving 3.1 Venn Diagrams and the Axioms of Probability Venn Diagrams Axioms of Probability 3.2 General Addition Rule 3.3 Conditional Probability 3.4 Diagnostic Tests and Relative Risk Relative Risk 3.5 Independence 3.6 The Multiplication Rule 3.7 Bayes' Theorem 4 Discrete Random Variables 4.1 Discrete and Continuous Variables 4.2 Discrete Density Functions and Expectation Expectation 4.3 Cumulative Distribution Function 4.4 Binomial Distribution Expected Value and Variance: Binomial Calculating Binomial Probabilities: Cumulative Distribution 4.5 Poisson Distribution 5 Continuous Random Variables 5.1 Continuous Random Variables Expectation 5.2 Cumulative Distribution Function 5.3 Normal Distribution Properties of Normal Curves Standard Normal Distribution Standardization 5.4 Normal Probability Rule and Medical Tables 6 Inferences on the Mean 6.1 Random Sampling and Randomization Simple Random Sampling Randomization 6.2 Point Estimation of the Mean and Introduction to Interval Estimation: Central Limit Theorem Interval Estimation Central Limit Theorem 6.3 Confidence Interval on the Population Mean and the T Distribution Properties of T Random Variables 6.4 Introduction to Hypothesis Testing 6.5 Testing Hypotheses on the Population Mean: T Test Preset Alpha Values 6.6 Sample Size: Confidence Intervals and Power Sample Size: Hypothesis Testing 7 Chi-Squared Distribution and Inferences on the Variance 7.1 Chi-Squared Distribution and Interval Estimation of the Population Variance Confidence Interval on s2 7.2 Testing Hypotheses on the Population Variance 8 Inferences on Proportions 8.1 Point Estimation 8.2 Interval Estimation of p 8.3 Sample Size for Estimating p 8.4 Hypothesis Testing on p 8.5 Comparing Two Proportions: Estimation Confidence Interval on the Difference in Two Proportions 8.6 Comparing Two Proportions: Hypothesis Testing Testing That the Null Value Is Zero: Pooled Test 9 Comparing Two Means and Two Variances 9.1 Comparing Two Means and Two Variances 9.2 Comparing Variances: F Distribution Rule of Thumb Variance Comparison F Test for Comparing Variances: F Distribution 9.3 Inferences on m1 - m2: Pooled T Interval Estimation of m1 - m2 Pooled T Tests 9.4 Inferences on m1 - m2: Unequal Variances 9.5 Inferences on m1 - m2: Paired T Paired T Test 10 k-Sample Procedures: Introduction to Design 10.1 One-Way Classification, Completely Random Design with Fixed Effects Data Format and Notation 10.2 Paired and Multiple Comparisons Bonferroni T Tests: Paired Comparisons Duncan's Multiple Range Test A Note on Computing 10.3 Random Effects 10.4 Randomized Complete Blocks Data Format and Notation Testing HO: m1. = m2. = @ @ @ = mk. Effectiveness of Blocking Paired and Multiple Comparisons A Note on Computing 10.5 Factorial Experiments Data Format and Notation Testing Main Effects and Interaction Multiple and Paired Comparisons A Note on Computing 11 Regression and Correlation 11.1 Introduction To Simple Linear Regression 11.2 Method of Least Squares Estimating an Individual Response A Note on Computing 11.3 Introduction to Correlation Estimating r 11.4 Evaluating the Strength of the Linear Relationship Coefficient of Determination Analysis of Variance A Note on Computing 11.5 Confidence Interval Estimation 11.6 Multiple Regression 12 Categorical Data 12.1 2 ´ 2 Contingency Tables Test of Independence Test of Homogeneity 12.2 r ´ c Contingency Tables 13 Some Additional Procedures and Distribution-Free Alternatives 13.1 Testing for Normality: The Lilliefors Test 13.2 Tests of Location: One Sample Sign Test for Median Wilcoxon Signed-Rank Test 13.3 Tests of Location: Paired Data Sign Test for Median Difference Wilcoxon Signed-Rank Test: Paired Data 13.4 Tests of Location: Unmatched Data Wilcoxon Rank-Sum Test 13.5 Kruskal-Wallis k-Sample Test for Location: Unmatched Data Kruskal-Wallis k-Sample Test 13.6 Friedman k-Sample Test for Location: Matched Data Friedman Test 13.7 Correlation Spearman's Rank Correlation Coefficient 13.8 Bartlett's Test for Equality of Variances 13.9 Normal Approximations 13.10 A Small Sample Test on Proportions Appendix A Summation Notation and Rules for Expectation and Variance Summation Notation Rules for Expectation and Variance Appendix B Statistical Tables References Answers to Odd-Numbered Problems Index
Table of contents:
1 Descriptive Methods 1.1 Distribution Tables: Discrete Data Bar Graphs Bivariate Data: Two-Way Tables 1.2 A Quick Look at Distribution: Stem and Leaf Constructing a Simple Stem-and-Leaf Diagram 1.3 Frequency Distributions: Histograms Rules for Breaking Data into Classes Cumulative Distribution 1.4 Measures of Location or Central Tendency Sample Mean Sample Median 1.5 Measures of Variability or Dispersion Sample Variance Sample Standard Deviation Sample Range Interquartile Range Finding the Sample Interquartile Range Multiple Data Sets 1.6 Box Plots Constructing a Box Plot 1.7 Handling Grouped Data 2 Introduction to Probability and Counting 2.1 Interpreting Probablilities 2.2 Tree Diagrams and Elementary Genetics Elementary Genetics 2.3 Permutations and Combinations 2.4 Multiplication Principle Guidelines for Using the Multiplication Principle 2.5 Permutations of Indistinguishable Objects 2.6 Combinations 3 Probability and Problem Solving 3.1 Venn Diagrams and the Axioms of Probability Venn Diagrams Axioms of Probability 3.2 General Addition Rule 3.3 Conditional Probability 3.4 Diagnostic Tests and Relative Risk Relative Risk 3.5 Independence 3.6 The Multiplication Rule 3.7 Bayes' Theorem 4 Discrete Random Variables 4.1 Discrete and Continuous Variables 4.2 Discrete Density Functions and Expectation Expectation 4.3 Cumulative Distribution Function 4.4 Binomial Distribution Expected Value and Variance: Binomial Calculating Binomial Probabilities: Cumulative Distribution 4.5 Poisson Distribution 5 Continuous Random Variables 5.1 Continuous Random Variables Expectation 5.2 Cumulative Distribution Function 5.3 Normal Distribution Properties of Normal Curves Standard Normal Distribution Standardization 5.4 Normal Probability Rule and Medical Tables 6 Inferences on the Mean 6.1 Random Sampling and Randomization Simple Random Sampling Randomization 6.2 Point Estimation of the Mean and Introduction to Interval Estimation: Central Limit Theorem Interval Estimation Central Limit Theorem 6.3 Confidence Interval on the Population Mean and the T Distribution Properties of T Random Variables 6.4 Introduction to Hypothesis Testing 6.5 Testing Hypotheses on the Population Mean: T Test Preset Alpha Values 6.6 Sample Size: Confidence Intervals and Power Sample Size: Hypothesis Testing 7 Chi-Squared Distribution and Inferences on the Variance 7.1 Chi-Squared Distribution and Interval Estimation of the Population Variance Confidence Interval on s2 7.2 Testing Hypotheses on the Population Variance 8 Inferences on Proportions 8.1 Point Estimation 8.2 Interval Estimation of p 8.3 Sample Size for Estimating p 8.4 Hypothesis Testing on p 8.5 Comparing Two Proportions: Estimation Confidence Interval on the Difference in Two Proportions 8.6 Comparing Two Proportions: Hypothesis Testing Testing That the Null Value Is Zero: Pooled Test 9 Comparing Two Means and Two Variances 9.1 Comparing Two Means and Two Variances 9.2 Comparing Variances: F Distribution Rule of Thumb Variance Comparison F Test for Comparing Variances: F Distribution 9.3 Inferences on m1 - m2: Pooled T Interval Estimation of m1 - m2 Pooled T Tests 9.4 Inferences on m1 - m2: Unequal Variances 9.5 Inferences on m1 - m2: Paired T Paired T Test 10 k-Sample Procedures: Introduction to Design 10.1 One-Way Classification, Completely Random Design with Fixed Effects Data Format and Notation 10.2 Paired and Multiple Comparisons Bonferroni T Tests: Paired Comparisons Duncan's Multiple Range Test A Note on Computing 10.3 Random Effects 10.4 Randomized Complete Blocks Data Format and Notation Testing HO: m1. = m2. = @ @ @ = mk. Effectiveness of Blocking Paired and Multiple Comparisons A Note on Computing 10.5 Factorial Experiments Data Format and Notation Testing Main Effects and Interaction Multiple and Paired Comparisons A Note on Computing 11 Regression and Correlation 11.1 Introduction To Simple Linear Regression 11.2 Method of Least Squares Estimating an Individual Response A Note on Computing 11.3 Introduction to Correlation Estimating r 11.4 Evaluating the Strength of the Linear Relationship Coefficient of Determination Analysis of Variance A Note on Computing 11.5 Confidence Interval Estimation 11.6 Multiple Regression 12 Categorical Data 12.1 2 ´ 2 Contingency Tables Test of Independence Test of Homogeneity 12.2 r ´ c Contingency Tables 13 Some Additional Procedures and Distribution-Free Alternatives 13.1 Testing for Normality: The Lilliefors Test 13.2 Tests of Location: One Sample Sign Test for Median Wilcoxon Signed-Rank Test 13.3 Tests of Location: Paired Data Sign Test for Median Difference Wilcoxon Signed-Rank Test: Paired Data 13.4 Tests of Location: Unmatched Data Wilcoxon Rank-Sum Test 13.5 Kruskal-Wallis k-Sample Test for Location: Unmatched Data Kruskal-Wallis k-Sample Test 13.6 Friedman k-Sample Test for Location: Matched Data Friedman Test 13.7 Correlation Spearman's Rank Correlation Coefficient 13.8 Bartlett's Test for Equality of Variances 13.9 Normal Approximations 13.10 A Small Sample Test on Proportions Appendix A Summation Notation and Rules for Expectation and Variance Summation Notation Rules for Expectation and Variance Appendix B Statistical Tables References Answers to Odd-Numbered Problems Index