Testbankszip for study guides

Statistics for the Life Sciences

Statistics for the Life Sciences 5th Edition

By: Myra Samuels Jeff Witmer Andrew Schaffner
ISBN-10: 0321989589
/ ISBN-13: 9780134150413
Edition: 5th Edition
Language: English
				
					=> All chapters included .
=> Download zip file into your device
=> Dedicated support
=> Affordable pricing 

				
			
secure-stripe-payment-logo.png

TEST BANK

$20

Contact

Digital product.

Statistics for the Life Sciences

Contents

Preface

    • Style and Approach
    • Organization
    • Changes to the Fifth Edition
    • Instructor Supplements
    • Student Supplements
    • Special Thanks

Unit I Data and Distributions

    • Chapter 1 Introduction
    • Objectives
    • 1.1 Statistics and the Life Sciences
    • Example 1.1.1 Vaccine for Anthrax
    • Example 1.1.2 Bacteria and Cancer
    • Example 1.1.3 Flooding and ATP
    • Example 1.1.4 MAO and Schizophrenia
    • Example 1.1.5 Food Choice by Insect Larvae
    • Example 1.1.6 Body Size and Energy Expenditure
    • A Look Ahead
    • 1.2 Types of Evidence
    • Example 1.2.1 Lightning and Deafness
    • Example 1.2.2 Sexual Orientation
    • Example 1.2.3 Health and Marriage
    • Example 1.2.4 Toxicity in Dogs
    • Example 1.2.5 Autism
    • Example 1.2.6 Bronchial Asthma
    • Example 1.2.7 Renal Denervation
    • Blinding
    • The Need for Control Groups
    • Example 1.2.8 Clofibrate
    • Example 1.2.9 The Common Cold
    • Example 1.2.10 Diet and Cancer Prevention
    • Historical Controls
    • Example 1.2.11 Coronary Artery Disease
    • Example 1.2.12 Healthcare Trials
    • Exercises 1.2.1–1.2.10
    • 1.3 Random Sampling
    • Samples and Populations
    • Remark
    • Definition of a Simple Random Sample
    • Employing Randomness
    • How to Choose a Random Sample
    • Example 1.3.1
    • Remark
    • Remark
    • Practical Concerns When Random Sampling
    • Nonsimple Random Sampling Methods
    • Example 1.3.2 La Graciosa Thistle
    • Example 1.3.3 Sand Crabs
    • Sampling Error
    • Example 1.3.4 Lengths of Fish
    • Example 1.3.5 Sizes of Nerve Cells
    • Example 1.3.6 Sucrose in Beet Roots
    • Example 1.3.7 Fungus Resistance in Corn
    • Example 1.3.8 Nitrite Metabolism
    • Example 1.3.9 Treatment of Ulcerative Colitis
    • Nonsampling Errors
    • Example 1.3.10 Abortion Funding
    • Example 1.3.11 HIV Testing
    • Example 1.3.12 Sugar and Hyperactivity
    • Exercises 1.3.1–1.3.7
    • Chapter 2 Description of Samples and Populations
    • Objectives
    • 2.1 Introduction
    • Variables
    • Observational Units
    • Notation for Variables and Observations
    • Exercises 2.1.1–2.1.5
    • 2.2 Frequency Distributions
    • Example 2.2.1 Color of Poinsettias
    • Example 2.2.2 School Bags and Neck Pain
    • Example 2.2.3 Infant Mortality
    • Example 2.2.4 Litter Size of Sows
    • Relative Frequency
    • Example 2.2.5 Color of Poinsettias
    • Grouped Frequency Distributions
    • Example 2.2.6 Serum CK
    • Example 2.2.7 Heights of Students
    • Interpreting Areas in a Histogram
    • Shapes of Distributions
    • Example 2.2.8 Microfossils
    • Example 2.2.9 Cell Firing Times
    • Example 2.2.10 Brain Weight
    • Sources of Variation
    • Example 2.2.11 Weights of Seeds
    • Example 2.2.12 Serum ALT
    • Exercises 2.2.1–2.2.9
    • 2.3 Descriptive Statistics: Measures of Center
    • The Median
    • Example 2.3.1 Weight Gain of Lambs
    • Example 2.3.2 Weight Gain of Lambs
    • The Mean
    • Example 2.3.3 Weight Gain of Lambs
    • Example 2.3.4 Weight Gain of Lambs
    • Robustness
    • Example 2.3.5 Weight Gain of Lambs
    • Visualizing the Mean and Median
    • Example 2.3.6 Cricket Singing Times
    • Mean versus Median
    • Exercises 2.3.1–2.3.14
    • 2.4 Boxplots
    • Quartiles and the Interquartile Range
    • Example 2.4.1 Blood Pressure
    • Example 2.4.2 Pulse
    • Outliers
    • Example 2.4.3 Pulse
    • Example 2.4.4 Radish Growth in Light
    • Boxplots for data with no outliers
    • Example 2.4.5 Radish Growth
    • Boxplots for data with Outliers
    • Exercises 2.4.1–2.4.8
    • 2.5 Relationships between Variables
    • Categorical–Categorical Relationships
    • Example 2.5.1 E. Coli Watershed Contamination
    • Example 2.5.2 E. Coli Watershed Contamination
    • Numeric–Categorical Relationships
    • Example 2.5.3 Radish Growth
    • Numeric–Numeric Relationships
    • Example 2.5.4 Whale Selenium
    • Exercises 2.5.1–2.5.4
    • 2.6 Measures of Dispersion
    • The Range
    • Example 2.6.1 Blood Pressure
    • The Standard Deviation
    • Example 2.6.2 Chrysanthemum Growth
    • Example 2.6.3 Chrysanthemum Growth
    • An abbreviation
    • Interpretation of the Definition of s
    • Example 2.6.4 Chrysanthemum Growth
    • Why n − 1?
    • Example 2.6.5 Chrysanthemum Growth
    • Visualizing Measures of Dispersion
    • Example 2.6.6 Daily Gain of Cattle
    • Visualizing the Standard Deviation
    • Example 2.6.7 Daily Gain of Cattle
    • Estimating the SD from a Histogram
    • Example 2.6.8 Pulse after Exercise
    • Comparison of Measures of Dispersion
    • Exercises 2.6.1–2.6.17
    • 2.7 Effect of Transformation of Variables (Optional)
    • Example 2.7.1 Weight
    • Example 2.7.2 Body Temperature
    • How Linear Transformations Affect the Frequency Distribution
    • Example 2.7.3 Body Temperature
    • How Linear Transformations Affect y¯ and s
    • Example 2.7.4 Additive Transformation
    • Other Statistics
    • Nonlinear Transformations
    • Example 2.7.5 Cricket Singing Times
    • Exercises 2.7.1–2.7.6
    • 2.8 Statistical Inference
    • Example 2.8.1 Blood Types
    • Specifying the Population
    • Example 2.8.2 Blood Types
    • Example 2.8.3 Alcohol and MOPEG
    • Describing a Population
    • Proportions
    • Example 2.8.4 Oat Plants
    • Remark
    • Example 2.8.5 Lung Cancer
    • Other Descriptive Measures
    • Example 2.8.6 Tobacco Leaves
    • 2.9 Perspective
    • Parameters and Statistics
    • A Look Ahead
    • Supplementary Exercises 2.S.1–2.S.24
    • Chapter 3 Probability and the Binomial Distribution
    • Objectives
    • 3.1 Probability and the Life Sciences
    • 3.2 Introduction to Probability
    • Basic Concepts
    • Example 3.2.1 Coin Tossing
    • Example 3.2.2 Coin Tossing
    • Example 3.2.3 Sampling Fruitflies
    • Frequency Interpretation of Probability
    • Example 3.2.4 Coin Tossing
    • Example 3.2.5 Coin Tossing
    • Example 3.2.6 Sampling Fruitflies
    • Probability Trees
    • Example 3.2.7 Coin Tossing
    • Combination of Probabilities
    • Example 3.2.8 Sampling Fruitflies
    • Example 3.2.9 Nitric Oxide
    • Hypothetical 1,000
    • Example 3.2.10 Nitric Oxide
    • Example 3.2.11 Medical Testing
    • Example 3.2.12 False Positives
    • Exercises 3.2.1–3.2.8
    • 3.3 Probability Rules (Optional)
    • Basic Rules
    • Example 3.3.1 Blood Type
    • Addition Rules
    • Example 3.3.2 Hair Color and Eye Color
    • Example 3.3.3 Hair Color and Eye Color
    • Example 3.3.4 Hair Color and Eye Color
    • Multiplication Rules
    • Example 3.3.5 Coin Tossing
    • Example 3.3.6 Blood Type
    • Example 3.3.7 Hair Color and Eye Color
    • Example 3.3.8 Hand Size
    • Exercises 3.3.1–3.3.5
    • 3.4 Density Curves
    • Relative Frequency Histograms and Density Curves
    • Example 3.4.1 Blood Glucose
    • Example 3.4.2 Blood Glucose
    • The Continuum Paradox
    • Probabilities and Density Curves
    • Example 3.4.3 Blood Glucose
    • Example 3.4.4 Tree Diameters
    • Exercises 3.4.1–3.4.5
    • 3.5 Random Variables
    • Example 3.5.1 Dice
    • Example 3.5.2 Family Size
    • Example 3.5.3 Medications
    • Example 3.5.4 Heights of Men
    • Mean and Variance of a Random Variable
    • Example 3.5.5 Fish Vertebrae
    • Example 3.5.6 Dice
    • Example 3.5.7 Fish Vertebrae
    • Example 3.5.8 Dice
    • Adding and Subtracting Random Variables (Optional)
    • Example 3.5.9 Temperature
    • Example 3.5.10 Feet to Inches
    • Example 3.5.11 Mass
    • Exercises 3.5.1–3.5.10
    • 3.6 The Binomial Distribution
    • The Independent-Trials Model
    • Example 3.6.1 Albinism
    • Example 3.6.2 Mutant Cats
    • An Example of the Binomial Distribution
    • Example 3.6.3 Albinism
    • The Binomial Distribution Formula
    • Notes on Table 2
    • Example 3.6.4 Mutant Cats
    • Remark
    • Computational Note
    • Example 3.6.5 Sampling Fruitflies
    • Example 3.6.6 Blood Type
    • Example 3.6.7 Blood Type
    • Mean and Standard Deviation of a Binomial
    • Example 3.6.8 Blood Type
    • Applicability of the Binomial Distribution
    • Application to Sampling
    • Contagion
    • Example 3.6.9 Chickenpox
    • Exercises 3.6.1–3.6.12
    • 3.7 Fitting a Binomial Distribution to Data (Optional)
    • Example 3.7.1 Sexes of Children
    • Exercises 3.7.1–3.7.3
    • Supplementary Exercises 3.S.1–3.S.12
    • Chapter 4 The Normal Distribution
    • Objectives
    • 4.1 Introduction
    • Example 4.1.1 Serum Cholesterol
    • Further Examples
    • Example 4.1.2 Blue Jay Bill Length
    • Example 4.1.3 Interspike Times in Nerve Cells
    • Example 4.1.4 Measurement Error
    • 4.2 The Normal Curves
    • 4.3 Areas under a Normal Curve
    • The Standardized Scale
    • Determining Areas for a Normal Curve
    • Example 4.3.1 Lengths of Fish
    • Inverse Reading of Table 3
    • Example 4.3.2 Lengths of Fish
    • Problem-Solving Tip
    • Exercises 4.3.1–4.3.17
    • 4.4 Assessing Normality
    • Example 4.4.1 Serum Cholesterol
    • Example 4.4.2 Moisture Content
    • Normal Quantile Plots
    • How Normal Quantile Plots Work
    • Example 4.4.3 Height of Eleven Women
    • Example 4.4.4 Heights of Eleven Women
    • Making Decisions about Normality
    • Transformations for Nonnormal Data
    • Example 4.4.5 Lentil Growth
    • An Objective Measure of Abnormality: The Shapiro–Wilk Test (optional)
    • Example 4.4.6 Lentil Growth
    • Caution
    • Exercises 4.4.1–4.4.8
    • 4.5 Perspective
    • Supplementary Exercises 4.S.1–4.S.21
    • Chapter 5 Sampling Distributions
    • Objectives
    • 5.1 Basic Ideas
    • Sampling Variability
    • The Meta-Study
    • Example 5.1.1 Rat Blood Pressure
    • Example 5.1.2 Bacterial Growth
    • Example 5.1.3 Knee Replacement
    • Relationship to Statistical Inference
    • Exercises 5.1.1–5.1.5
    • 5.2 The Sample Mean
    • The Sampling Distribution of Y¯
    • Example 5.2.1 Serum Cholesterol
    • Example 5.2.2 Weights of Seeds
    • Remark
    • Dependence on Sample Size
    • Example 5.2.3 Weights of Seeds
    • Populations, Samples, and Sampling Distributions
    • Example 5.2.4 Weights of Seeds
    • Other Aspects of Sampling Variability
    • Example 5.2.5 Weights of Seeds
    • Exercises 5.2.1–5.2.19
    • 5.3 Illustration of the Central Limit Theorem (Optional)
    • Example 5.3.1 Eye Facets
    • Example 5.3.2 Reaction Time
    • Example 5.3.3 Reaction Time
    • Exercises 5.3.1–5.3.3
    • 5.4 The Normal Approximation to the Binomial Distribution (Optional)
    • Remarks
    • Example 5.4.1 Normal Approximation to Binomial
    • The Continuity Correction
    • Example 5.4.2
    • Example 5.4.3
    • Remark
    • Example 5.4.4
    • How Large Must n Be?
    • Exercises 5.4.1–5.4.14
    • 5.5 Perspective
    • Supplementary Exercises 5.S.1–5.S.13
    • Unit I Highlights and Study (Reflections on Chapters 1–5)
    • Reflections on Chapter 1
    • Reflections on Chapters 2–4
    • Reflections on Chapter 5
    • Unit I Summary Exercises

Unit II Inference for Means

    • Chapter 6 Confidence Intervals
    • Objectives
    • 6.1 Statistical Estimation
    • Example 6.1.1 Butterfly Wings
    • 6.2 Standard Error of the Mean
    • Example 6.2.1 Butterfly Wings
    • Standard Error versus Standard Deviation
    • Example 6.2.2 Lamb Birthweights
    • Example 6.2.3 Lamb Birthweights
    • Graphical Presentation of the SE and the SD
    • Example 6.2.4 MAO and Schizophrenia
    • Exercises 6.2.1–6.2.9
    • 6.3 Confidence Interval for μ
    • Confidence Interval for μ: Basic Idea
    • Confidence Interval for μ: Mathematics
    • Student’s t Distribution
    • Confidence Interval for μ: Method
    • Example 6.3.1 Butterfly Wings
    • Example 6.3.2 Butterfly Wings
    • Remark
    • Confidence Intervals and Randomness
    • Example 6.3.3 Blue Jay Bill Length
    • Interpretation of a Confidence Interval
    • Example 6.3.4 Bone Mineral Density
    • Example 6.3.5 Seeds per Fruit
    • Relationship to Sampling Distribution of y¯
    • One-Sided Confidence Intervals
    • Example 6.3.6 Seeds per Fruit—One-Sided, 90%
    • Example 6.3.7 Seeds per Fruit—One-Sided, 95%
    • Exercises 6.3.1–6.3.22
    • 6.4 Planning a Study to Estimate μ
    • Example 6.4.1 Butterfly Wings
    • Exercises 6.4.1–6.4.6
    • 6.5 Conditions for Validity of Estimation Methods
    • Conditions for Validity of the SE Formula
    • Example 6.5.1 Marijuana and Intelligence
    • Example 6.5.2 Canine Anatomy
    • Example 6.5.3 Germination of Spores
    • Conditions for Validity of a Confidence Interval for μ
    • Summary of Conditions
    • Verification of Conditions
    • Example 6.5.4 Sediment Yield
    • Exercises 6.5.1–6.5.8
    • 6.6 Comparing Two Means
    • Notation
    • Standard Error of (γ1¯−γ¯2)
    • Basic Ideas
    • Example 6.6.1 Vital Capacity
    • Example 6.6.2 Vital Capacity
    • Example 6.6.3 Tonsillectomy
    • The Pooled Standard Error (Optional)
    • Example 6.6.4 Vital Capacity
    • Exercises 6.6.1–6.6.9
    • 6.7 Confidence Interval for (μ1 − μ2)
    • Example 6.7.1 Fast Plants
    • Example 6.7.2 Fast Plants
    • Example 6.7.3 Thorax Weight
    • 6.8 Perspective and Summary
    • Sampling Distributions and Data Analysis
    • Choice of Confidence Level
    • Characteristics of Other Measures
    • Summary of Estimation Methods
    • Supplementary Exercises 6.S.1–6.S.23
    • Chapter 7 Comparison of Two Independent Samples
    • Objectives
    • 7.1 Hypothesis Testing: The Randomization Test
    • Example 7.1.1 Flexibility
    • Example 7.1.2 Flexibility
    • Larger Samples
    • Example 7.1.3 Leaf Area
    • Exercises 7.1.1–7.1.5
    • Preview of the t Test (Section 7.2)
    • 7.2 Hypothesis Testing: The t Test
    • The Null and Alternative Hypotheses
    • Example 7.2.1 Toluene and the Brain
    • The t Statistic
    • Example 7.2.2 Toluene and the Brain
    • The P-Value
    • Example 7.2.3 Toluene and the Brain
    • Drawing Conclusions from a t Test
    • Example 7.2.4 Toluene and the Brain
    • Example 7.2.5 Fast Plants
    • Using Tables versus Using Technology
    • Example 7.2.6 Fast Plants
    • Reporting the Results of a t Test
    • Exercises 7.2.1–7.2.18
    • 7.3 Further Discussion of the t Test
    • Relationship Between Test and Confidence Interval
    • Example 7.3.1 Crawfish Lengths
    • Interpretation of α
    • Example 7.3.2 Music and Marigolds*
    • Significance Level versus P-Value
    • Type I and Type II Errors
    • Example 7.3.3 Marijuana and the Pituitary
    • Example 7.3.4 Immunotherapy
    • Power
    • Exercises 7.3.1–7.3.11
    • 7.4 Association and Causation
    • Example 7.4.1 Mosquito Weight
    • Example 7.4.2 Exercise During Pregnancy
    • Observational versus Experimental Studies
    • Example 7.4.3 Cigarette Smoking
    • More on Observational Studies
    • Example 7.4.4 Race and Brain Size
    • Confounding
    • Example 7.4.5 Smoking and Birthweight
    • Example 7.4.6 Smoking and Birthweight
    • Spurious Association
    • Example 7.4.7 Ultrasound
    • More on Experiments
    • Example 7.4.8 Headache Pain
    • Randomization Distributions
    • Only Statistical?
    • Exercises 7.4.1–7.4.10
    • 7.5 One-Tailed t Tests
    • Directional Alternative Hypotheses
    • Example 7.5.1 Niacin Supplementation
    • The One-Tailed Test Procedure
    • Example 7.5.2 Niacin Supplementation
    • Directional versus Nondirectional Alternatives
    • Example 7.5.3 Niacin Supplementation
    • Choosing the Form of HA
    • Example 7.5.4 Music and Marigolds
    • Exercises 7.5.1–7.5.16
    • 7.6 More on Interpretation of Statistical Significance
    • Significant Difference versus Important Difference
    • Example 7.6.1 Serum LD
    • Example 7.6.2 Body Weight
    • Effect Size
    • Example 7.6.3 Serum LD
    • Example 7.6.4 Body Weight
    • Confidence Intervals to Assess Importance
    • Example 7.6.5 Serum LD
    • Example 7.6.6 Body Weight
    • Example 7.6.7 Yield of Tomatoes
    • Example 7.6.8 Yield of Tomatoes
    • Exercises 7.6.1–7.6.11
    • 7.7 Planning for Adequate Power (Optional)
    • Dependence of Power on α
    • Dependence on σ
    • Dependence on n
    • Dependence on (μ1−μ2)
    • Example 7.7.1 Heights of People
    • Planning a Study
    • Example 7.7.2 Heights of People
    • Example 7.7.3 Postpartum Weight Loss
    • Exercises 7.7.1–7.7.12
    • 7.8 Student’s t: Conditions and Summary
    • Conditions
    • Verification of Conditions
    • Consequences of Inappropriate Use of Student’s t
    • Other Approaches
    • Example 7.8.1 Tissue Inflammation
    • Summary of t Test Mechanics
    • Exercises 7.8.1–7.8.3
    • 7.9 More on Principles of Testing Hypotheses
    • A General View of Hypothesis Tests
    • How are H0 and HA Chosen?
    • Another Look at P-Value
    • Interpretation of Error Probabilities
    • Example 7.9.1 Medical Testing
    • An Implicit Assumption
    • Perspective
    • Exercise 7.9.1
    • 7.10 The Wilcoxon-Mann-Whitney Test
    • Statement of H0 and HA
    • Example 7.10.1 Soil Respiration
    • Applicability of the Wilcoxon-Mann-Whitney Test
    • Method
    • Example 7.10.2 Soil Respiration
    • Directionality
    • Directional Alternative
    • Example 7.10.3 Directional HA
    • Rationale
    • Conditions for Use of the Wilcoxon-Mann-Whitney Test
    • The Wilcoxon-Mann-Whitney Test versus the t Test and the Randomization Test
    • Exercises 7.10.1–7.10.10
    • Supplementary Exercises 7.S.1–7.S.35
    • Chapter 8 Comparison of Paired Samples
    • Objectives
    • 8.1 Introduction
    • Example 8.1.1 Hunger Rating
    • Example 8.1.2 Hunger Rating Randomization Test
    • Exercise 8.1.1
    • 8.2 The Paired-Sample t Test and Confidence Interval
    • Analyzing Differences
    • Example 8.2.1 Hunger Rating
    • Confidence Interval and Test of Hypothesis
    • Example 8.2.2 Hunger Rating
    • Example 8.2.3 Hunger Rating
    • Result of Ignoring Pairing
    • Example 8.2.4 Cheese Gumminess
    • Conditions for Validity of Student’s t Analysis
    • Example 8.2.5 Squirrels
    • Summary of Formulas
    • Exercises 8.2.1–8.2.11
    • 8.3 The Paired Design
    • Examples of Paired Designs
    • Experiments with Pairs of Units
    • Example 8.3.1 Fertilizers for Eggplants
    • Observational Studies
    • Example 8.3.2 Smoking and Lung Cancer
    • Repeated Measurements
    • Example 8.3.3 Working Memory
    • Pairing by Time
    • Example 8.3.4 Stream Coliform Contamination
    • Purposes of Pairing
    • Randomized Pairs Design Versus Completely Randomized Design
    • Example 8.3.5 Fertilizers for Eggplants
    • Choice of Analysis
    • Exercises 8.3.1–8.3.5
    • 8.4 The Sign Test
    • Method
    • Example 8.4.1 Skin Grafts
    • Example 8.4.2 Stream Coliform Contamination
    • Bracketing the P-Value
    • Directional Alternative
    • Caution
    • Treatment of Zeros
    • Example 8.4.3 Null Distribution
    • How Table 7 Is Calculated
    • Example 8.4.4
    • Applicability of the Sign Test
    • Example 8.4.5 THC and Chemotherapy
    • Exercises 8.4.1–8.4.11
    • 8.5 The Wilcoxon Signed-Rank Test
    • Method
    • Example 8.5.1 Nerve Cell Density
    • Bracketing the P-Value
    • Directional Alternative
    • Treatment of Zeros
    • Treatment of Ties
    • Applicability of the Wilcoxon Signed-Rank Test
    • Exercises 8.5.1–8.5.7
    • 8.6 Perspective
    • Before–After Studies
    • Example 8.6.1 Wrist Fracture and Pain
    • Limitation of D¯
    • Example 8.6.2 Measuring Serum Cholesterol
    • Limitation of the Aggregate Viewpoint
    • Example 8.6.3 Treatment of Acne
    • Reporting of Data
    • Exercises 8.6.1–8.6.5
    • Supplementary Exercises 8.S.1–8.S.23
    • Unit II Highlights and Study (Reflections on Chapters 6, 7, and 8)
    • Hypothesis Testing
    • Type I and Type II Errors
    • Effect Size and Confidence Intervals
    • Nonsignificant Findings
    • Requirements
    • Outliers
    • Unit II Summary Exercises
    • Background for II.15–II.19

Unit III Inference for Categorical Data

    • Chapter 9 Categorical Data: One-Sample Distributions
    • Objectives
    • 9.1 Dichotomous Observations
    • The Wilson-Adjusted Sample Proportion, p˜
    • Example 9.1.1 Contaminated Soda
    • Example 9.1.2 Contaminated Soda
    • The Sampling Distribution of p˜
    • Example 9.1.3 Contaminated Soda
    • Example 9.1.4 Contaminated Soda and a Larger Sample
    • Relationship to Statistical Inference
    • Example 9.1.5 Contaminated Soda
    • Dependence on Sample Size
    • Example 9.1.6 Contaminated Soda
    • Exercises 9.1.1–9.1.10
    • 9.2 Confidence Interval for a Population Proportion
    • Standard Error of p˜
    • Example 9.2.1 Smoking During Pregnancy
    • 95% Confidence Interval for p
    • Example 9.2.2 Breast Cancer
    • Example 9.2.3 Breast Cancer
    • Example 9.2.4 ECMO
    • Conditions for Use of the Wilson 95% Confidence Interval for p
    • One-Sided Confidence Intervals
    • Example 9.2.5 ECMO—One-Sided
    • Planning a Study to Estimate p
    • Example 9.2.6 Vegetarians
    • Planning in Ignorance
    • Example 9.2.7 Vegetarians
    • Exercises 9.2.1–9.2.17
    • 9.3 Other Confidence Levels (Optional)
    • Example 9.3.1 Vegetarians
    • Exercises 9.3.1–9.3.4
    • 9.4 Inference for Proportions: The Chi-Square Goodness-of-Fit Test
    • Example 9.4.1 Deer Habitat and Fire
    • Example 9.4.2 Deer Habitat and Fire
    • The Chi-Square Statistic
    • Example 9.4.3 Deer Habitat and Fire
    • Example 9.4.4 Deer Habitat and Fire
    • The x2 Distribution
    • The Goodness-of-Fit Test
    • Example 9.4.5 Deer Habitat and Fire
    • Example 9.4.6 Flax Seeds
    • Compound Hypotheses and Directionality
    • Example 9.4.7 Deer Habitat and Fire
    • Dichotomous Variables
    • Directional Conclusion
    • Example 9.4.8 Deer Habitat, Fire, and Two Regions
    • Directional Alternative
    • Example 9.4.9 Harvest Moon Festival
    • Exercises 9.4.1–9.4.13
    • 9.5 Perspective and Summary
    • Supplementary Exercises 9.S.1–9.S.22
    • Chapter 10 Categorical Data: Relationships
    • Objectives
    • 10.1 Introduction
    • Example 10.1.1 Migraine Headache
    • Example 10.1.2 HIV Testing
    • Conditional Probability
    • Example 10.1.3 Migraine Headache
    • A Randomization Test
    • Example 10.1.4 ECMO
    • 10.2 The Chi-Square Test for the 2 × 2 Contingency Table
    • Example 10.2.1 Migraine Headache
    • The Chi-Square Statistic
    • Example 10.2.2 Migraine Headache
    • Example 10.2.3 Migraine Headache
    • The Test Procedure
    • Example 10.2.4 Migraine Headache
    • Computational Notes
    • Illustration of the Null Hypothesis
    • Example 10.2.5 Fictitious Migraine Study
    • Example 10.2.6 HIV Testing
    • Exercises 10.2.1–10.2.15
    • 10.3 Independence and Association in the 2 × 2 Contingency Table
    • Two Contexts for Contingency Tables
    • Independence and Association
    • Example 10.3.1 Hair Color and Eye Color
    • Example 10.3.2 Hair Color and Eye Color
    • Example 10.3.3 Plant Height and Disease Resistance
    • Facts About Rows and Columns
    • Fact 10.3.1
    • Fact 10.3.2
    • Verbal Description of Association
    • Example 10.3.4 Plant Height and Disease Resistance
    • Example 10.3.5 Hair Color and Eye Color
    • Exercises 10.3.1–10.3.12
    • 10.4 Fisher’s Exact Test (Optional)
    • Example 10.4.1 ECMO
    • Combinations
    • Example 10.4.2 ECMO
    • Example 10.4.3 ECMO
    • Comparison to the Chi-Square Test
    • Example 10.4.4 ECMO
    • Nondirectional Alternatives and the Exact Test
    • Example 10.4.5 Flu Shots
    • Exercises 10.4.1–10.4.9
    • 10.5 The r × k Contingency Table
    • Example 10.5.1 Plover Nesting
    • The Chi-Square Test for the r × k Table
    • Example 10.5.2 Plover Nesting
    • Two Contexts for r × k Contingency Tables
    • Example 10.5.3 Hair Color and Eye Color
    • Exercises 10.5.1–10.5.11
    • 10.6 Applicability of Methods
    • Conditions for Validity
    • Verification of Design Conditions
    • Example 10.6.1 Food Choice by Insect Larvae
    • Example 10.6.2 Pollination of Flowers
    • Power Considerations
    • Example 10.6.3 Physiotherapy
    • Exercises 10.6.1–10.6.3
    • 10.7 Confidence Interval for Difference Between Probabilities
    • Example 10.7.1 Migraine Headache
    • 10.8 Paired Data and 2 × 2 Tables (Optional)
    • Example 10.8.1 HIV Transmission to Children
    • McNemar’s Test
    • Example 10.8.2 HIV Transmission to Children
    • Exercises 10.8.1–10.8.4
    • 10.9 Relative Risk and the Odds Ratio (Optional)
    • Relative Risk
    • Example 10.9.1 Smoking and Lung Cancer
    • The Odds Ratio
    • Example 10.9.2 Smoking and Lung Cancer
    • Odds Ratio and Relative Risk
    • Example 10.9.3 Smoking and Lung Cancer
    • Advantage of the Odds Ratio
    • Example 10.9.4 Smoking and Lung Cancer
    • Example 10.9.5 Smoking and Lung Cancer
    • Example 10.9.6 Smoking and Lung Cancer
    • Example 10.9.7 Smoking and Lung Cancer
    • Confidence Interval for the Odds Ratio
    • Example 10.9.8 Smoking and Lung Cancer
    • Example 10.9.9 Heart Attacks and Aspirin
    • Exercises 10.9.1–10.9.9
    • 10.10 Summary of Chi-Square Test
    • Supplementary Exercises 10.S.1–10.S.21
    • Unit III Highlights and Study (Reflections on Chapters 9 and 10)
    • Chi-Square Test of Independence
    • Drawing Conclusions Based on the Study Design
    • Chi-Square Goodness-of-Fit Test
    • Wilson Adjusted Confidence Interval for a Proportion
    • Unit III Summary Exercises
    • Background for III.6–III.9

Unit IV Modeling Relationships

    • Chapter 11 Comparing the Means of Many Independent Samples
    • Objectives
    • 11.1 Introduction
    • Example 11.1.1 Sweet Corn
    • A Randomization Test
    • Example 11.1.2 Sweet Corn
    • Why Not Repeated t Tests?
    • A Graphical Perspective on ANOVA
    • A Look Ahead
    • 11.2 The Basic One-Way Analysis of Variance
    • Notation
    • Example 11.2.1 Weight Gain of Lambs
    • Measuring Variation Within Groups
    • Example 11.2.2 Weight Gain of Lambs
    • ANOVA Notation
    • Example 11.2.3 Weight Gain of Lambs
    • Variation Between Groups
    • Example 11.2.4 Weight Gain of Lambs
    • A Fundamental Relationship of ANOVA
    • Example 11.2.5 Weight Gain of Lambs
    • The ANOVA Table
    • Example 11.2.6 Weight Gain of Lambs
    • Comment on Terminology
    • Summary of Formulas
    • Exercises 11.2.1–11.2.7
    • 11.3 The Analysis of Variance Model
    • Example 11.3.1 Weight Gain of Lambs
    • 11.4 The Global F Test
    • The F Distributions
    • The F Test
    • Example 11.4.1 Weight Gain of Lambs
    • Relationship between F Test and t Test
    • Exercises 11.4.1–11.4.9
    • 11.5 Applicability of Methods
    • Standard Conditions
    • Verification of Conditions
    • Example 11.5.1 Weight Gain of Lambs
    • Example 11.5.2 Sweet Corn
    • Example 11.5.3 Sweet Corn
    • Further Analysis
    • Exercises 11.5.1–11.5.4
    • 11.6 One-Way Randomized Blocks Design
    • Example 11.6.1 Alfalfa and Acid Rain
    • Example 11.6.2 Blocking by Litter
    • Example 11.6.3 Within-Subject Blocking (Pairing)
    • Example 11.6.4 Blocking in an Agricultural Field Study
    • Creating the Blocks
    • Example 11.6.5 Agricultural Field Study
    • The Randomization Procedure
    • Example 11.6.6 Agricultural Field Study
    • Analyzing Data from a Randomized Block Experiment
    • Example 11.6.7 Alfalfa and Acid Rain
    • Visualizing the Block Effects
    • The One-Way Randomized Complete Block F Test
    • Example 11.6.8 Alfalfa and Acid Rain
    • Example 11.6.9 Alfalfa and Acid Rain
    • Exercises 11.6.1–11.6.12
    • 11.7 Two-Way ANOVA
    • Factorial ANOVA
    • Example 11.7.1 Growth of Soybeans
    • Example 11.7.2 Growth of Soybeans
    • Example 11.7.3 Iron Supplements in Milk-Based Fruit Beverages
    • Example 11.7.4 Iron Supplements in Milk-Based Fruit Beverages
    • Example 11.7.5 Growth of Soybeans
    • Example 11.7.6 Toads
    • Exercises 11.7.1–11.7.9
    • 11.8 Linear Combinations of Means (Optional)
    • Linear Combinations for Adjustment
    • Example 11.8.1 Forced Vital Capacity
    • Contrasts
    • Example 11.8.2 Growth of Soybeans
    • Standard Error of a Linear Combination
    • Example 11.8.3 Forced Vital Capacity
    • Example 11.8.4 Growth of Soybeans
    • Confidence Intervals
    • Example 11.8.5 Growth of Soybeans
    • t Tests
    • Contrasts to Assess Interaction
    • Example 11.8.6 Growth of Soybeans
    • Example 11.8.7 Chromosomal Aberrations
    • Exercises 11.8.1–11.8.10
    • 11.9 Multiple Comparisons (Optional)
    • Experimentwise Versus Comparisonwise Error
    • Fisher’s Least Significant Difference
    • Example 11.9.1 Oysters and Seagrass
    • How does Fisher’s LSD control the experimentwise Type I error rate?
    • Displaying Results
    • Example 11.9.2 Oysters and Seagrass
    • The Bonferroni Method
    • Example 11.9.3 Oysters and Seagrass
    • Tukey’s Honest Significant Difference
    • Conditions for Validity
    • Exercises 11.9.1–11.9.8
    • 11.10 Perspective
    • Advantages of Global Approach
    • Other Experimental Designs
    • Nonparametric Approaches
    • Supplementary Exercises 11.S.1–11.S.20
    • Chapter 12 Linear Regression and Correlation
    • Objectives
    • 12.1 Introduction
    • Examples
    • Example 12.1.1 Amphetamine and Food Consumption
    • Example 12.1.2 Dissolved Oxygen
    • 12.2 The Correlation Coefficient
    • Example 12.2.1 Length and Weight of Snakes
    • Measuring Strength of Linear Association
    • Interpreting the Correlation Coefficient
    • Example 12.2.2 Length and Weight of Snakes
    • Inference Concerning Correlation
    • R Testing the Hypothesis H0:ρ=0
    • A randomization test
    • Example 12.2.3 Blood Pressure and Platelet Calcium
    • A t Test
    • Example 12.2.4 Blood Pressure and Platelet Calcium
    • Why n − 2?
    • Confidence Interval for ρ (Optional)
    • Example 12.2.5 Blood Pressure and Platelet Calcium
    • Correlation and Causation
    • Example 12.2.6 Reproduction of an Alga
    • Cautionary Notes
    • Exercises 12.2.1–12.2.12
    • 12.3 The Fitted Regression Line
    • Example 12.3.1 Ocean Temperature
    • The SD Line
    • Example 12.3.2 Dissolved Oxygen
    • Example 12.3.3 Dissolved Oxygen
    • Equation of the Fitted Regression Line
    • Example 12.3.4 Dissolved Oxygen
    • Graph of Averages
    • Example 12.3.5 Amphetamine and Food Consumption
    • Example 12.3.6 Amphetamine and Food Consumption
    • The Residual Sum of Squares
    • Example 12.3.7 Dissolved Oxygen
    • The Least-Squares Criterion
    • The Residual Standard Deviation
    • Example 12.3.8 Dissolved Oxygen
    • Example 12.3.9 Dissolved Oxygen
    • The Coefficient of Determination
    • Example 12.3.10 Dissolved Oxygen
    • Example 12.3.11 Amphetamine and Food Consumption
    • Exercises 12.3.1–12.3.13
    • 12.4 Parametric Interpretation of Regression: The Linear Model
    • Conditional Populations and Conditional Distributions
    • Example 12.4.1 Amphetamine and Food Consumption
    • Example 12.4.2 Height and Weight of Young Men
    • The Linear Model
    • Example 12.4.3 Amphetamine and Food Consumption
    • Example 12.4.4 Height and Weight of Young Men
    • Remark
    • Estimation in the Linear Model
    • Example 12.4.5 Length and Weight of Snakes
    • Interpolation in the Linear Model
    • Example 12.4.6 Dissolved Oxygen
    • Example 12.4.7 Amphetamine and Food Consumption
    • Prediction and the Linear Model
    • Exercises 12.4.1–12.4.9
    • 12.5 Statistical Inference Concerning β1
    • The Standard Error of b1
    • Example 12.5.1 Length and Weight of Snakes
    • Structure of the SE
    • Implications for Design
    • Confidence Interval for β1
    • Example 12.5.2 Length and Weight of Snakes
    • Testing the Hypothesis H0:β1 = 0
    • Example 12.5.3 Blood Pressure and Platelet Calcium
    • A randomization test
    • Example 12.5.4 Blood Pressure and Platelet Calcium
    • Exercises 12.5.1–12.5.10
    • 12.6 Guidelines for Interpreting Regression and Correlation
    • When Is Linear Regression Descriptively Inadequate?
    • Example 12.6.1 A Curvilinear Relationship with X
    • Conditions for Inference
    • Guidelines Concerning the Sampling Conditions
    • Example 12.6.2 Serum Cholesterol and Serum Glucose
    • Example 12.6.3 Growth of Beef Steers
    • Example 12.6.4
    • Labeling X and Y
    • Example 12.6.5 Height and Weight of Young Men
    • Guidelines Concerning the Linear Model and Normality Condition
    • Residual Plots
    • The Use of Transformations
    • Example 12.6.6 Growth of Soybeans
    • Exercises 12.6.1–12.6.9
    • 12.7 Precision in Prediction (Optional)
    • Confidence and Prediction Intervals
    • Example 12.7.1 Dissolved Oxygen
    • Computing the Intervals
    • Exercises 12.7.1–12.7.4
    • 12.8 Perspective
    • Regression and the t Test
    • Example 12.8.1 Toluene and the Brain
    • Example 12.8.2 Blood Pressure and Platelet Calcium
    • Extensions of Least Squares
    • Example 12.8.3 Serum Cholesterol and Blood Pressure
    • Nonparametric and Robust Regression and Correlation
    • Analysis of Covariance
    • Example 12.8.4 Caterpillar Head Size
    • Logistic Regression
    • Example 12.8.5 Esophageal Cancer
    • 12.9 Summary of Formulas
    • Supplementary Exercises 12.S.1–12.S.30
    • Unit IV Highlights and Study (Reflections on Chapters 11 and 12)
    • Multiple Comparisons (Optional)
    • Checking ANOVA Requirements
    • Regression
    • Checking Regression Requirements
    • Unit IV Summary Exercises
    • Chapter 13 A Summary of Inference Methods
    • Objectives
    • 13.1 Introduction
    • Exploratory Data Analysis
    • 13.2 Data Analysis Examples
    • Example 13.2.1 Gibberellic Acid
    • Example 13.2.2 Whale Swimming Speed
    • Example 13.2.3 Progesterone Gel and Preemies
    • Example 13.2.4 Cystic Fibrosis
    • Example 13.2.5 Therapeutic Touch
    • Brief Examples
    • Example 13.2.6 Seastars
    • Example 13.2.7 Twins
    • Example 13.2.8 Soil Samples
    • Example 13.2.9 Vaccinations
    • Example 13.2.10 Estrogen and Steroids
    • Example 13.2.11 Damselflies
    • Example 13.2.12 Tobacco Use Prevention
    • Example 13.2.13 Reaction Times
    • Exercises 13.2.1–13.2.24

Chapter Appendices

    • Appendix
    • Appendix 3.1 More on the Binomial Distribution Formula
    • Appendix 3.2 Mean and Standard Deviation of the Binomial Distribution
    • Appendix 5.1 Relationship between Central Limit Theorem and Normal Approximation to Binomial Distribution
    • Appendix 7.1 How Power Is Calculated

Chapter Appendices

    • Appendix
    • Appendix 4.1 Areas of Indefinitely Extended Regions
    • Appendix 6.1 Significant Digits
    • Appendix 7.2 More on the Wilcoxon-Mann-Whitney Test
    • Appendix 9.1 More on Confidence Intervals for a Proportion
    • Appendix 12.1 Least-Squares Formulas
    • Appendix 12.2 Derivation of Fact 12.3.1

Chapter References

Chapter Notes

Answers to Selected Exercises

Statistical Tables

Credits

    • Chapter 1
    • Chapter 2
    • Chapter 6
    • Chapter 7
    • Unit III

Reviews

There are no reviews yet.

Be the first to review “Test Bank for Statistics for the Life Sciences 5th Edition by Myra Samuels”

Ebook Title:

Authors:

Reviews

Create a Free Account

* We don’t share your personal info with anyone. Check out our Privacy Policy for more information