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• Table of Contents and Preface
  • Cover Page
  • Title Page
  • Copyright
  • Dedication
  • About the Author
  • Brief Contents
  • Contents
  • Preface
    • Motivation
    • Mathematical Level
    • Computer Use
    • Content
    • New for this Edition
    • Recommended Coverage
    • Instructor Resources
    • Acknowledgments
    • Additional Resources
  • Key Features
    • Content Overview
    • Real-World Data Sets
    • Computer Output
  • Connect
• Chapter 1: Sampling and Descriptive Statistics
  • Chapter 1 Introduction
    • Introduction
    • The Basic Idea
  • 1.1 Sampling
    • Independence
    • Other Sampling Methods
    • Types of Experiments
    • Types of Data
    • Controlled Experiments and Observational Studies
    • Exercises for Section 1.1
  • 1.2 Summary Statistics
    • The Sample Mean
    • The Standard Deviation
    • The Sample Median
    • The Trimmed Mean
    • Outliers
    • Resistance to Outliers
    • The Mode and the Range
    • Quartiles
    • Percentiles
    • Summary Statistics for Categorical Data
    • Sample Statistics and Population Parameters
    • Exercises for Section 1.2
  • 1.3 Graphical Summaries
    • Stem-and-Leaf Plots
    • Dotplots
    • Histograms
    • Unequal Class Widths
    • Symmetry and Skewness
    • Unimodal and Bimodal Histograms
    • Boxplots
    • Comparative Boxplots
    • Multivariate Data
    • Exercises for Section 1.3
  • Supplementary Exercises for Chapter 1
• Chapter 2: Probability
  • Chapter 2 Introduction
    • Introduction
  • 2.1 Basic Ideas
    • Combining Events
    • Mutually Exclusive Events
    • Probabilities
    • Axioms of Probability
    • Sample Spaces with Equally Likely Outcomes
    • The Addition Rule
    • Exercises for Section 2.1
  • 2.2 Counting Methods
    • Permutations
    • Combinations
    • Exercises for Section 2.2
  • 2.3 Conditional Probability and Independence
    • Independent Events
    • The Multiplication Rule
    • The Law of Total Probability
    • Bayes’ Rule
    • Application to Reliability Analysis
    • Exercises for Section 2.3
  • 2.4 Random Variables
    • Random Variables and Populations
    • Discrete Random Variables
    • The Cumulative Distribution Function of a Discrete Random Variable
    • Mean and Variance for Discrete Random Variables
    • The Probability Histogram
    • Continuous Random Variables
    • Computing Probabilities with the Probability Density Function
    • The Cumulative Distribution Function of a Continuous Random Variable
    • Mean and Variance for Continuous Random Variables
    • The Population Median and Percentiles
    • Chebyshev’s Inequality
    • Exercises for Section 2.4
  • 2.5 Linear Functions of Random Variables
    • Adding a Constant
    • Multiplying by a Constant
    • Means of Linear Combinations of Random Variables
    • Independent Random Variables
    • Variances of Linear Combinations of Independent Random Variables
    • Independence and Simple Random Samples
    • The Mean and Variance of a Sample Mean
    • Exercises for Section 2.5
  • 2.6 Jointly Distributed Random Variables
    • Jointly Discrete Random Variables
    • Jointly Continuous Random Variables
    • More than Two Random Variables
    • Means of Functions of Random Variables
    • Conditional Distributions
    • Conditional Expectation
    • Independent Random Variables
    • Covariance
    • Correlation
    • Covariance, Correlation, and Independence
    • Linear Combinations of Random Variables
    • The Mean and Variance of a Sample Mean
    • Application to Portfolio Management
    • Exercises for Section 2.6
  • Supplementary Exercises for Chapter 2
• Chapter 3: Propagation of Error
  • Chapter 3 Introduction
    • Introduction
  • 3.1 Measurement Error
    • Exercises for Section 3.1
  • 3.2 Linear Combinations of Measurements
    • Repeated Measurements
    • Repeated Measurements with Differing Uncertainties
    • Linear Combinations of Dependent Measurements
    • Exercises for Section 3.2
  • 3.3 Uncertainties for Functions of One Measurement
    • Propagation of Error Uncertainties Are Only Approximate
    • Nonlinear Functions Are Biased
    • Relative Uncertainties for Functions of One Measurement
    • Exercises for Section 3.3
  • 3.4 Uncertainties for Functions of Several Measurements
    • Uncertainties for Functions of Dependent Measurements
    • Relative Uncertainties for Functions of Several Measurements
    • Exercises for Section 3.4
  • Supplementary Exercises for Chapter 3
• Chapter 4: Commonly Used Distributions
  • Chapter 4 Introduction
    • Introduction
  • 4.1 The Bernoulli Distribution
    • Mean and Variance of a Bernoulli Random Variable
    • Exercises for Section 4.1
  • 4.2 The Binomial Distribution
    • Probability Mass Function of a Binomial Random Variable
    • A Binomial Random Variable Is a Sum of Bernoulli Random Variables
    • The Mean and Variance of a Binomial Random Variable
    • Using a Sample Proportion to Estimate a Success Probability
    • Uncertainty in the Sample Proportion
    • Exercises for Section 4.2
  • 4.3 The Poisson Distribution
    • The Mean and Variance of a Poisson Random Variable
    • Using the Poisson Distribution to Estimate a Rate
    • Uncertainty in the Estimated Rate
    • Exercises for Section 4.3
  • 4.4 Some Other Discrete Distributions
    • The Hypergeometric Distribution
    • Mean and Variance of the Hypergeometric Distribution
    • Comparison with the Binomial Distribution
    • The Geometric Distribution
    • The Mean and Variance of a Geometric Distribution
    • The Negative Binomial Distribution
    • A Negative Binomial Random Variable Is a Sum of Geometric Random Variables
    • The Mean and Variance of the Negative Binomial Distribution
    • The Multinomial Distribution
    • Exercises for Section 4.4
  • 4.5 The Normal Distribution
    • Estimating the Parameters of a Normal Distribution
    • Linear Functions of Normal Random Variables
    • Linear Combinations of Independent Normal Random Variables
    • How Can I Tell Whether My Data Come from a Normal Population?
    • Exercises for Section 4.5
  • 4.6 The Lognormal Distribution
    • Estimating the Parameters of a Lognormal Distribution
    • How Can I Tell Whether My Data Come from a Lognormal Population?
    • Exercises for Section 4.6
  • 4.7 The Exponential Distribution
    • The Exponential Distribution and the Poisson Process
    • Memoryless Property
    • Using the Exponential Distribution to Estimate a Rate
    • Correcting the Bias
    • Exercises for Section 4.7
  • 4.8 Some Other Continuous Distributions
    • The Uniform Distribution
    • The Gamma Distribution
    • The Weibull Distribution
    • Exercises for Section 4.8
  • 4.9 Some Principles of Point Estimation
    • Measuring the Goodness of an Estimator
    • The Method of Maximum Likelihood
    • Desirable Properties of Maximum Likelihood Estimators
    • Exercises for Section 4.9
  • 4.10 Probability Plots
    • Interpreting Probability Plots
    • Exercises for Section 4.10
  • 4.11 The Central Limit Theorem
    • Normal Approximation to the Binomial
    • The Continuity Correction
    • Accuracy of the Continuity Correction
    • Normal Approximation to the Poisson
    • Continuity Correction for the Poisson Distribution
    • Exercises for Section 4.11
  • 4.12 Simulation
    • Using Simulation to Estimate a Probability
    • Estimating Means and Variances
    • Using Simulation to Determine Whether a Population Is Approximately Normal
    • Using Simulation in Reliability Analysis
    • Using Simulation to Estimate Bias
    • The Bootstrap
    • Parametric and Nonparametric Bootstrap
    • Comparison of Simulation with Propagation of Error
    • Exercises for Section 4.12
  • Supplementary Exercises for Chapter 4
• Chapter 5: Confidence Intervals
  • Chapter 5 Introduction
    • Introduction
  • 5.1 Confidence Intervals for a Population Mean, Variance Known
    • More About Confidence Levels
    • Probability versus Confidence
    • One-Sided Confidence Intervals
    • Confidence Intervals Must Be Based on Random Samples
    • Exercises for Section 5.1
  • 5.2 Confidence Intervals for a Population Mean, Variance Unknown
    • The Student’s t Distribution
    • Confidence Intervals Using the Student’s t Distribution
    • How Do I Determine Whether the Student’s t Distribution Is Appropriate?
    • Approximating the Sample Size Needed for a Confidence Interval of a Specified Width
    • The Student’s t Distribution Is Close to Normal When the Sample Size Is Large
    • Exercises for Section 5.2
  • 5.3 Confidence Intervals for Proportions
    • The Traditional Method
    • Exercises for Section 5.3
  • 5.4 Confidence Intervals for the Difference Between Two Means
    • Constructing Confidence Intervals When Population Variances Are Known
    • Constructing Confidence Intervals When Population Variances Are Unknown
    • When the Populations Have Equal Variances
    • Don’t Assume the Population Variances Are Equal Just Because the Sample Variances Are Close
    • Exercises for Section 5.4
  • 5.5 Confidence Intervals for the Difference Between Two Proportions
    • The Traditional Method
    • Exercises for Section 5.5
  • 5.6 Confidence Intervals with Paired Data
    • Exercises for Section 5.6
  • 5.7 Confidence Intervals for the Variance and Standard Deviation of a Normal Population
    • The Chi-Square Distribution
    • Confidence Intervals for the Variance of a Normal Population
    • Confidence Intervals for the Variance Are Sensitive to Departures from Normality
    • Exercises for Section 5.7
  • 5.8 Prediction Intervals and Tolerance Intervals
    • Prediction Intervals
    • Comparison of Prediction Intervals and Confidence Intervals
    • One-Sided Prediction Intervals
    • Prediction Intervals Are Sensitive to Departures from Normality
    • Tolerance Intervals for a Normal Population
    • Exercises for Section 5.8
  • 5.9 Using Simulation to Construct Confidence Intervals
    • Confidence Intervals Using the Bootstrap
    • Using Simulation to Evaluate Confidence Intervals
    • Exercises for Section 5.9
  • Supplementary Exercises for Chapter 5
• Chapter 6: Hypothesis Testing
  • Chapter 6 Introduction
    • Introduction
  • 6.1 Tests for a Population Mean, Variance Known
    • Another Way to Express H 0
    • Exercises for Section 6.1
  • 6.2 Drawing Conclusions from the Results of Hypothesis Tests
    • Statistical Significance
    • The P-value Is Not the Probability That H 0 Is True
    • Choose H 0 to Answer the Right Question
    • Statistical Significance Is Not the Same as Practical Significance
    • The Relationship Between Hypothesis Tests and Confidence Intervals
    • Exercises for Section 6.2
  • 6.3 Tests for a Population Mean, Variance Unknown
    • Exercises for Section 6.3
  • 6.4 Tests for a Population Proportion
    • The Sample Size Must Be Large
    • Relationship with Confidence Intervals for a Proportion
    • Exercises for Section 6.4
  • 6.5 Tests for the Difference Between Two Means
    • Tests When Population Variances Are Known
    • Tests When Population Variances Are Unknown
    • When the Populations Have Equal Variances
    • Don’t Assume the Population Variances Are Equal Just Because the Sample Variances Are Close
    • Exercises for Section 6.5
  • 6.6 Tests for the Difference Between Two Proportions
    • Exercises for Section 6.6
  • 6.7 Tests with Paired Data
    • Exercises for Section 6.7
  • 6.8 Distribution-Free Tests
    • The Wilcoxon Signed-Rank Test
    • Ties
    • Differences of Zero
    • Large-Sample Approximation
    • The Wilcoxon Rank-Sum Test
    • Large-Sample Approximation
    • Distribution-Free Methods Are Not Assumption-Free
    • Exercises for Section 6.8
  • 6.9 Tests with Categorical Data
    • The Chi-Square Test for Homogeneity
    • The Chi-Square Test for Independence
    • Exercises for Section 6.9
  • 6.10 Tests for Variances of Normal Populations
    • Testing the Variance of a Normal Population
    • The F Test for Equality of Variance
    • The F Distribution
    • The F Statistic for Testing Equality of Variance
    • The F Test Is Sensitive to Departures from Normality
    • The F Test Cannot Prove That Two Variances Are Equal
    • Exercises for Section 6.10
  • 6.11 Fixed-Level Testing
    • Critical Points and Rejection Regions
    • Type I and Type II Errors
    • Exercises for Section 6.11
  • 6.12 Power
    • Using a Computer to Calculate Power
    • Exercises for Section 6.12
  • 6.13 Multiple Tests
    • The Bonferroni Method
    • Exercises for Section 6.13
  • 6.14 Using Simulation to Perform Hypothesis Tests
    • Testing Hypotheses with Bootstrap Confidence Intervals
    • Randomization Tests
    • Using Simulation to Estimate Power
    • Exercises for Section 6.14
  • Supplementary Exercises for Chapter 6
• Chapter 7: Correlation and Simple Linear Regression
  • Chapter 7 Introduction
    • Introduction
  • 7.1 Correlation
    • How the Correlation Coefficient Works
    • The Correlation Coefficient Is Unitless
    • The Correlation Coefficient Measures Only Linear Association
    • The Correlation Coefficient Can Be Misleading when Outliers Are Present
    • Correlation Is Not Causation
    • Controlled Experiments Reduce the Risk of Confounding
    • Inference on the Population Correlation
    • Exercises for Section 7.1
  • 7.2 The Least-Squares Line
    • Computing the Equation of the Least-Squares Line
    • Computing Formulas
    • Interpreting the Slope of the Least-Squares Line
    • The Estimates Are Not the Same as the True Values
    • The Residuals Are Not the Same as the Errors
    • Don’t Extrapolate Outside the Range of the Data
    • Interpreting the y-Intercept of the Least-Squares Line
    • Don’t Use the Least-Squares Line When the Data Aren’t Linear
    • Another Look at the Least-Squares Line
    • Measuring Goodness-of-Fit
    • Exercises for Section 7.2
  • 7.3 Uncertainties in the Least-Squares Coefficients
    • The More Spread in the x Values, the Better (Within Reason)
    • Inferences on the Slope and Intercept
    • Inferences on the Mean Response
    • Prediction Intervals for Future Observations
    • Interpreting Computer Output
    • Exercises for Section 7.3
  • 7.4 Checking Assumptions and Transforming Data
    • The Plot of Residuals versus Fitted Values
    • Transforming the Variables
    • Determining Which Transformation to Apply
    • Transformations Don’t Always Work
    • Residual Plots with Only a Few Points Can Be Hard to Interpret
    • Outliers and Influential Points
    • Methods Other Than Transforming Variables
    • Checking Independence and Normality
    • Empirical Models and Physical Laws
    • Exercises for Section 7.4
  • Supplementary Exercises for Chapter 7
• Chapter 8: Multiple Regression
  • Chapter 8 Introduction
    • Introduction
  • 8.1 The Multiple Regression Model
    • Estimating the Coefficients
    • Sums of Squares
    • The Statistics s 2 , R 2 , and F
    • An Example
    • Checking Assumptions in Multiple Regression
    • Exercises for Section 8.1
  • 8.2 Confounding and Collinearity
    • Collinearity
    • Exercises for Section 8.2
  • 8.3 Model Selection
    • Determining Whether Variables Can Be Dropped from a Model
    • Best Subsets Regression
    • Stepwise Regression
    • Model Selection Procedures Sometimes Find Models When They Shouldn’t
    • Exercises for Section 8.3
  • Supplementary Exercises for Chapter 8
• Chapter 9: Factorial Experiments
  • Chapter 9 Introduction
    • Introduction
  • 9.1 One-Factor Experiments
    • Completely Randomized Experiments
    • One-Way Analysis of Variance
    • Confidence Intervals for the Treatment Means
    • The ANOVA Table
    • Checking the Assumptions
    • Balanced versus Unbalanced Designs
    • The Analysis of Variance Identity
    • An Alternative Parameterization
    • Power
    • Random Effects Models
    • Exercises for Section 9.1
  • 9.2 Pairwise Comparisons in One-Factor Experiments
    • Fisher’s Least Significant Difference (LSD) Method
    • The Bonferroni Method of Multiple Comparisons
    • The Tukey-Kramer Method of Multiple Comparisons
    • Exercises for Section 9.2
  • 9.3 Two-Factor Experiments
    • Parameterization for Two-Way Analysis of Variance
    • Using Two-Way ANOVA to Test Hypotheses
    • Checking the Assumptions
    • Don’t Interpret the Main Effects When the Additive Model Doesn’t Hold
    • A Two-Way ANOVA Is Not the Same as Two One-Way ANOVAs
    • Interaction Plots
    • Multiple Comparisons in Two-Way ANOVA
    • Two-Way ANOVA when K=1
    • Random Factors
    • Unbalanced Designs
    • Exercises for Section 9.3
  • 9.4 Randomized Complete Block Designs
    • Multiple Comparisons in Randomized Complete Block Designs
    • Exercises for Section 9.4
  • 9.5 2p Factorial Experiments
    • Notation for 2 3 Factorial Experiments
    • Estimating Effects in a 2 3 Factorial Experiment
    • Interpreting Computer Output
    • Estimating Effects in a 2 p Factorial Experiment
    • Factorial Experiments without Replication
    • Using Probability Plots to Detect Large Effects
    • Fractional Factorial Experiments
    • Exercises for Section 9.5
  • Supplementary Exercises for Chapter 9
• Chapter 10: Statistical Quality Control
  • Chapter 10 Introduction
    • Introduction
  • 10.1 Basic Ideas
    • Collecting Data–Rational Subgroups
    • Control versus Capability
    • Process Control Must Be Done Continually
    • Similarities Between Control Charts and Hypothesis Tests
    • Exercises for Section 10.1
  • 10.2 Control Charts for Variables
    • Control Chart Performance
    • The Western Electric Rules
    • The S chart
    • Which Is Better, the S Chart or the R Chart?
    • Samples of Size 1
    • Exercises for Section 10.2
  • 10.3 Control Charts for Attributes
    • The p Chart
    • Interpreting Out-of-Control Signals in Attribute Charts
    • The c Chart
    • Exercises for Section 10.3
  • 10.4 The CUSUM Chart
    • Exercises for Section 10.4
  • 10.5 Process Capability
    • Estimating the Proportion of Nonconforming Units from Process Capability
    • Six-Sigma Quality
    • One-Sided Tolerances
    • Exercises for Section 10.5
  • Supplementary Exercises for Chapter 10
• Appendix
  • Appendix A: Tables
    • Tables
  • Appendix B: Partial Derivatives
    • Partial Derivatives
    • Exercises for Appendix B
  • Appendix C: Data Sets
    • Data Sets
  • Appendix D: References
    • References
• Answers to Selected Exercises
  • Answers to Selected Exercises
• Index
  • Index
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    • B
    • C
    • D
    • E
    • F
    • G
    • H
    • I
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    • K
    • L
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    • W
    • X
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  • Figure 7.3 Text Alternative (Chapter 7)
  • Figure 7.4 Text Alternative (Chapter 7)
  • Figure 7.5 Text Alternative (Chapter 7)
  • Figure 7.6 Text Alternative (Chapter 7)
  • Figure 7.7 Text Alternative (Chapter 7)
  • Figure 7.8 Text Alternative (Chapter 7)
  • 2 Scatter Plots, One is of Percen Text Alternative (Chapter 7)
  • A Scatter Plot of Time in Hours Text Alternative (Chapter 7)
  • 2 Scatter Plots, One is of Percent Text Alternative (Chapter 7)
  • A Scatter Plot of Time in Hours Text Alternative (Chapter 7)
  • A Scatter Plot Representing Text Alternative (Chapter 7)
  • A Scatter Plot Represents Text Alternative (Chapter 7)
  • A Scatter Plot Represents Text Alternative (Chapter 7)
  • Figure 7.9 Text Alternative (Chapter 7)
  • Figure 7.10 Text Alternative (Chapter 7)
  • Figure 7.11 Text Alternative (Chapter 7)
  • Figure 7.12 Text Alternative (Chapter 7)
  • A Scatter Plot of Right Foot Text Alternative (Chapter 7)
  • A Scatter Plot of the Frequency Text Alternative (Chapter 7)
  • Scatter Plot of the Vertical Expansion Text Alternative (Chapter 7)
  • Figure 7.13 Text Alternative (Chapter 7)
  • A Scatter Plot of Production Text Alternative (Chapter 7)
  • Figure 7.14 Text Alternative (Chapter 7)
  • Figure 7.15(a) Text Alternative (Chapter 7)
  • Figure 7.15(b) Text Alternative (Chapter 7)
  • Figure 7.16(a) Text Alternative (Chapter 7)
  • Figure 7.16(b) Text Alternative (Chapter 7)
  • Figure 7.17(a) Text Alternative (Chapter 7)
  • Figure 7.17(b) Text Alternative (Chapter 7)
  • Figure 7.18(a) Text Alternative (Chapter 7)
  • Figure 7.18(b) Text Alternative (Chapter 7)
  • Figure 7.19(a) Text Alternative (Chapter 7)
  • Figure 7.19(b) Text Alternative (Chapter 7)
  • Figure 7.20(a) Text Alternative (Chapter 7)
  • Figure 7.20(b) Text Alternative (Chapter 7)
  • Figure 7.21(a) Text Alternative (Chapter 7)
  • Figure 7.21(b) Text Alternative (Chapter 7)
  • Figure 7.22(a) Text Alternative (Chapter 7)
  • Figure 7.22(b) Text Alternative (Chapter 7)
  • Figure 7.23(a) Text Alternative (Chapter 7)
  • Figure 7.23(b) Text Alternative (Chapter 7)
  • Figure 7.23(c) Text Alternative (Chapter 7)
  • Figure 7.24(a) Text Alternative (Chapter 7)
  • Figure 7.24(b) Text Alternative (Chapter 7)
  • Figure 7.24(c) Text Alternative (Chapter 7)
  • Figure 7.24(d) Text Alternative (Chapter 7)
  • Figure 7.25(a) Text Alternative (Chapter 7)
  • Figure 7.25(b) Text Alternative (Chapter 7)
  • Figure 7.25(c) Text Alternative (Chapter 7)
  • Figure 7.25(d) Text Alternative (Chapter 7)
  • Figure 7.26 Text Alternative (Chapter 7)
  • Figure 7.27(a) Text Alternative (Chapter 7)
  • Figure 7.27(b) Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatterplot Representing the Non-Uniform Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Length Along Text Alternative (Chapter 7)
  • A Scatter Plot Represents the Plotting Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • 2 Scatter Plots Consist of Various Text Alternative (Chapter 7)
  • A Scatter Plot of Residual Text Alternative (Chapter 7)
  • Figure 8.1 Text Alternative (Chapter 8)
  • Figure 8.2 Text Alternative (Chapter 8)
  • Figure 8.3 Text Alternative (Chapter 8)
  • A Scatter Plot of Residual Text Alternative (Chapter 8)
  • Figure 8.4 Text Alternative (Chapter 8)
  • Figure 8.4 Text Alternative (Chapter 8)
  • Figure 8.5 Text Alternative (Chapter 8)
  • Figure 8.6 Text Alternative (Chapter 8)
  • A Scatterplot is Shown on a Graph Text Alternative (Chapter 8)
  • A Scatter Plot of Residual Text Alternative (Chapter 8)
  • A Scatter Plot of Residual Text Alternative (Chapter 8)
  • A Scatter Plot of R Subscript 1 Raised Text Alternative (Chapter 8)
  • A Scatterplot is Shown on a Graph Text Alternative (Chapter 8)
  • Figure 8.7 Text Alternative (Chapter 8)
  • A Scatter Plot of Residual Text Alternative (Chapter 8)
  • A Scatter Plot of Residual Text Alternative (Chapter 8)
  • A Scatter Plot of Residual Text Alternative (Chapter 8)
  • A Scatter Plot of Residual Text Alternative (Chapter 8)
  • A Scatter Plot of Residual Text Alternative (Chapter 8)
  • A Scatter Plot of Residual Text Alternative (Chapter 8)
  • A Scatterplot is Shown on a Graph1 Text Alternative (Chapter 8)
  • Figure 9.1 Text Alternative (Chapter 9)
  • Figure 9.2 Text Alternative (Chapter 9)
  • Figure 9.3 Text Alternative (Chapter 9)
  • Figure 9.4 Text Alternative (Chapter 9)
  • Figure 9.5 Text Alternative (Chapter 9)
  • Figure 9.6 Text Alternative (Chapter 9)
  • Figure 9.7 Text Alternative (Chapter 9)
  • Figure 9.8 Text Alternative (Chapter 9)
  • Figure 9.9 Text Alternative (Chapter 9)
  • Figure 9.10 Text Alternative (Chapter 9)
  • A Plot of Sorption Text Alternative (Chapter 9)
  • Figure 9.11 Text Alternative (Chapter 9)
  • Figure 9.12 Text Alternative (Chapter 9)
  • Figure 9.13 Text Alternative (Chapter 9)
  • Figure 10.1 Text Alternative (Chapter 10)
  • Figure 10.2 Text Alternative (Chapter 10)
  • Figure 10.3 Text Alternative (Chapter 10)
  • Figure 10.4 Text Alternative (Chapter 10)
  • Figure 10.5 Text Alternative (Chapter 10)
  • Figure 10.6 Text Alternative (Chapter 10)
  • Figure 10.7 Text Alternative (Chapter 10)
  • Figure 10.8 Text Alternative (Chapter 10)
  • Figure 10.9 Text Alternative (Chapter 10)
  • Figure 10.10 Text Alternative (Chapter 10)
  • Figure 10.11 Text Alternative (Chapter 10)
  • Figure 10.12 Text Alternative (Chapter 10)
  • Figure 10.13 Text Alternative (Chapter 10)
  • The Plot with the Values Ranging Text Alternative (Chapter 10)
  • Figure 10.14 Text Alternative (Chapter 10)
  • Figure 10.15 Text Alternative (Chapter 10)
  • Figure 10.16 Text Alternative (Chapter 10)
  • A Plot of Cumulative Sum Along Text Alternative (Chapter 10)
  • A Plot of Cumulative Sum Along Text Alternative (Chapter 10)
  • A Plot of Cumulative Sum Along Text Alternative (Chapter 10)
  • Figure 10.17 Text Alternative (Chapter 10)
  • Figure 10.18 Text Alternative (Chapter 10)
  • A Plot of Cumulative Sum Along Text Alternative (Chapter 10)
  • The Region Enclosed Under Text Alternative (Appendix A)
  • The Region Enclosed Under Text Alternative (Appendix A)
  • The Region Enclosed Under Text Alternative (Appendix A)
  • A Histogram in the Shape of a Bell Text Alternative (Appendix A)
  • A Histogram is Given in the form of Bell Text Alternative (Appendix A)
  • The Region Enclosed Under Text Alternative (Appendix A)
  • The Region Enclosed Under Text Alternative (Appendix A)
  • The Region Enclosed Under Text Alternative (Appendix A)
  • A Plot of Relative Frequency Text Alternative (Answer)
  • A Dot Plot of Rainfall in Inches Text Alternative (Answer)
  • A Boxplot with Rainfall Text Alternative (Answer)
  • 2 Plots of Relative Frequency Text Alternative (Answer)
  • Two Boxplots, Labeled Catalyst Text Alternative (Answer)
  • A Plot of Frequency Along Text Alternative (Answer)
  • A Plot of Density Along the Vertical Text Alternative (Answer)
  • A Boxplot with Number Absent Text Alternative (Answer)
  • A Boxplot with the Vertical Text Alternative (Answer)
  • A Boxplot with Fracture Stress Text Alternative (Answer)
  • A Dot Plot of Fracture Stress Text Alternative (Answer)
  • A Plot of Density Along Text Alternative (Answer)
  • A Scatter Plot of the Y Along Text Alternative (Answer)
  • A Scatter Plot of the Natural Log Text Alternative (Answer)
  • A Plot of Density Along the Vertical Text Alternative (Answer)
  • A Plot of Density Along the Vertical Text Alternative (Answer)
  • A Plot of Density Along the Vertical Text Alternative (Answer)
  • A Scatter Plot Having Values Along Text Alternative (Answer)
  • A Scatter Plot Having Values Along Text Alternative (Answer)
  • A Scatter Plot Having Values Text Alternative (Answer)
  • A Scatter Plot Having Values Text Alternative (Answer)
  • A Scatter Plot Having Values Text Alternative (Answer)
  • A Dot Plot Representation of a Total Text Alternative (Answer)
  • A Dot Plot Representation of a Total Text Alternative (Answer)
  • A Scatter Plot of Right Text Alternative (Answer)
  • A Scatter Plot of the Frequency Text Alternative (Answer)
  • Scatter Plot of the Vertical Text Alternative (Answer)
  • A Scatter Plot of Residual Along Text Alternative (Answer)
  • A Scatter Plot of Residual Along Text Alternative (Answer)
  • A Scatter Plot of Residual Along Text Alternative (Answer)
  • A Scatter Plot of Residual Along Text Alternative (Answer)
  • A Scatter Plot of Residual Along Text Alternative (Answer)
  • A Scatterplot Representing Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot Represents Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • Lewis Structure of a Compound Text Alternative (Answer)
  • A Scatter Plot of R Subscript Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Scatter Plot of Residual Text Alternative (Answer)
  • A Plot of Sorption in Percentage Text Alternative (Answer)
  • A Plot of Cumulative Sum Along Text Alternative (Answer)
  • A Plot of Cumulative Sum Along Text Alternative (Answer)
  • A Plot of Cumulative Sum Along Text Alternative (Answer)
  • A Plot of Cumulative Sum Along Text Alternative (Answer)

  UM RAFBÆKUR Á HEIMKAUP.IS

  Bókahillan þín er þitt svæði og þar eru bækurnar þínar geymdar. Þú kemst í bókahilluna þína hvar og hvenær sem er í tölvu eða snjalltæki. Einfalt og þægilegt!
  
  Rafbók til eignar
  Rafbók til eignar þarf að hlaða niður á þau tæki sem þú vilt nota innan eins árs frá því bókin er keypt.
  
  Þú kemst í bækurnar hvar sem er
  Þú getur nálgast allar raf(skóla)bækurnar þínar á einu augabragði, hvar og hvenær sem er í bókahillunni þinni. Engin taska, enginn kyndill og ekkert vesen (hvað þá yfirvigt).
  
  Auðvelt að fletta og leita
  Þú getur flakkað milli síðna og kafla eins og þér hentar best og farið beint í ákveðna kafla úr efnisyfirlitinu. Í leitinni finnur þú orð, kafla eða síður í einum smelli.
  
  Glósur og yfirstrikanir
  Þú getur auðkennt textabrot með mismunandi litum og skrifað glósur að vild í rafbókina. Þú getur jafnvel séð glósur og yfirstrikanir hjá bekkjarsystkinum og kennara ef þeir leyfa það. Allt á einum stað.
  
  Hvað viltu sjá? / Þú ræður hvernig síðan lítur út
  Þú lagar síðuna að þínum þörfum. Stækkaðu eða minnkaðu myndir og texta með multi-level zoom til að sjá síðuna eins og þér hentar best í þínu námi.
  
  
  
  Fleiri góðir kostir
  - Þú getur prentað síður úr bókinni (innan þeirra marka sem útgefandinn setur)
  - Möguleiki á tengingu við annað stafrænt og gagnvirkt efni, svo sem myndbönd eða spurningar úr efninu
  - Auðvelt að afrita og líma efni/texta fyrir t.d. heimaverkefni eða ritgerðir
  - Styður tækni sem hjálpar nemendum með sjón- eða heyrnarskerðingu