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The evolution of a classic The new 12th edition of Introduction to Genetic Analysis takes this cornerstone textbook to the next level. The hallmark focuses on genetic analysis, quantitative problem solving, and experimentation continue in this new edition while incorporating robust updates to the science. Introduction to Genetic Analysis is now supported in Achieve, Macmillan’s new online learning platform.
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- Höfundar: Michael C. Whitlock, Dolph Schluter
- Útgáfa:3
- Útgáfudagur: 15-03-2020
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- Format:ePub
- ISBN 13: 9781319325350
- Print ISBN: 9781319325343
- ISBN 10: 1319325351
Efnisyfirlit
- About this Book
- Cover Page
- Halftitle Page
- Title Page
- Copyright Page
- Dedication
- Contents in brief
- Contents
- Preface
- About the authors
- Acknowledgments
- SaplingPlus for Statistics: Course Resources
- Online Resources for Students
- Chapter 1 Statistics and samples
- 1.1 What is statistics?
- 1.2 Sampling Populations
- Populations and samples
- Properties of good samples
- Random sampling
- How to take a random sample
- The sample of convenience
- Volunteer bias
- Data in the real world
- 1.3 Types of Data and Variables
- Categorical and numerical variables
- Explanatory and response variables
- 1.4 Frequency distributions and probability distributions
- 1.5 Types of studies
- 1.6 Summary
- Chapter 1 Problems
- Practice problems
- Assignment problems
- 2.1 Guidelines for effective graphs
- How to draw a bad graph
- How to draw a good graph
- 2.2 Showing data for one variable
- Showing categorical data: frequency table and bar graph
- Making a good bar graph
- A bar graph is usually better than a pie chart
- Showing numerical data: frequency table and histogram
- Describing the shape of a histogram
- How to draw a good histogram
- Other graphs for numerical data
- 2.3 Showing association between two variables and differences between groups
- Showing association between categorical variables
- Showing association between numerical variables: scatter plot
- Showing association between a numerical and a categorical variable
- 2.4 Showing trends in time and space
- 2.5 How to make good tables
- Follow similar principles for display tables
- 2.6 How to make data files
- 2.7 Summary
- Chapter 2 Problems
- Practice problems
- Assignment problems
- 3.1 Arithmetic mean and standard deviation
- The sample mean
- Variance and standard deviation
- Rounding means, standard deviations, and other quantities
- Coefficient of variation
- Calculating mean and standard deviation from a frequency table
- Effect of changing measurement scale
- 3.2 Median and interquartile range
- The median
- The interquartile range
- The box plot
- 3.3 How measures of location and spread compare
- Mean versus median
- Standard deviation versus interquartile range
- 3.4 Cumulative frequency distribution
- Percentiles and quantiles
- Displaying cumulative relative frequencies
- 3.5 Proportions
- Calculating a proportion
- The proportion is like a sample mean
- 3.6 Summary
- 3.7 Quick formula summary
- Table of formulas for descriptive statistics
- Chapter 3 Problems
- Practice problems
- Assignment problems
- 4.1 The sampling distribution of an estimate
- Estimating mean gene length with a random sample
- The sampling distribution of Y¯
- 4.2 Measuring the uncertainty of an estimate
- Standard error
- The standard error of Y¯
- The standard error of Y¯ from data
- 4.3 Confidence intervals
- The 2SE rule of thumb
- 4.4 Error bars
- 4.5 Summary
- 4.6 Quick formula summary
- Standard error of the mean
- Chapter 4 Problems
- Practice problems
- Assignment problems
- 5.1 The probability of an event
- 5.2 Venn diagrams
- 5.3 Mutually exclusive events
- 5.4 Probability distributions
- Discrete probability distributions
- Continuous probability distributions
- 5.5 Either this or that: adding probabilities
- The addition rule
- The probabilities of all possible mutually exclusive outcomes add to one
- The general addition rule
- 5.6 Independence and the multiplication rule
- Multiplication rule
- “And” versus “or”
- Independence of more than two events
- 5.7 Probability trees
- 5.8 Dependent events
- 5.9 Conditional probability and Bayes’ theorem
- Conditional probability
- The general multiplication rule
- Sampling without replacement
- Bayes’ theorem
- 5.10 Summary
- Chapter 5 Problems
- Practice problems
- Assignment problems
- 6.1 Making and using statistical hypotheses
- Null hypothesis
- Alternative hypothesis
- To reject or not to reject
- 6.2 Hypothesis testing: an example
- Stating the hypotheses
- The test statistic
- The null distribution
- Quantifying uncertainty: the P-value
- Draw the appropriate conclusion
- Reporting the results
- 6.3 Errors in hypothesis testing
- Type I and Type II errors
- 6.4 When the null hypothesis is not rejected
- The test
- Interpreting a nonsignificant result
- 6.5 One-sided tests
- 6.6 Hypothesis testing versus confidence intervals
- 6.7 Summary
- Chapter 6 Problems
- Practice problems
- Assignment problems
- 7.1 The binomial distribution
- Formula for the binomial distribution
- Number of successes in a random sample
- Sampling distribution of the proportion
- 7.2 Testing a proportion: the binomial test
- Approximations for the binomial test
- 7.3 Estimating proportions
- Estimating the standard error of a proportion
- Confidence intervals for proportions—the Agresti–Coull method
- Confidence intervals for proportions—the Wald method
- 7.4 Deriving the binomial distribution
- 7.5 Summary
- 7.6 Quick formula summary
- Binomial distribution
- Proportion
- Agresti–Coull 95% confidence interval for a proportion
- Binomial test
- Chapter 7 Problems
- Practice problems
- Assignment problems
- 8.1 χ2 Goodness-of-fit test: the proportional model
- Null and alternative hypotheses
- Observed and expected frequencies
- The χ2 test statistic
- The sampling distribution of χ2 under the null hypothesis
- Calculating the P-value
- Critical values for the χ2 distribution
- 8.2 Assumptions of the χ2 goodness-of-fit test
- 8.3 Goodness-of-fit tests when there are only two categories
- 8.4 Random in space or time: the Poisson distribution
- Formula for the Poisson distribution
- Testing randomness with the Poisson distribution
- Comparing the variance to the mean
- 8.5 Summary
- 8.6 Quick formula summary
- χ2 Goodness-of-fit test
- Test statistic: χ2
- Poisson distribution
- Chapter 8 Problems
- Practice problems
- Assignment problems
- 9.1 Associating two categorical variables
- 9.2 Estimating association in 2×2 tables: relative risk
- Relative risk
- Reduction in risk
- 9.3 Estimating association in 2×2 tables: the odds ratio
- Odds
- Odds ratio
- Standard error and confidence interval for odds ratio
- Odds ratio vs. relative risk
- 9.4 The χ2 contingency test
- Hypotheses
- Expected frequencies assuming independence
- The χ2 statistic
- Degrees of freedom
- P-value and conclusion
- A shortcut for calculating the expected frequencies
- The χ2 contingency test is a special case of the χ2 goodness-of-fit test
- Assumptions of the χ2 contingency test
- Correction for continuity
- 9.5 Fisher’s exact test
- 9.6 Summary
- 9.7 Quick formula summary
- Confidence interval for relative risk
- Confidence interval for odds ratio
- The χ2 contingency test
- Fisher’s exact test
- Chapter 9 Problems
- Practice problems
- Assignment problems
- 10.1 Bell-shaped curves and the normal distribution
- 10.2 The formula for the normal distribution
- 10.3 Properties of the normal distribution
- 10.4 The standard normal distribution and statistical tables
- Using the standard normal table
- Using the standard normal to describe any normal distribution
- 10.5 The normal distribution of sample means
- Calculating probabilities of sample means
- 10.6 Central limit theorem
- 10.7 Normal approximation to the binomial distribution
- 10.8 Summary
- 10.9 Quick formula summary
- Z-standardization
- Normal approximation to the binomial distribution
- Chapter 10 Problems
- Practice problems
- Assignment problems
- 11.1 The t-distribution for sample means
- Student’s t-distribution
- Finding critical values of the t-distribution
- 11.2 The confidence interval for the mean of a normal distribution
- The 95% confidence interval for the mean
- The 99% confidence interval for the mean
- 11.3 The one-sample t-test
- The effects of larger sample size: body temperature revisited
- 11.4 Assumptions of the one-sample t-test
- 11.5 Estimating the standard deviation and variance of a normal population
- Confidence limits for the variance
- Confidence limits for the standard deviation
- Assumptions
- 11.6 Summary
- 11.7 Quick formula summary
- Confidence interval for a mean
- One-sample t-test
- Confidence interval for variance
- Chapter 11 Problems
- Practice problems
- Assignment problems
- 12.1 Paired sample versus two independent samples
- 12.2 Paired comparison of means
- Estimating mean difference from paired data
- Paired t-test
- Assumptions
- 12.3 Two-sample comparison of means
- Confidence interval for the difference between two means
- Two-sample t-test
- Assumptions
- Welch’s t-test
- 12.4 Using the correct sampling units
- 12.5 The fallacy of indirect comparison
- 12.6 Interpreting overlap of confidence intervals
- 12.7 Comparing variances
- The F-test of equal variances
- Levene’s test for homogeneity of variances
- 12.8 Summary
- 12.9 Quick formula summary
- Confidence interval for the mean difference (paired data)
- Paired t-test
- Standard error of difference between two means
- Confidence interval for the difference between two means (two samples)
- Two-sample t-test
- Welch’s confidence interval for the difference between two means
- Welch’s approximate t-test
- F-test
- Levene’s test
- Chapter 12 Problems
- Practice problems
- Assignment problems
- 13.1 Detecting deviations from normality
- Graphical methods
- Formal test of normality
- 13.2 When to ignore violations of assumptions
- Violations of normality
- Unequal standard deviations
- 13.3 Data transformations
- Log transformation
- Other transformations
- Confidence intervals with transformations
- Avoid multiple testing with transformations
- 13.4 Nonparametric alternatives to one-sample and paired t-tests
- Sign test
- The Wilcoxon signed-rank test
- 13.5 Comparing two groups: the Mann–Whitney U-test
- Tied ranks
- Large samples and the normal approximation
- 13.6 Assumptions of nonparametric tests
- 13.7 Type I and Type II error rates of nonparametric methods
- 13.8 Permutation tests
- Assumptions of permutation tests
- 13.9 Summary
- 13.10 Quick formula summary
- Transformations
- Back-transformations
- Sign test
- Mann–Whitney U-test
- Chapter 13 Problems
- Practice problems
- Assignment problems
- 14.1 Lessons from clinical trials
- Design components
- 14.2 How to reduce bias
- Simultaneous control group
- Randomization
- Blinding
- 14.3 How to reduce the influence of sampling error
- Replication
- Balance
- Blocking
- Extreme treatments
- 14.4 Experiments with more than one factor
- 14.5 What if you can’t do experiments?
- Match and adjust
- 14.6 Choosing a sample size
- Plan for precision
- Plan for power
- Plan for data loss
- 14.7 Summary
- 14.8 Quick formula summary
- Planning for precision
- Planning for power
- Chapter 14 Problems
- Practice problems
- Assignment problems
- 15.1 The analysis of variance
- Hypotheses
- ANOVA in a nutshell
- ANOVA tables
- Partitioning the sum of squares
- Calculating the mean squares
- The variance ratio, F
- Variation explained: R^2
- ANOVA with two groups
- 15.2 Assumptions and alternatives
- The robustness of ANOVA
- Data transformations
- Nonparametric alternatives to ANOVA
- 15.3 Planned comparisons
- Planned comparison between two means
- 15.4 Unplanned comparisons
- Testing all pairs of means using the Tukey–Kramer method
- Assumptions
- 15.5 Fixed and random effects
- 15.6 ANOVA with randomly chosen groups
- ANOVA calculations
- Variance components
- Repeatability
- Assumptions
- 15.7 Summary
- 15.8 Quick formula summary
- Analysis of variance (ANOVA)
- Kruskal–Wallis test
- Planned confidence interval for the difference between two of k means
- Planned test of the difference between two of k means
- Tukey–Kramer test of all pairs of means
- Repeatability and variance components
- Chapter 15 Problems
- Practice problems
- Assignment problems
- 16.1 Estimating a linear correlation coefficient
- The correlation coefficient
- Standard error
- Approximate confidence interval
- 16.2 Testing the null hypothesis of zero correlation
- 16.3 Assumptions
- 16.4 The correlation coefficient depends on the range
- 16.5 Spearman’s rank correlation
- Procedure for large n
- Assumptions of Spearman’s correlation
- 16.6 The effects of measurement error on correlation
- 16.7 Summary
- 16.8 Quick formula summary
- Shortcuts
- Covariance
- Correlation coefficient
- Confidence interval (approximate) for a population correlation
- The t-test of zero linear correlation
- Spearman’s rank correlation
- Spearman’s rank correlation test
- Correlation corrected for measurement error
- Chapter 16 Problems
- Practice problems
- Assignment problems
- 17.1 Linear regression
- The method of least squares
- Formula for the line
- Calculating the slope and intercept
- Populations and samples
- Predicted values
- Residuals
- Standard error of slope
- Confidence interval for the slope
- 17.2 Confidence in predictions
- Confidence intervals for predictions
- Extrapolation
- 17.3 Testing hypotheses about a slope
- The t-test of regression slope
- The ANOVA approach
- 17.4 Regression toward the mean
- 17.5 Assumptions of regression
- Outliers
- Detecting nonlinearity
- Detecting non-normality and unequal variance
- 17.6 Transformations
- 17.7 The effects of measurement error on regression
- 17.8 Regression with nonlinear relationships
- A curve with an asymptote
- Quadratic curves
- Formula-free curve fitting
- 17.9 Logistic regression: fitting a binary response variable
- 17.10 Summary
- 17.11 Quick formula summary
- Shortcuts
- Regression slope
- Regression intercept
- Confidence interval for the regression slope
- Confidence interval for the predicted mean Y at a given X (confidence bands)
- Confidence interval for the predicted individual Y at a given X (prediction intervals)
- The t-test of a regression slope
- The ANOVA method for testing zero slope
- Chapter 17 Problems
- Practice problems
- Assignment problems
- 18.1 ANOVA and linear regression are linear models
- Modeling with linear regression
- Generalizing linear regression
- Linear models
- 18.2 Analyzing experiments with blocking
- Analyzing data from a randomized block design
- Model formula
- Fitting the model to data
- 18.3 Analyzing factorial designs
- Model formula
- Testing the factors
- The importance of distinguishing fixed and random factors
- 18.4 Adjusting for the effects of a covariate
- Testing interaction
- Fitting a model without an interaction term
- 18.5 Assumptions of linear models
- 18.6 Summary
- Chapter 18 Problems
- Practice problems
- Assignment problems
- 19.1 Hypothesis testing using simulation
- 19.2 Bootstrap standard errors and confidence intervals
- Bootstrap standard error
- Confidence intervals by bootstrapping
- Bootstrapping with multiple groups
- Assumptions and limitations of the bootstrap
- 19.3 Summary
- Chapter 19 Problems
- Practice problems
- Assignment problems
- 20.1 What is likelihood?
- 20.2 Two uses of likelihood in biology
- Phylogeny estimation
- Gene mapping
- 20.3 Maximum likelihood estimation
- Probability model
- The likelihood formula
- The maximum likelihood estimate
- Likelihood-based confidence intervals
- 20.4 Versatility of maximum likelihood estimation
- Probability model
- The likelihood formula
- The maximum likelihood estimate
- Bias
- 20.5 Log-likelihood ratio test
- Likelihood ratio test statistic
- Testing a population proportion
- 20.6 Summary
- 20.7 Quick formula summary
- Likelihood
- Likelihood-based confidence interval for a single parameter
- Log-likelihood ratio test for a single parameter
- Chapter 20 Problems
- Practice problems
- Assignment problems
- 21.1 Survival curves
- Calculation summary
- Confidence intervals
- Median survival time
- Assumptions
- 21.2 Compare survival curves
- Hazard ratio
- Hazard ratio calculation
- Logrank test
- Assumptions
- 21.3 Summary
- 21.4 Quick formula summary
- Hazard ratio
- 95% Confidence interval for the hazard ratio
- Logrank test
- Chapter 21 Problems
- Practice problems
- Assignment problems
- Using statistical tables
- Statistical Table A: The χ2 distribution
- Statistical Table B: The standard normal (Z) distribution
- Statistical Table C: Student’s t-distribution
- Statistical Table D: The F-distribution
- Statistical Table E: Mann–Whitney U-distribution
- Statistical Table F: Tukey–Kramer q-distribution
- Statistical Table G: Critical values for the Spearman’s rank correlation
- Chapter 1
- Chapter 2
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- Chapter 9
- Review 1
- Chapter 10
- Chapter 11
- Chapter 12
- Chapter 13
- Review 2
- Chapter 14
- Chapter 15
- Chapter 16
- Chapter 17
- Review 3
- Chapter 18
- Chapter 19
- Chapter 20
- Chapter 21
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