Efnisyfirlit

• Cover
• Title Page
• Copyright Page
• Contents
• Preface
• 1 Introduction
  • 1.1 Scientific method
    • 1.1.1 Pattern description
    • 1.1.2 Models
    • 1.1.3 Hypotheses and tests
    • 1.1.4 Alternatives to falsification
    • 1.1.5 Role of statistical analysis
  • 1.2 Experiments and other tests
  • 1.3 Data, observations and variables
  • 1.4 Probability
  • 1.5 Probability distributions
    • 1.5.1 Distributions for variables
    • 1.5.2 Distributions for statistics
• 2 Estimation
  • 2.1 Samples and populations
  • 2.2 Common parameters and statistics
    • 2.2.1 Center (location) of distribution
    • 2.2.2 Spread or variability
  • 2.3 Standard errors and confidence intervals for the mean
    • 2.3.1 Normal distributions and the Central Limit Theorem
    • 2.3.2 Standard error of the sample mean
    • 2.3.3 Confidence intervals for population mean
    • 2.3.4 Interpretation of confidence intervals for population mean
    • 2.3.5 Standard errors for other statistics
  • 2.4 Methods for estimating parameters
    • 2.4.1 Maximum likelihood (ML)
    • 2.4.2 Ordinary least squares (OLS)
    • 2.4.3 ML vs OLS estimation
  • 2.5 Resampling methods for estimation
    • 2.5.1 Bootstrap
    • 2.5.2 Jackknife
  • 2.6 Bayesian inference – estimation
    • 2.6.1 Bayesian estimation
    • 2.6.2 Prior knowledge and probability
    • 2.6.3 Likelihood function
    • 2.6.4 Posterior probability
    • 2.6.5 Examples
    • 2.6.6 Other comments
• 3 Hypothesis testing
  • 3.1 Statistical hypothesis testing
    • 3.1.1 Classical statistical hypothesis testing
    • 3.1.2 Associated probability and Type I error
    • 3.1.3 Hypothesis tests for a single population
    • 3.1.4 One- and two-tailed tests
    • 3.1.5 Hypotheses for two populations
    • 3.1.6 Parametric tests and their assumptions
  • 3.2 Decision errors
    • 3.2.1 Type I and II errors
    • 3.2.2 Asymmetry and scalable decision criteria
  • 3.3 Other testing methods
    • 3.3.1 Robust parametric tests
    • 3.3.2 Randomization (permutation) tests
    • 3.3.3 Rank-based non-parametric tests
  • 3.4 Multiple testing
    • 3.4.1 The problem
    • 3.4.2 Adjusting significance levels and/or P values
  • 3.5 Combining results from statistical tests
    • 3.5.1 Combining P values
    • 3.5.2 Meta-analysis
  • 3.6 Critique of statistical hypothesis testing
    • 3.6.1 Dependence on sample size and stopping rules
    • 3.6.2 Sample space – relevance of data not observed
    • 3.6.3 P values as measure of evidence
    • 3.6.4 Null hypothesis always false
    • 3.6.5 Arbitrary significance levels
    • 3.6.6 Alternatives to statistical hypothesis testing
  • 3.7 Bayesian hypothesis testing
• 4 Graphical exploration of data
  • 4.1 Exploratory data analysis
    • 4.1.1 Exploring samples
  • 4.2 Analysis with graphs
    • 4.2.1 Assumptions of parametric linear models
  • 4.3 Transforming data
    • 4.3.1 Transformations and distributional assumptions
    • 4.3.2 Transformations and linearity
    • 4.3.3 Transformations and additivity
  • 4.4 Standardizations
  • 4.5 Outliers
  • 4.6 Censored and missing data
    • 4.6.1 Missing data
    • 4.6.2 Censored (truncated) data
  • 4.7 General issues and hints for analysis
    • 4.7.1 General issues
• 5 Correlation and regression
  • 5.1 Correlation analysis
    • 5.1.1 Parametric correlation model
    • 5.1.2 Robust correlation
    • 5.1.3 Parametric and non-parametric confidence regions
  • 5.2 Linear models
  • 5.3 Linear regression analysis
    • 5.3.1 Simple (bivariate) linear regression
    • 5.3.2 Linear model for regression
    • 5.3.3 Estimating model parameters
    • 5.3.4 Analysis of variance
    • 5.3.5 Null hypotheses in regression
    • 5.3.6 Comparing regression models
    • 5.3.7 Variance explained
    • 5.3.8 Assumptions of regression analysis
    • 5.3.9 Regression diagnostics
    • 5.3.10 Diagnostic graphics
    • 5.3.11 Transformations
    • 5.3.12 Regression through the origin
    • 5.3.13 Weighted least squares
    • 5.3.14 X random (Model II regression)
    • 5.3.15 Robust regression
  • 5.4 Relationship between regression and correlation
  • 5.5 Smoothing
    • 5.5.1 Running means
    • 5.5.2 LO(W)ESS
    • 5.5.3 Splines
    • 5.5.4 Kernels
    • 5.5.5 Other issues
  • 5.6 Power of tests in correlation and regression
  • 5.7 General issues and hints for analysis
    • 5.7.1 General issues
    • 5.7.2 Hints for analysis
• 6 Multiple and complex regression
  • 6.1 Multiple linear regression analysis
    • 6.1.1 Multiple linear regression model
    • 6.1.2 Estimating model parameters
    • 6.1.3 Analysis of variance
    • 6.1.4 Null hypotheses and model comparisons
    • 6.1.5 Variance explained
    • 6.1.6 Which predictors are important?
    • 6.1.7 Assumptions of multiple regression
    • 6.1.8 Regression diagnostics
    • 6.1.9 Diagnostic graphics
    • 6.1.10 Transformations
    • 6.1.11 Collinearity
    • 6.1.12 Interactions in multiple regression
    • 6.1.13 Polynomial regression
    • 6.1.14 Indicator (dummy) variables
    • 6.1.15 Finding the “best” regression model
    • 6.1.16 Hierarchical partitioning
    • 6.1.17 Other issues in multiple linear regression
  • 6.2 Regression trees
  • 6.3 Path analysis and structural equation modeling
  • 6.4 Nonlinear models
  • 6.5 Smoothing and response surfaces
  • 6.6 General issues and hints for analysis
    • 6.6.1 General issues
    • 6.6.2 Hints for analysis
• 7 Design and power analysis
  • 7.1 Sampling
    • 7.1.1 Sampling designs
    • 7.1.2 Size of sample
  • 7.2 Experimental design
    • 7.2.1 Replication
    • 7.2.2 Controls
    • 7.2.3 Randomization
    • 7.2.4 Independence
    • 7.2.5 Reducing unexplained variance
  • 7.3 Power analysis
    • 7.3.1 Using power to plan experiments (a priori power analysis)
    • 7.3.2 Post hoc power calculation
    • 7.3.3 The effect size
    • 7.3.4 Using power analyses
  • 7.4 General issues and hints for analysis
    • 7.4.1 General issues
    • 7.4.2 Hints for analysis
• 8 Comparing groups or treatments – analysis of variance
  • 8.1 Single factor (one way) designs
    • 8.1.1 Types of predictor variables (factors)
    • 8.1.2 Linear model for single factor analyses
    • 8.1.3 Analysis of variance
    • 8.1.4 Null hypotheses
    • 8.1.5 Comparing ANOVA models
    • 8.1.6 Unequal sample sizes (unbalanced designs)
  • 8.2 Factor effects
    • 8.2.1 Random effects: variance components
    • 8.2.2 Fixed effects
  • 8.3 Assumptions
    • 8.3.1 Normality
    • 8.3.2 Variance homogeneity
    • 8.3.3 Independence
  • 8.4 ANOVA diagnostics
  • 8.5 Robust ANOVA
    • 8.5.1 Tests with heterogeneous variances
    • 8.5.2 Rank-based (“non-parametric”) tests
    • 8.5.3 Randomization tests
  • 8.6 Specific comparisons of means
    • 8.6.1 Planned comparisons or contrasts
    • 8.6.2 Unplanned pairwise comparisons
    • 8.6.3 Specific contrasts versus unplanned pairwise comparisons
  • 8.7 Tests for trends
  • 8.8 Testing equality of group variances
  • 8.9 Power of single factor ANOVA
  • 8.10 General issues and hints for analysis
    • 8.10.1 General issues
    • 8.10.2 Hints for analysis
• 9 Multifactor analysis of variance
  • 9.1 Nested (hierarchical) designs
    • 9.1.1 Linear models for nested analyses
    • 9.1.2 Analysis of variance
    • 9.1.3 Null hypotheses
    • 9.1.4 Unequal sample sizes (unbalanced designs)
    • 9.1.5 Comparing ANOVA models
    • 9.1.6 Factor effects in nested models
    • 9.1.7 Assumptions for nested models
    • 9.1.8 Specific comparisons for nested designs
    • 9.1.9 More complex designs
    • 9.1.10 Design and power
  • 9.2 Factorial designs
    • 9.2.1 Linear models for factorial designs
    • 9.2.2 Analysis of variance
    • 9.2.3 Null hypotheses
    • 9.2.4 What are main effects and interactions really measuring?
    • 9.2.5 Comparing ANOVA models
    • 9.2.6 Unbalanced designs
    • 9.2.7 Factor effects
    • 9.2.8 Assumptions
    • 9.2.9 Robust factorial ANOVAs
    • 9.2.10 Specific comparisons on main effects
    • 9.2.11 Interpreting interactions
    • 9.2.12 More complex designs
    • 9.2.13 Power and design in factorial ANOVA
  • 9.3 Pooling in multifactor designs
  • 9.4 Relationship between factorial and nested designs
  • 9.5 General issues and hints for analysis
    • 9.5.1 General issues
    • 9.5.2 Hints for analysis
• 10 Randomized blocks and simple repeated measures: unreplicated two factor designs
  • 10.1 Unreplicated two factor experimental designs
    • 10.1.1 Randomized complete block (RCB) designs
    • 10.1.2 Repeated measures (RM) designs
  • 10.2 Analyzing RCB and RM designs
    • 10.2.1 Linear models for RCB and RM analyses
    • 10.2.2 Analysis of variance
    • 10.2.3 Null hypotheses
    • 10.2.4 Comparing ANOVA models
  • 10.3 Interactions in RCB and RM models
    • 10.3.1 Importance of treatment by block interactions
    • 10.3.2 Checks for interaction in unreplicated designs
  • 10.4 Assumptions
    • 10.4.1 Normality, independence of errors
    • 10.4.2 Variances and covariances – sphericity
    • 10.4.3 Recommended strategy
  • 10.5 Robust RCB and RM analyses
  • 10.6 Specific comparisons
  • 10.7 Efficiency of blocking (to block or not to block?)
  • 10.8 Time as a blocking factor
  • 10.9 Analysis of unbalanced RCB designs
  • 10.10 Power of RCB or simple RM designs
  • 10.11 More complex block designs
    • 10.11.1 Factorial randomized block designs
    • 10.11.2 Incomplete block designs
    • 10.11.3 Latin square designs
    • 10.11.4 Crossover designs
  • 10.12 Generalized randomized block designs
  • 10.13 RCB and RM designs and statistical software
  • 10.14 General issues and hints for analysis
    • 10.14.1 General issues
    • 10.14.2 Hints for analysis
• 11 Split-plot and repeated measures designs: partly nested analyses of variance
  • 11.1 Partly nested designs
    • 11.1.1 Split-plot designs
    • 11.1.2 Repeated measures designs
    • 11.1.3 Reasons for using these designs
  • 11.2 Analyzing partly nested designs
    • 11.2.1 Linear models for partly nested analyses
    • 11.2.2 Analysis of variance
    • 11.2.3 Null hypotheses
    • 11.2.4 Comparing ANOVA models
  • 11.3 Assumptions
    • 11.3.1 Between plots/subjects
    • 11.3.2 Within plots/subjects and multisample sphericity
  • 11.4 Robust partly nested analyses
  • 11.5 Specific comparisons
    • 11.5.1 Main effects
    • 11.5.2 Interactions
    • 11.5.3 Profile (i.e. trend) analysis
  • 11.6 Analysis of unbalanced partly nested designs
  • 11.7 Power for partly nested designs
  • 11.8 More complex designs
    • 11.8.1 Additional between-plots/subjects factors
    • 11.8.2 Additional within-plots/subjects factors
    • 11.8.3 Additional between-plots/subjects and within-plots/subjects factors
    • 11.8.4 General comments about complex designs
  • 11.9 Partly nested designs and statistical software
  • 11.10 General issues and hints for analysis
    • 11.10.1 General issues
    • 11.10.2 Hints for individual analyses
• 12 Analyses of covariance
  • 12.1 Single factor analysis of covariance (ANCOVA)
    • 12.1.1 Linear models for analysis of covariance
    • 12.1.2 Analysis of (co)variance
    • 12.1.3 Null hypotheses
    • 12.1.4 Comparing ANCOVA models
  • 12.2 Assumptions of ANCOVA
    • 12.2.1 Linearity
    • 12.2.2 Covariate values similar across groups
    • 12.2.3 Fixed covariate (X)
  • 12.3 Homogeneous slopes
    • 12.3.1 Testing for homogeneous within-group regression slopes
    • 12.3.2 Dealing with heterogeneous within-group regression slopes
    • 12.3.3 Comparing regression lines
  • 12.4 Robust ANCOVA
  • 12.5 Unequal sample sizes (unbalanced designs)
  • 12.6 Specific comparisons of adjusted means
    • 12.6.1 Planned contrasts
    • 12.6.2 Unplanned comparisons
  • 12.7 More complex designs
    • 12.7.1 Designs with two or more covariates
    • 12.7.2 Factorial designs
    • 12.7.3 Nested designs with one covariate
    • 12.7.4 Partly nested models with one covariate
  • 12.8 General issues and hints for analysis
    • 12.8.1 General issues
    • 12.8.2 Hints for analysis
• 13 Generalized linear models and logistic regression
  • 13.1 Generalized linear models
  • 13.2 Logistic regression
    • 13.2.1 Simple logistic regression
    • 13.2.2 Multiple logistic regression
    • 13.2.3 Categorical predictors
    • 13.2.4 Assumptions of logistic regression
    • 13.2.5 Goodness-of-fit and residuals
    • 13.2.6 Model diagnostics
    • 13.2.7 Model selection
    • 13.2.8 Software for logistic regression
  • 13.3 Poisson regression
  • 13.4 Generalized additive models
  • 13.5 Models for correlated data
    • 13.5.1 Multi-level (random effects) models
    • 13.5.2 Generalized estimating equations
  • 13.6 General issues and hints for analysis
    • 13.6.1 General issues
    • 13.6.2 Hints for analysis
• 14 Analyzing frequencies
  • 14.1 Single variable goodness-of-fit tests
  • 14.2 Contingency tables
    • 14.2.1 Two way tables
    • 14.2.2 Three way tables
  • 14.3 Log-linear models
    • 14.3.1 Two way tables
    • 14.3.2 Log-linear models for three way tables
    • 14.3.3 More complex tables
  • 14.4 General issues and hints for analysis
    • 14.4.1 General issues
    • 14.4.2 Hints for analysis
• 15 Introduction to multivariate analyses
  • 15.1 Multivariate data
  • 15.2 Distributions and associations
  • 15.3 Linear combinations, eigenvectors and eigenvalues
    • 15.3.1 Linear combinations of variables
    • 15.3.2 Eigenvalues
    • 15.3.3 Eigenvectors
    • 15.3.4 Derivation of components
  • 15.4 Multivariate distance and dissimilarity measures
    • 15.4.1 Dissimilarity measures for continuous variables
    • 15.4.2 Dissimilarity measures for dichotomous (binary) variables
    • 15.4.3 General dissimilarity measures for mixed variables
    • 15.4.4 Comparison of dissimilarity measures
  • 15.5 Comparing distance and/or dissimilarity matrices
  • 15.6 Data standardization
  • 15.7 Standardization, association and dissimilarity
  • 15.8 Multivariate graphics
  • 15.9 Screening multivariate data sets
    • 15.9.1 Multivariate outliers
    • 15.9.2 Missing observations
  • 15.10 General issues and hints for analysis
    • 15.10.1 General issues
    • 15.10.2 Hints for analysis
• 16 Multivariate analysis of variance and discriminant analysis
  • 16.1 Multivariate analysis of variance (MANOVA)
    • 16.1.1 Single factor MANOVA
    • 16.1.2 Specific comparisons
    • 16.1.3 Relative importance of each response variable
    • 16.1.4 Assumptions of MANOVA
    • 16.1.5 Robust MANOVA
    • 16.1.6 More complex designs
  • 16.2 Discriminant function analysis
    • 16.2.1 Description and hypothesis testing
    • 16.2.2 Classification and prediction
    • 16.2.3 Assumptions of discriminant function analysis
    • 16.2.4 More complex designs
  • 16.3 MANOVA vs discriminant function analysis
  • 16.4 General issues and hints for analysis
    • 16.4.1 General issues
    • 16.4.2 Hints for analysis
• 17 Principal components and correspondence analysis
  • 17.1 Principal components analysis
    • 17.1.1 Deriving components
    • 17.1.2 Which association matrix to use?
    • 17.1.3 Interpreting the components
    • 17.1.4 Rotation of components
    • 17.1.5 How many components to retain?
    • 17.1.6 Assumptions
    • 17.1.7 Robust PCA
    • 17.1.8 Graphical representations
    • 17.1.9 Other uses of components
  • 17.2 Factor analysis
  • 17.3 Correspondence analysis
    • 17.3.1 Mechanics
    • 17.3.2 Scaling and joint plots
    • 17.3.3 Reciprocal averaging
    • 17.3.4 Use of CA with ecological data
    • 17.3.5 Detrending
  • 17.4 Canonical correlation analysis
  • 17.5 Redundancy analysis
  • 17.6 Canonical correspondence analysis
  • 17.7 Constrained and partial “ordination”
  • 17.8 General issues and hints for analysis
    • 17.8.1 General issues
    • 17.8.2 Hints for analysis
• 18 Multidimensional scaling and cluster analysis
  • 18.1 Multidimensional scaling
    • 18.1.1 Classical scaling – principal coordinates analysis (PCoA)
    • 18.1.2 Enhanced multidimensional scaling
    • 18.1.3 Dissimilarities and testing hypotheses about groups of objects
    • 18.1.4 Relating MDS to original variables
    • 18.1.5 Relating MDS to covariates
  • 18.2 Classification
    • 18.2.1 Cluster analysis
  • 18.3 Scaling (ordination) and clustering for biological data
  • 18.4 General issues and hints for analysis
    • 18.4.1 General issues
    • 18.4.2 Hints for analysis
• 19 Presentation of results
  • 19.1 Presentation of analyses
    • 19.1.1 Linear models
    • 19.1.2 Other analyses
  • 19.2 Layout of tables
  • 19.3 Displaying summaries of the data
    • 19.3.1 Bar graph
    • 19.3.2 Line graph (category plot)
    • 19.3.3 Scatterplots
    • 19.3.4 Pie charts
  • 19.4 Error bars
    • 19.4.1 Alternative approaches
  • 19.5 Oral presentations
    • 19.5.1 Slides, computers, or overheads?
    • 19.5.2 Graphics packages
    • 19.5.3 Working with color
    • 19.5.4 Scanned images
    • 19.5.5 Information content
  • 19.6 General issues and hints
• References
• Index

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