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Understand applied statistics and its application in biology research

Biology and its related sciences generate prodigious quantities of data through experimentation and observation. Interpreting this data and using it to generate statistically defensible inferences has become one of the most significant components of modern biological research. There are, however, very few up-to-date resources by which graduate students and researchers in biology can familiarize themselves with the key methodologies of applied statistics as they specifically connect to the applied life sciences.

Applied Statistics in Biology remedies this oversight with a thorough, accessible overview to statistics and its biological applications. Beginning with the history and fundamentals of statistics, it covers all major statistical modes of analysis that biologists might find useful, with an eye towards a robust quantitative education for biologists. Fully up to date and addressing all conventional approaches to statistical analysis, it's a must-own for biology students and researchers alike.

Applied Statistics in Biology readers will also find:

Treatment rooted in years of graduate teaching in statistics and biology Detailed discussion of topics including regression, "non-Gaussian data," multivariate techniques, and many more A valuable complement to existing resources on applied statistics

Applied Statistics in Biology is ideal for graduate students in agriculture, biology, natural resources, and related fields, as well as for instructors and researchers in these and related subjects.

Tabla de contenidos provista por Syndetics

• Preface(xiii)
• 1 Introduction(1)
• Statistics(1)
• Application of Statistics(1)
• Scientific Method(2)
• Statistical Null Hypothesis(4)
• Type I Error (α)(5)
• Type II Error (β)(5)
• Power of the Test(6)
• P-Value Misuse(8)
• Effect Size(9)
• Diagnostic Tests(10)
• Bias(12)
• Summary(13)
• SAS Code(14)
• R Code(19)
• JMP Method(21)
• References(25)
• Additional Reading(25)
• 2 Data Management(27)
• Data Management Plan(27)
• Organize Files(28)
• Data Workbooks(29)
• Backup(33)
• Securing Data(33)
• Data Analysis(33)
• Data Preservation(34)
• Data Sharing(35)
• Summary(35)
• Additional Reading(35)
• 3 Distributions(37)
• Measures of Central Tendency(37)
• Dispersion(38)
• Accuracy and Precision(41)
• Normal Distribution(42)
• Normal Probability Plot(43)
• Measures of Departures from Normality(44)
• Tests of Normality(45)
• Comparing Distributions(48)
• Comparing Two Mean Estimates(50)
• Student's t-Test(50)
• Wald Z-Test(54)
• Bootstrap(54)
• Summary(57)
• SAS Code(58)
• R Code(63)
• JMP Method(68)
• References(80)
• Additional Reading(81)
• 4 Goodness-of-Fit(83)
• X 2 Distribution(83)
• Enumeration Data(83)
• Two Cell Tests(85)
• Sample Size to Differentiate Alternative Ratios(87)
• Contingency Tests, Goodness-of-Fit(88)
• Contingency Tests, No Expected Distribution(89)
• Meta-Analysis(92)
• Summary(94)
• SAS Code(95)
• R Code(100)
• JMP Method(106)
• References(121)
• Additional Reading(121)
• 5 Variance Analyses-Gaussian(123)
• Factors(123)
• Experimental Unit(124)
• Effect Types(124)
• One-Factor Analysis(125)
• Experimental Error(126)
• F-Distribution(127)
• Replication(128)
• Randomized Complete Block(129)
• Arrangement(130)
• Variance Analysis(132)
• Block: Fixed or Random Effect?(133)
• Mixed Model Analysis(134)
• REML Estimation(135)
• Significance of Effects(136)
• Generalized Linear Mixed Model(137)
• Conditional and Marginal Models(138)
• Covariance Structure(139)
• Negative Variance Estimates(140)
• Means Comparisons(142)
• Contrasts(143)
• Estimate of a Difference(144)
• BLUE and BLUP Estimates(145)
• Multiplicity Adjustment(146)
• Letter Codes(147)
• Test CV(148)
• Power Analyses(148)
• Summary(149)
• SAS Code(149)
• R Code(164)
• JMP Method(173)
• References(187)
• Additional Reading(187)
• 6 Correlation and Regression(189)
• Rank Correlations(191)
• Linear Regression(192)
• Model I(194)
• Model II(194)
• Prediction of Y from X(196)
• Broad and Narrow Inference(197)
• Regression Through the Origin(198)
• Inverse Prediction(198)
• Transformations for Linear Regression(199)
• Nonlinear Regression(203)
• Dosage Response(206)
• Segmented or Spline Regression(208)
• Logistic Regression(209)
• Creating Plots for Publication(213)
• Summary(214)
• SAS Code(214)
• R Code(226)
• JMP Method(236)
• References(277)
• Additional Reading(277)
• 7 Regression in ANOVA(279)
• Unequally Spaced or Unequally Balanced Treatments(281)
• Dummy Variables(284)
• Optimum Treatment Level(286)
• Comparison of Regression Response(287)
• Comparison of Responses(289)
• Non-Gaussian Data(290)
• Summary(291)
• SAS Code(292)
• R Code(300)
• JMP Method(307)
• References(324)
• Additional Reading(324)
• 8 Checking Model Fit(325)
• Violation of Assumptions(326)
• Fit the Model to the Data(326)
• Checking Assumptions(326)
• Residual Types(327)
• Residual Adjustment(327)
• Plots of Residuals(328)
• Model Modifications(335)
• Fit Statistics(337)
• Chi-Square/DF(339)
• Link Function(340)
• Outliers and Influential Observations(340)
• Influence Statistics for Generalized Models(342)
• Pea Study, Epilogue(344)
• Summary(345)
• SAS Code(346)
• R Code(355)
• JMP Method(359)
• References(374)
• Additional Reading(375)
• 9 Non-Gaussian Data(377)
• Denominator df(378)
• Quantitative Data(378)
• Count Data(379)
• Zero-Inflated Models(382)
• Proportion Data, Continuous(383)
• Values of 0 and 1(383)
• Proportion Data, Discrete(384)
• Multinomial Data(386)
• Ordinal Multinomial Analysis(387)
• Nominal Multinomial Analysis(390)
• Compositional Data(392)
• Summary(393)
• SAS Code(394)
• R Code(404)
• JMP Method(410)
• References(424)
• Additional Reading(425)
• 10 Error Control(427)
• Experimental Error(428)
• Variation Within Experimental Units(428)
• Heterogeneity Among Experimental Units(431)
• Analysis of Covariance(431)
• Heterogeneity Within a Study(436)
• Minimizing Heterogeneity(437)
• Post-hoc Detection of Heterogeneity(438)
• Spatial Error-Covariance Adjustment(442)
• Beyond the RCBD(451)
• Latin Square(451)
• Lattice Designs(452)
• Balanced Lattice(452)
• Partially Balanced Lattice(453)
• Simple Lattice Repeated(454)
• Rectangular Lattice(454)
• A-Designs(455)
• Augmented Designs(456)
• Partially Replicated Designs(456)
• Experimental Design Software(459)
• Summary(459)
• SAS Code(460)
• R Code(474)
• JMP Method(482)
• References(503)
• Additional Reading(504)
• 11 Factorial Experiments(507)
• Expected Mean Squares(509)
• Estimation of Variance Components(512)
• Subsampling(513)
• Two-Factor Factorials(515)
• Three-Factor Factorials(515)
• Split-Plot(516)
• Do Not Under- or Over-Specify(518)
• Model Specification(518)
• Bias Correction(521)
• Split-Block(523)
• Repeated Measures(524)
• Correlated Errors Are Not Restricted to Time(527)
• Selection of Covariance Structure(527)
• Repeated Measures, Non-Gaussian(529)
• No Convergence(532)
• Adjusting for Baseline(533)
• Combined Experiments(535)
• Coefficients for Contrasts and Estimates(539)
• Investigating Interactions(542)
• Fixed, Random, or a Bit of Both?(544)
• Summary(545)
• SAS Code(545)
• R Code(571)
• JMP Method(579)
• References(609)
• Additional Reading(610)
• 12 Response Surface(613)
• First-Order Designs(614)
• Second-Order Designs(615)
• Central Composite Design(615)
• Central Rotatable Composite Design(616)
• Mixture and Double Mixture Designs(618)
• Plotting Response Surfaces(623)
• Hoerl and Spline Models(624)
• Avoid Extrapolation(624)
• Summary(627)
• SAS Code(627)
• R Code(643)
• JMP Method(649)
• References(661)
• Additional Reading(661)
• 13 Multiple Regression(663)
• Linear Model(663)
• Assumptions(664)
• Variable Selection-Fixed Effect Models(664)
• Variable Selection-Mixed Models(666)
• Multimodel Inference(668)
• Collinear Variables(670)
• Variance Inflation Factor(670)
• Collinearity Diagnostics(671)
• Adjusting Collinear Variables(672)
• Polynomial Models(672)
• Prediction Models Involving Collinear Variables(673)
• Cross-Validation(673)
• Model Validation(674)
• Latent Factor Regression(675)
• Summary(680)
• SAS Code(681)
• R Code(692)
• JMP Method(697)
• References(714)
• Additional Reading(714)
• 14 Multivariate Analyses(717)
• Analyses of Dependence(717)
• Genotypic Correlations(719)
• Path Analysis(720)
• Analyses of Interdependence(722)
• Assumptions(723)
• Example Multivariate Dataset (Grin)(723)
• Dimension Reduction(726)
• Value of Variables(728)
• Number of Components/Factors(729)
• Clustering(733)
• Distance Measures(735)
• Cluster Methods(737)
• Number of Clusters(739)
• Groupings Unknown(740)
• Partialling Out(741)
• Cluster Validation(743)
• Groupings Known(745)
• Canonical Correlation Analysis(746)
• Canonical Discriminant Analysis(746)
• Comparing Distance Matrices(750)
• Summary(752)
• SAS Code(753)
• R Code(768)
• JMP Method(774)
• References(798)
• Additional Reading(799)
• 15 GxE Analysis(801)
• Fixed or Random Environments?(802)
• I. Univariate Models(803)
• Mean-CV(803)
• Regression Coefficient(805)
• Regression Deviation(806)
• Random Environment Effect(807)
• Yield Stability Index(810)
• Superiority Measure(811)
• II. Multivariate Models(812)
• Biplots(816)
• Confidence Intervals(820)
• AMMI or GGE?(821)
• GxE Analyses-Summary(822)
• SAS Code(822)
• R Code(837)
• JMP Method(847)
• References(863)
• Additional Reading(864)

Notas de autor provistas por Syndetics

Edzard van Santen, PhD Professor in the Department of Agronomy, University of Florida, USA.