{"product_id":"applied-statistics-in-biology-a-practical-guide-using-sas-r-and-jmp-paperback-softback-9780891183945","title":"Applied Statistics in Biology; A Practical Guide Using SAS, R, and JMP (Paperback \/ softback) 9780891183945","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eApplied Statistics in Biology\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Practical Guide Using SAS, R, and JMP\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eStephen R. Bowley (Author), Edzard van Santen (Author), Simon Riley (Author), Diane K. Michelson (Author), Ruth M. Hummel (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780891183945, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 25 September 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e880 pages\u003cbr\u003e27.4 x 21.6 x 3.6 cm, 1.746 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eUnderstand applied statistics and its application in biology research\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eBiology 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. \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eApplied Statistics in Biology\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eApplied Statistics in Biology\u003c\/i\u003e readers will also find: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTreatment rooted in years of graduate teaching in statistics and biology\u003c\/li\u003e\n\u003cli\u003eDetailed discussion of topics including regression, “non-Gaussian data,” multivariate techniques, and many more\u003c\/li\u003e\n\u003cli\u003eA valuable complement to existing resources on applied statistics\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Statistics in Biology\u003c\/i\u003e 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStatistics, 1\u003c\/p\u003e \u003cp\u003eApplication of Statistics, 1\u003c\/p\u003e \u003cp\u003eScientific Method, 2\u003c\/p\u003e \u003cp\u003eStatistical Null Hypothesis, 4\u003c\/p\u003e \u003cp\u003eType I Error (α), 5\u003c\/p\u003e \u003cp\u003eType II Error (β), 5\u003c\/p\u003e \u003cp\u003ePower of the Test, 6\u003cbr\u003e \u003ci\u003eP-Value Misuse, 8\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eEffect Size, 9\u003c\/p\u003e \u003cp\u003eDiagnostic Tests, 10\u003c\/p\u003e \u003cp\u003eBias, 12\u003c\/p\u003e \u003cp\u003eSummary, 13\u003c\/p\u003e \u003cp\u003eSAS Code, 14\u003c\/p\u003e \u003cp\u003eR Code, 19\u003c\/p\u003e \u003cp\u003eJMP Method, 21\u003c\/p\u003e \u003cp\u003eReferences, 25\u003c\/p\u003e \u003cp\u003eAdditional Reading, 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Data Management 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Management Plan, 27\u003c\/p\u003e \u003cp\u003eOrganize Files, 28\u003c\/p\u003e \u003cp\u003eData Workbooks, 29\u003c\/p\u003e \u003cp\u003eBackup, 33\u003c\/p\u003e \u003cp\u003eSecuring Data, 33\u003c\/p\u003e \u003cp\u003eData Analysis, 33\u003c\/p\u003e \u003cp\u003eData Preservation, 34\u003c\/p\u003e \u003cp\u003eData Sharing, 35\u003c\/p\u003e \u003cp\u003eSummary, 35\u003c\/p\u003e \u003cp\u003eAdditional Reading, 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Distributions 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMeasures of Central Tendency, 37\u003c\/p\u003e \u003cp\u003eDispersion, 38\u003c\/p\u003e \u003cp\u003eAccuracy and Precision, 41\u003c\/p\u003e \u003cp\u003eNormal Distribution, 42\u003c\/p\u003e \u003cp\u003eNormal Probability Plot, 43\u003c\/p\u003e \u003cp\u003eMeasures of Departures from Normality, 44\u003c\/p\u003e \u003cp\u003eTests of Normality, 45\u003c\/p\u003e \u003cp\u003eComparing Distributions, 48\u003c\/p\u003e \u003cp\u003eComparing Two Mean Estimates, 50\u003c\/p\u003e \u003cp\u003eStudent’s t-Test, 50\u003c\/p\u003e \u003cp\u003eWald Z-Test, 54\u003c\/p\u003e \u003cp\u003eBootstrap, 54\u003c\/p\u003e \u003cp\u003eSummary, 57\u003c\/p\u003e \u003cp\u003eSAS Code, 58\u003c\/p\u003e \u003cp\u003eR Code, 63\u003c\/p\u003e \u003cp\u003eJMP Method, 68\u003c\/p\u003e \u003cp\u003eReferences, 80\u003c\/p\u003e \u003cp\u003eAdditional Reading, 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Goodness-of-fit 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eχ 2 Distribution, 83\u003c\/p\u003e \u003cp\u003eEnumeration Data, 83\u003c\/p\u003e \u003cp\u003eTwo Cell Tests, 85\u003c\/p\u003e \u003cp\u003eSample Size to Differentiate Alternative Ratios, 87\u003c\/p\u003e \u003cp\u003eContingency Tests, Goodness-of-Fit, 88\u003c\/p\u003e \u003cp\u003eContingency Tests, No Expected Distribution, 89\u003c\/p\u003e \u003cp\u003eMeta-Analysis, 92\u003c\/p\u003e \u003cp\u003eSummary, 94\u003c\/p\u003e \u003cp\u003eSAS Code, 95\u003c\/p\u003e \u003cp\u003eR Code, 100\u003c\/p\u003e \u003cp\u003eJMP Method, 106\u003c\/p\u003e \u003cp\u003eReferences, 121\u003c\/p\u003e \u003cp\u003eAdditional Reading, 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Variance Analyses—gaussian 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFactors, 123\u003c\/p\u003e \u003cp\u003eExperimental Unit, 124\u003c\/p\u003e \u003cp\u003eEffect Types, 124\u003c\/p\u003e \u003cp\u003eOne-Factor Analysis, 125\u003c\/p\u003e \u003cp\u003eExperimental Error, 126\u003c\/p\u003e \u003cp\u003eF-Distribution, 127\u003c\/p\u003e \u003cp\u003eReplication, 128\u003c\/p\u003e \u003cp\u003eRandomized Complete Block, 129\u003c\/p\u003e \u003cp\u003eArrangement, 130\u003c\/p\u003e \u003cp\u003eVariance Analysis, 132\u003c\/p\u003e \u003cp\u003eBlock: Fixed or Random Effect?, 133\u003c\/p\u003e \u003cp\u003eMixed Model Analysis, 134\u003c\/p\u003e \u003cp\u003eREML Estimation, 135\u003c\/p\u003e \u003cp\u003eSignificance of Effects, 136\u003c\/p\u003e \u003cp\u003eGeneralized Linear Mixed Model, 137\u003c\/p\u003e \u003cp\u003eConditional and Marginal Models, 138\u003c\/p\u003e \u003cp\u003eCovariance Structure, 139\u003c\/p\u003e \u003cp\u003eNegative Variance Estimates, 140\u003c\/p\u003e \u003cp\u003eMeans Comparisons, 142\u003c\/p\u003e \u003cp\u003eContrasts, 143\u003c\/p\u003e \u003cp\u003eEstimate of a Difference, 144\u003c\/p\u003e \u003cp\u003eBLUE and BLUP Estimates, 145\u003c\/p\u003e \u003cp\u003eMultiplicity Adjustment, 146\u003c\/p\u003e \u003cp\u003eLetter Codes, 147\u003c\/p\u003e \u003cp\u003eTest CV, 148\u003c\/p\u003e \u003cp\u003ePower Analyses, 148\u003c\/p\u003e \u003cp\u003eSummary, 149\u003c\/p\u003e \u003cp\u003eSAS Code, 149\u003c\/p\u003e \u003cp\u003eR Code, 164\u003c\/p\u003e \u003cp\u003eJMP Method, 173\u003c\/p\u003e \u003cp\u003eReferences, 187\u003c\/p\u003e \u003cp\u003eAdditional Reading, 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Correlation and Regression 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRank Correlations, 191\u003c\/p\u003e \u003cp\u003eLinear Regression, 192\u003c\/p\u003e \u003cp\u003eModel I, 194\u003c\/p\u003e \u003cp\u003eModel II, 194\u003c\/p\u003e \u003cp\u003ePrediction of Y from X, 196\u003c\/p\u003e \u003cp\u003eBroad and Narrow Inference, 197\u003c\/p\u003e \u003cp\u003eRegression Through the Origin, 198\u003c\/p\u003e \u003cp\u003eInverse Prediction, 198\u003c\/p\u003e \u003cp\u003eTransformations for Linear Regression, 199\u003c\/p\u003e \u003cp\u003eNonlinear Regression, 203\u003c\/p\u003e \u003cp\u003eDosage Response, 206\u003c\/p\u003e \u003cp\u003eSegmented or Spline Regression, 208\u003c\/p\u003e \u003cp\u003eLogistic Regression, 209\u003c\/p\u003e \u003cp\u003eCreating Plots for Publication, 213\u003c\/p\u003e \u003cp\u003eSummary, 214\u003c\/p\u003e \u003cp\u003eSAS Code, 214\u003c\/p\u003e \u003cp\u003eR Code, 226\u003c\/p\u003e \u003cp\u003eJMP Method, 236\u003c\/p\u003e \u003cp\u003eReferences, 277\u003c\/p\u003e \u003cp\u003eAdditional Reading, 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Regression in Anova 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnequally Spaced or Unequally Balanced Treatments, 281\u003c\/p\u003e \u003cp\u003eDummy Variables, 284\u003c\/p\u003e \u003cp\u003eOptimum Treatment Level, 286\u003c\/p\u003e \u003cp\u003eComparison of Regression Response, 287\u003c\/p\u003e \u003cp\u003eComparison of Responses, 289\u003c\/p\u003e \u003cp\u003eNon-Gaussian Data, 290\u003c\/p\u003e \u003cp\u003eSummary, 291\u003c\/p\u003e \u003cp\u003eSAS Code, 292\u003c\/p\u003e \u003cp\u003eR Code, 300\u003c\/p\u003e \u003cp\u003eJMP Method, 307\u003c\/p\u003e \u003cp\u003eReferences, 324\u003c\/p\u003e \u003cp\u003eAdditional Reading, 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Checking Model Fit 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eViolation of Assumptions, 326\u003c\/p\u003e \u003cp\u003eFit the Model to the Data, 326\u003c\/p\u003e \u003cp\u003eChecking Assumptions, 326\u003c\/p\u003e \u003cp\u003eResidual Types, 327\u003c\/p\u003e \u003cp\u003eResidual Adjustment, 327\u003c\/p\u003e \u003cp\u003ePlots of Residuals, 328\u003c\/p\u003e \u003cp\u003eModel Modifications, 335\u003c\/p\u003e \u003cp\u003eFit Statistics, 337\u003c\/p\u003e \u003cp\u003eChi-Square\/DF, 339\u003c\/p\u003e \u003cp\u003eLink Function, 340\u003c\/p\u003e \u003cp\u003eOutliers and Influential Observations, 340\u003c\/p\u003e \u003cp\u003eInfluence Statistics for Generalized Models, 342\u003c\/p\u003e \u003cp\u003ePea Study, Epilogue, 344\u003c\/p\u003e \u003cp\u003eSummary, 345\u003c\/p\u003e \u003cp\u003eSAS Code, 346\u003c\/p\u003e \u003cp\u003eR Code, 355\u003c\/p\u003e \u003cp\u003eJMP Method, 359\u003c\/p\u003e \u003cp\u003eReferences, 374\u003c\/p\u003e \u003cp\u003eAdditional Reading, 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Non-gaussian Data 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDenominator df, 378\u003c\/p\u003e \u003cp\u003eQuantitative Data, 378\u003c\/p\u003e \u003cp\u003eCount Data, 379\u003c\/p\u003e \u003cp\u003eZero-Inflated Models, 382\u003c\/p\u003e \u003cp\u003eProportion Data, Continuous, 383\u003c\/p\u003e \u003cp\u003eValues of 0 and 1, 383\u003c\/p\u003e \u003cp\u003eProportion Data, Discrete, 384\u003c\/p\u003e \u003cp\u003eMultinomial Data, 386\u003c\/p\u003e \u003cp\u003eOrdinal Multinomial Analysis, 387\u003c\/p\u003e \u003cp\u003eNominal Multinomial Analysis, 390\u003c\/p\u003e \u003cp\u003eCompositional Data, 392\u003c\/p\u003e \u003cp\u003eSummary, 393\u003c\/p\u003e \u003cp\u003eSAS Code, 394\u003c\/p\u003e \u003cp\u003eR Code, 404\u003c\/p\u003e \u003cp\u003eJMP Method, 410\u003c\/p\u003e \u003cp\u003eReferences, 424\u003c\/p\u003e \u003cp\u003eAdditional Reading, 425\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Error Control 427\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExperimental Error, 428\u003c\/p\u003e \u003cp\u003eVariation Within Experimental Units, 428\u003c\/p\u003e \u003cp\u003eHeterogeneity Among Experimental Units, 431\u003c\/p\u003e \u003cp\u003eAnalysis of Covariance, 431\u003c\/p\u003e \u003cp\u003eHeterogeneity Within a Study, 436\u003c\/p\u003e \u003cp\u003eMinimizing Heterogeneity, 437\u003c\/p\u003e \u003cp\u003ePost-hoc Detection of Heterogeneity, 438\u003c\/p\u003e \u003cp\u003eSpatial Error–Covariance Adjustment, 442\u003c\/p\u003e \u003cp\u003eBeyond the RCBD, 451\u003c\/p\u003e \u003cp\u003eLatin Square, 451\u003c\/p\u003e \u003cp\u003eLattice Designs, 452\u003c\/p\u003e \u003cp\u003eBalanced Lattice, 452\u003c\/p\u003e \u003cp\u003ePartially Balanced Lattice, 453\u003c\/p\u003e \u003cp\u003eSimple Lattice Repeated, 454\u003c\/p\u003e \u003cp\u003eRectangular Lattice, 454\u003c\/p\u003e \u003cp\u003eα-Designs, 455\u003c\/p\u003e \u003cp\u003eAugmented Designs, 456\u003c\/p\u003e \u003cp\u003ePartially Replicated Designs, 456\u003c\/p\u003e \u003cp\u003eExperimental Design Software, 459\u003c\/p\u003e \u003cp\u003eSummary, 459\u003c\/p\u003e \u003cp\u003eSAS Code, 460\u003c\/p\u003e \u003cp\u003eR Code, 474\u003c\/p\u003e \u003cp\u003eJMP Method, 482\u003c\/p\u003e \u003cp\u003eReferences, 503\u003c\/p\u003e \u003cp\u003eAdditional Reading, 504\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Factorial Experiments 507\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExpected Mean Squares, 509\u003c\/p\u003e \u003cp\u003eEstimation of Variance Components, 512\u003c\/p\u003e \u003cp\u003eSubsampling, 513\u003c\/p\u003e \u003cp\u003eTwo-Factor Factorials, 515\u003c\/p\u003e \u003cp\u003eThree-Factor Factorials, 515\u003c\/p\u003e \u003cp\u003eSplit-Plot, 516\u003c\/p\u003e \u003cp\u003eDo Not Under- or Over-Specify, 518\u003c\/p\u003e \u003cp\u003eModel Specification, 518\u003c\/p\u003e \u003cp\u003eBias Correction, 521\u003c\/p\u003e \u003cp\u003eSplit-Block, 523\u003c\/p\u003e \u003cp\u003eRepeated Measures, 524\u003c\/p\u003e \u003cp\u003eCorrelated Errors Are Not Restricted to Time, 527\u003c\/p\u003e \u003cp\u003eSelection of Covariance Structure, 527\u003c\/p\u003e \u003cp\u003eRepeated Measures, Non-Gaussian, 529\u003c\/p\u003e \u003cp\u003eNo Convergence, 532\u003c\/p\u003e \u003cp\u003eAdjusting for Baseline, 533\u003c\/p\u003e \u003cp\u003eCombined Experiments, 535\u003c\/p\u003e \u003cp\u003eCoefficients for Contrasts and Estimates, 539\u003c\/p\u003e \u003cp\u003eInvestigating Interactions, 542\u003c\/p\u003e \u003cp\u003eFixed, Random, or a Bit of Both?, 544\u003c\/p\u003e \u003cp\u003eSummary, 545\u003c\/p\u003e \u003cp\u003eSAS Code, 545\u003c\/p\u003e \u003cp\u003eR Code, 571\u003c\/p\u003e \u003cp\u003eJMP Method, 579\u003c\/p\u003e \u003cp\u003eReferences, 609\u003c\/p\u003e \u003cp\u003eAdditional Reading, 610\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Response Surface 613\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFirst-Order Designs, 614\u003c\/p\u003e \u003cp\u003eSecond-Order Designs, 615\u003c\/p\u003e \u003cp\u003eCentral Composite Design, 615\u003c\/p\u003e \u003cp\u003eCentral Rotatable Composite Design, 616\u003c\/p\u003e \u003cp\u003eMixture and Double Mixture Designs, 618\u003c\/p\u003e \u003cp\u003ePlotting Response Surfaces, 623\u003c\/p\u003e \u003cp\u003eHoerl and Spline Models, 624\u003c\/p\u003e \u003cp\u003eAvoid Extrapolation, 624\u003c\/p\u003e \u003cp\u003eSummary, 627\u003c\/p\u003e \u003cp\u003eSAS Code, 627\u003c\/p\u003e \u003cp\u003eR Code, 643\u003c\/p\u003e \u003cp\u003eJMP Method, 649\u003c\/p\u003e \u003cp\u003eReferences, 661\u003c\/p\u003e \u003cp\u003eAdditional Reading, 661\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Multiple Regression 663\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLinear Model, 663\u003c\/p\u003e \u003cp\u003eAssumptions, 664\u003c\/p\u003e \u003cp\u003eVariable Selection—Fixed Effect Models, 664\u003c\/p\u003e \u003cp\u003eVariable Selection—Mixed Models, 666\u003c\/p\u003e \u003cp\u003eMultimodel Inference, 668\u003c\/p\u003e \u003cp\u003eCollinear Variables, 670\u003c\/p\u003e \u003cp\u003eVariance Inflation Factor, 670\u003c\/p\u003e \u003cp\u003eCollinearity Diagnostics, 671\u003c\/p\u003e \u003cp\u003eAdjusting Collinear Variables, 672\u003c\/p\u003e \u003cp\u003ePolynomial Models, 672\u003c\/p\u003e \u003cp\u003ePrediction Models Involving Collinear Variables, 673\u003c\/p\u003e \u003cp\u003eCross-Validation, 673\u003c\/p\u003e \u003cp\u003eModel Validation, 674\u003c\/p\u003e \u003cp\u003eLatent Factor Regression, 675\u003c\/p\u003e \u003cp\u003eSummary, 680\u003c\/p\u003e \u003cp\u003eSAS Code, 681\u003c\/p\u003e \u003cp\u003eR Code, 692\u003c\/p\u003e \u003cp\u003eJMP Method, 697\u003c\/p\u003e \u003cp\u003eReferences, 714\u003c\/p\u003e \u003cp\u003eAdditional Reading, 714\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Multivariate Analyses 717\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAnalyses of Dependence, 717\u003c\/p\u003e \u003cp\u003eGenotypic Correlations, 719\u003c\/p\u003e \u003cp\u003ePath Analysis, 720\u003c\/p\u003e \u003cp\u003eAnalyses of Interdependence, 722\u003c\/p\u003e \u003cp\u003eAssumptions, 723\u003c\/p\u003e \u003cp\u003eExample Multivariate Dataset (Grin), 723\u003c\/p\u003e \u003cp\u003eDimension Reduction, 726\u003c\/p\u003e \u003cp\u003eValue of Variables, 728\u003c\/p\u003e \u003cp\u003eNumber of Components\/Factors, 729\u003c\/p\u003e \u003cp\u003eClustering, 733\u003c\/p\u003e \u003cp\u003eDistance Measures, 735\u003c\/p\u003e \u003cp\u003eCluster Methods, 737\u003c\/p\u003e \u003cp\u003eNumber of Clusters, 739\u003c\/p\u003e \u003cp\u003eGroupings Unknown, 740\u003c\/p\u003e \u003cp\u003ePartialling Out, 741\u003c\/p\u003e \u003cp\u003eCluster Validation, 743\u003c\/p\u003e \u003cp\u003eGroupings Known, 745\u003c\/p\u003e \u003cp\u003eCanonical Correlation Analysis, 746\u003c\/p\u003e \u003cp\u003eCanonical Discriminant Analysis, 746\u003c\/p\u003e \u003cp\u003eComparing Distance Matrices, 750\u003c\/p\u003e \u003cp\u003eSummary, 752\u003c\/p\u003e \u003cp\u003eSAS Code, 753\u003c\/p\u003e \u003cp\u003eR Code, 768\u003c\/p\u003e \u003cp\u003eJMP Method, 774\u003c\/p\u003e \u003cp\u003eReferences, 798\u003c\/p\u003e \u003cp\u003eAdditional Reading, 799\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 G×e Analysis 801\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFixed or Random Environments?, 802\u003c\/p\u003e \u003cp\u003eI. Univariate Models, 803\u003c\/p\u003e \u003cp\u003eMean-CV, 803\u003c\/p\u003e \u003cp\u003eRegression Coefficient, 805\u003c\/p\u003e \u003cp\u003eRegression Deviation, 806\u003c\/p\u003e \u003cp\u003eRandom Environment Effect, 807\u003c\/p\u003e \u003cp\u003eYield Stability Index, 810\u003c\/p\u003e \u003cp\u003eSuperiority Measure, 811\u003c\/p\u003e \u003cp\u003eII. Multivariate Models, 812\u003c\/p\u003e \u003cp\u003eBiplots, 816\u003c\/p\u003e \u003cp\u003eConfidence Intervals, 820\u003c\/p\u003e \u003cp\u003eAMMI or GGE?, 821\u003c\/p\u003e \u003cp\u003eG×E Analyses-Summary, 822\u003c\/p\u003e \u003cp\u003eSAS Code, 822\u003c\/p\u003e \u003cp\u003eR Code, 837\u003c\/p\u003e \u003cp\u003eJMP Method, 847\u003c\/p\u003e \u003cp\u003eReferences, 863\u003c\/p\u003e \u003cp\u003eAdditional Reading, 864\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Agriculture \u0026amp; farming [\u003ca title=\"See our other books on Agriculture \u0026amp; farming\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Agriculture%20\u0026amp;%20farming%20%5BTV%5D%22\"\u003eTV\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"ACSESS","offers":[{"title":"Brand New","offer_id":52410740080920,"sku":"9780891183945","price":96.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780891183945.jpg?v=1784250989","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/applied-statistics-in-biology-a-practical-guide-using-sas-r-and-jmp-paperback-softback-9780891183945","provider":"Freshly Printed Books","version":"1.0","type":"link"}