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It introduces biplots as a generalization of scatterplots, enabling many variables to be displayed together in a compact, interpretable form. The work develops core variants including regression, generalized linear model, multidimensional scaling, reduced-dimension, principal component analysis, log-ratio, correspondence, multiple correspondence, discriminant, and constrained biplots and triplots, and grounds the methods with multiple applied case studies and supporting appendices.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/biplots-in-practice/289761/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/biplots-in-practice/289761.png","ImageObject",300,407,{"name":92,"@type":93},"Paura","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is a biplot, and how does it extend scatterplots?","Question",{"text":113,"@type":114},"A biplot generalizes scatterplots to handle many variables at once. 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Biplots—the Basic Idea ............................................................................ 15  \n2. Regression Biplots .................................................................................... 25  \n3. Generalized Linear Model Biplots .......................................................... 35  \n4. Multidimensional Scaling Biplots ........................................................... 43  \n5. Reduced-Dimension Biplots .................................................................... 51  \n6. Principal Component Analysis Biplots .................................................... 59  \n7. Log-ratio Biplots ....................................................................................... 69  \n8. Correspondence Analysis Biplots ............................................................ 79  \n9. Multiple Correspondence Analysis Biplots I .......................................... 89  \n10. Multiple Correspondence Analysis Biplots II ......................................... 99  \n11. Discriminant Analysis Biplots .................................................................. 109  \n12. Constrained Biplots and Triplots ............................................................ 119  \n13. Case Study 1—Biomedicine Comparing Cancer Types According to Gene Expression Arrays ........ 129  \n14. Case Study 2—Socio-economics Positioning the “Middle” Category in Survey Research ........................ 139  \n15. Case Study 3—Ecology The Relationship between Fish Morphology and Diet ......................... 153  \nAppendix A: Computation of Biplots ............................................................ 167  \nAppendix B: Bibliography ............................................................................... 199  \nAppendix C: Glossary of Terms ...................................................................... 205  \nAppendix D: Epilogue .................................................................................... 213  \nList of Exhibits ................................................................................................ 221  \nIndex ................................................................................................................ 231  \nAbout the Author ............................................................................................ 237  \nPREFACE  \nIn almost every area of research where numerical data are collected, databasesand spreadsheets are being filled with tables of numbers and these data are being analyzed at various levels of statistical sophistication. Sometimes simple summary methods are used, such as calculating means and standard deviations of quantitative variables, or correlation coefficients between them, or counting category frequencies of discrete variables or frequencies in cross-tabulations. At the other end of the spectrum, advanced statistical modelling is performed, which depends on the researcher’s preconceived ideas or hypotheses about the data, or ","cbCair2UPOwGYUnc","https://ap.wps.com/l/cbCair2UPOwGYUnc","pdf",10312042,241,"English","# Preface\n# Biplots—the Basic Idea\n# Regression Biplots\n# Generalized Linear Model Biplots\n# Multidimensional Scaling Biplots\n# Reduced-Dimension Biplots\n# Principal Component Analysis Biplots\n# Log-ratio Biplots\n# Correspondence Analysis Biplots\n# Multiple Correspondence Analysis Biplots I\n# Multiple Correspondence Analysis Biplots II\n# Discriminant Analysis Biplots\n# Constrained Biplots and Triplots\n# Case Study 1\n# Case Study 2\n# Case Study 3\n# Appendix A: Computation of Biplots\n# Appendix B: Bibliography\n# Appendix C: Glossary of Terms\n# Appendix D: Epilogue\n# List of Exhibits\n# Index\n# About the Author","[{\"question\":\"What is a biplot, and how does it extend scatterplots?\",\"answer\":\"A biplot generalizes scatterplots to handle many variables at once. While scatterplots show two variables on perpendicular axes, biplots use axes corresponding to the variables and choose orientations to represent cases and interpret patterns.\"},{\"question\":\"How does the book organize its biplot methods?\",\"answer\":\"It covers a sequence of biplot types, including regression and generalized linear model biplots, dimensionality-based variants such as principal component analysis biplots, and classification and constrained forms like discriminant and constrained biplots and triplots.\"},{\"question\":\"What kinds of applications are included?\",\"answer\":\"The book includes case studies drawn from biomedicine, socio-economics, and ecology, using biplot techniques to explore relationships such as gene-expression patterns, survey-based category positioning, and links between fish morphology and diet.\"}]","Biplots in Practice | PDF",607]