LEADER 04011nam 22006375 450 001 9910299973303321 005 20200703093202.0 010 $a3-319-11734-3 024 7 $a10.1007/978-3-319-11734-8 035 $a(CKB)3710000000343723 035 $a(SSID)ssj0001424521 035 $a(PQKBManifestationID)11849598 035 $a(PQKBTitleCode)TC0001424521 035 $a(PQKBWorkID)11368889 035 $a(PQKB)11310598 035 $a(DE-He213)978-3-319-11734-8 035 $a(MiAaPQ)EBC6314916 035 $a(MiAaPQ)EBC5578337 035 $a(Au-PeEL)EBL5578337 035 $a(OCoLC)1083463223 035 $a(PPN)183517148 035 $a(EXLCZ)993710000000343723 100 $a20150121d2014 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aLinear Models in Matrix Form $eA Hands-On Approach for the Behavioral Sciences /$fby Jonathon D. Brown 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (XIX, 536 p. 77 illus., 28 illus. in color.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-11733-5 320 $aIncludes bibliographical references and index. 327 $aMatrix Properties and Operations -- Simple Linear Regression -- Maximum Likelihood Estimation -- Multiple Regression -- Matrix Decompositions -- Problematic Observations -- Errors and Residuals -- Linearizing Transformations and Nonparametric Smoothers -- Cross-Product Terms and Interactions -- Polynomial Regression -- Categorical Predictors -- Factorial Designs -- Analysis of Covariance -- Moderation -- Mediation. 330 $aThis textbook is an approachable introduction to statistical analysis using matrix algebra. Prior knowledge of matrix algebra is not necessary. Advanced topics are easy to follow through analyses that were performed on an open-source spreadsheet using a few built-in functions. These topics include ordinary linear regression, as well as maximum likelihood estimation, matrix decompositions, nonparametric smoothers and penalized cubic splines. Each data set (1) contains a limited number of observations to encourage readers to do the calculations themselves, and (2) tells a coherent story based on statistical significance and confidence intervals. In this way, students will learn how the numbers were generated and how they can be used to make cogent arguments about everyday matters. This textbook is designed for use in upper level undergraduate courses or first year graduate courses. The first chapter introduces students to linear equations, then covers matrix algebra, focusing on three essential operations: sum of squares, the determinant, and the inverse. These operations are explained in everyday language, and their calculations are demonstrated using concrete examples. The remaining chapters build on these operations, progressing from simple linear regression to mediational models with bootstrapped standard errors. 606 $aStatistics  606 $aPsychometrics 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 606 $aPsychometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/Y43000 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 615 0$aStatistics . 615 0$aPsychometrics. 615 14$aStatistics for Social Sciences, Humanities, Law. 615 24$aPsychometrics. 615 24$aStatistical Theory and Methods. 676 $a150.1519535 700 $aBrown$b Jonathon D$4aut$4http://id.loc.gov/vocabulary/relators/aut$0525080 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299973303321 996 $aLinear models in matrix form$9822773 997 $aUNINA