Applied Linear Regression ModelsKutner, Neter, Nachtsheim, Wasserman, Applied Linear Regression Models, 4/e (ALRM4e) is the long established leading authoritative text and reference on regression (previously Neter was lead author.) For students in most any discipline where statistical analysis or interpretation is used, ALRM has served as the industry standard. The text includes brief introductory and review material, and then proceeds through regression and modeling. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in any discipline. ALRM 4e provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor by using larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done. |
Contents
Alternative Versions of Regression Model | 12 |
Chapter 4 | 27 |
2 | 35 |
Copyright | |
37 other sections not shown
Other editions - View all
Applied Linear Regression Models Michael H. Kutner,Chris J. Nachtsheim,John Neter No preview available - 2003 |
Applied Linear Regression Models Michael H. Kutner,Chris Nachtsheim,John Neter No preview available - 2018 |
Common terms and phrases
95 percent confidence appropriate B₁ bivariate normal Bo and B₁ Bonferroni column conclusion confidence band confidence interval correlation data set decision rule degrees of freedom denoted error sum error terms error variance estimated regression coefficients estimated regression function expected value explanatory variables extra sum family confidence coefficient Figure fitted regression function fitted values Ŷ Hence interval estimate lack of fit least squares estimates likelihood function linear regression function linear regression model mean response mean square measure MINITAB multicollinearity nonlinear regression normal distribution normal probability plot observations P-value percent confidence interval prediction interval probability distribution Problem procedure Refer regression analysis regression coefficients regression model 2.1 regression relation residual plot response function response variable sampling distribution simple linear regression ẞ₁ SSTO standard deviation subset sum of squares test statistic Toluca Company example transformation variance-covariance matrix vector X₁ Y₁ Y₂ Yh(new