Applied Linear Regression ModelsMcGraw-Hill/Irwin, 2004 - 701 sidor Kutner, 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. |
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Applied Linear Regression Models Michael H. Kutner,Chris J. Nachtsheim,John Neter Ingen förhandsgranskning - 2003 |
Applied Linear Regression Models Michael H. Kutner,Chris Nachtsheim,John Neter Ingen förhandsgranskning - 2018 |
Applied Linear Regression Models, International Revised Edition with Student ... Michael H. Kutner,Nachtsheim,John Neter Ingen förhandsgranskning - 2004 |
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95 percent confidence appropriate b₁ B₂ Bo and B₁ Bonferroni box plot column conclusion confidence band confidence interval data set decision rule degrees of freedom denoted error regression model error sum error terms error variance estimated regression coefficients estimated regression function expected values 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 MINITAB multicollinearity nonlinear regression normal distribution normal probability plot observations P-value parameters percent confidence interval prediction interval probability distribution Problem procedure Refer regression analysis regression model 2.1 regression relation residual plot response function response variable scatter plot simple linear regression ẞ₁ SSTO sum of squares test statistic Toluca Company example transformation variance-covariance matrix vector weighted least squares X₁ Y₁ Y₂ Yh(new