Applied Linear Regression ModelsIrwin, 1989 - 667 sidor Applied Linear Regression Models was listed in the newsletter of the Decision Sciences Institute as a classic in its field and a text that should be on every member's shelf. The third edition continues this tradition. It is a successful blend of theory and application. The authors have taken an applied approach, and emphasize understanding concepts; this text demonstrates their approach trough worked-out examples. Sufficient theory is provided so that applications of regression analysis can be carried out with understanding. John Neter is past president of the Decision Science Institute, and Michael Kutner is a top statistician in the health and life sciences area. Applied Linear Regression Models should be sold into the one-term course that focuses on regression models and applications. This is likely to be required for undergraduate and graduate students majoring in allied health, business, economics, and life sciences. |
Innehåll
Some Basic Results in Probability and Statistics | 1 |
Linear Regression with One Independent Variable | 23 |
Inferences in Regression Analysis | 62 |
Upphovsrätt | |
16 andra avsnitt visas inte
Vanliga ord och fraser
95 percent confidence appropriate approximate autocorrelation b₁ B₂ Bonferroni Calculate column conclude confidence interval confidence limits decision rule degrees of freedom denoted error sum error terms error variances estimated regression coefficients estimated regression function extra sums family confidence coefficient Figure fitted regression function gression Hence independent variables indicator variables interval estimate least squares estimates linear regression model logistic regression logistic regression model matrix mean response mean squared multicollinearity multiple regression nonlinear regression normal probability plot normally distributed Note observations Obtain the residuals outlying P-value parameters percent confidence interval point estimate prediction interval probability distribution Problem procedure random variables reduced model Refer regres regression analysis regression coefficients regression model 3.1 residual plot sample simple linear regression ẞ₁ SSR X2 SSTO subset sum of squares Table test statistic tion variance-covariance matrix vector weighted least squares X₁ Y₁ Y₂ σ² ΣΧ