標(biāo)題: Titlebook: Applied Regression Analysis; A Research Tool John O. Rawlings,Sastry G. Pantula,David A. Dickey Textbook 1998Latest edition Springer Scienc [打印本頁] 作者: Animosity 時(shí)間: 2025-3-21 18:39
書目名稱Applied Regression Analysis影響因子(影響力)
作者: metropolitan 時(shí)間: 2025-3-22 00:01 作者: hyperuricemia 時(shí)間: 2025-3-22 04:26 作者: 指耕作 時(shí)間: 2025-3-22 06:10 作者: Defraud 時(shí)間: 2025-3-22 12:37 作者: flourish 時(shí)間: 2025-3-22 15:11 作者: stress-test 時(shí)間: 2025-3-22 19:56 作者: AXIS 時(shí)間: 2025-3-22 21:48 作者: grieve 時(shí)間: 2025-3-23 02:35 作者: pineal-gland 時(shí)間: 2025-3-23 07:30 作者: 斷言 時(shí)間: 2025-3-23 10:25
Infrared Sensors and Ultrasonic Sensors, and inadequacies in the model, statistics to flag observations that are dominating the regression, and methods of detecting situations in which strong relationships among the independent variables are affecting the results.作者: 提名 時(shí)間: 2025-3-23 14:50
Romy Escher,Melanie Walter-Roggately represented by straight-line relationships. This chapter extends those ideas to the large class of usually more realistic models that are nonlinear in the parameters. First, several examples of nonlinear models are given. Then regression methods for fitting these models are presented.作者: 有機(jī)體 時(shí)間: 2025-3-23 19:13 作者: Parameter 時(shí)間: 2025-3-23 23:02
Geometry of Least Squares,ing relationships among the vectors. The intent of this chapter is to give insight into the basic principles of least squares. This chapter is not essential for an understanding of the remaining topics.作者: incision 時(shí)間: 2025-3-24 04:45
Model Development: Variable Selection,t the variables are in the appropriate form. The effect of variable selection on least squares, the use of automated methods of selecting variables, and criteria for choice of subset model are discussed.作者: carbohydrate 時(shí)間: 2025-3-24 09:02
Polynomial Regression,o characterize curvilinear relationships. Such models are linear in the parameters and linear least squares is appropriate for estimation of the parameters. Models that are nonlinear in the parameters are introduced in Chapter 15.作者: 親愛 時(shí)間: 2025-3-24 13:12
Class Variables in Regression,ssion to include the classical analysis of variance models and models containing both continuous and class variables, such as analysis of covariance models and models to test homogeneity of regressions over groups.作者: 縱火 時(shí)間: 2025-3-24 17:22
Problem Areas in Least Squares,the data to contain either errors or observations that are somewhat unusual compared to the rest of the data. This chapter presents a synopsis of the problem areas that commonly arise in least squares analysis.作者: 不要嚴(yán)酷 時(shí)間: 2025-3-24 22:05
Regression Diagnostics, and inadequacies in the model, statistics to flag observations that are dominating the regression, and methods of detecting situations in which strong relationships among the independent variables are affecting the results.作者: entail 時(shí)間: 2025-3-25 02:02
Models Nonlinear in the Parameters,ately represented by straight-line relationships. This chapter extends those ideas to the large class of usually more realistic models that are nonlinear in the parameters. First, several examples of nonlinear models are given. Then regression methods for fitting these models are presented.作者: 易于 時(shí)間: 2025-3-25 05:29 作者: Heterodoxy 時(shí)間: 2025-3-25 09:40 作者: 作嘔 時(shí)間: 2025-3-25 12:12
https://doi.org/10.1007/b98890Analysis of variance; Excel; Regression analysis; SAS; STATISTICA; Time series; linear regression作者: Prognosis 時(shí)間: 2025-3-25 16:58 作者: 虛弱的神經(jīng) 時(shí)間: 2025-3-25 22:44 作者: Glucocorticoids 時(shí)間: 2025-3-26 01:08 作者: fiction 時(shí)間: 2025-3-26 04:46 作者: 外觀 時(shí)間: 2025-3-26 11:32
Infrared Sensors and Ultrasonic Sensors,ates the application of least squares regression to a problem involving five independent variables. The full model is fit and then the model is simplified to a two-variable model that conveys most of the information on ..作者: 歡騰 時(shí)間: 2025-3-26 13:35 作者: Sputum 時(shí)間: 2025-3-26 18:51
Infrared Sensors and Ultrasonic Sensors, form these variables should take..This chapter discusses methods of deciding which variables should be included in the model. It is still assumed that the variables are in the appropriate form. The effect of variable selection on least squares, the use of automated methods of selecting variables, a作者: nascent 時(shí)間: 2025-3-26 23:56 作者: Mri485 時(shí)間: 2025-3-27 03:43 作者: Forsake 時(shí)間: 2025-3-27 07:43
Positioning and Navigation Technology,data are correct and representative of the intended populations..In reality, the least squares assumptions hold only approximately and one can expect the data to contain either errors or observations that are somewhat unusual compared to the rest of the data. This chapter presents a synopsis of the 作者: 技術(shù) 時(shí)間: 2025-3-27 10:12 作者: Libido 時(shí)間: 2025-3-27 15:50 作者: 起草 時(shí)間: 2025-3-27 20:05 作者: 點(diǎn)燃 時(shí)間: 2025-3-27 23:38 作者: 貧困 時(shí)間: 2025-3-28 03:04
Preparation of Reports and Assessments,eiball model to characterize the seed yield response to ozone are combined, the residuals are inspected, and the response variable is transformed as indicated by the analysis. The response models are fit to the transformed data.作者: Meditative 時(shí)間: 2025-3-28 10:17 作者: 外觀 時(shí)間: 2025-3-28 12:16
https://doi.org/10.1007/978-3-319-06278-5e is more than one random term. This chapter introduces mixed models that contain both fixed effects and several random effects. Analysis of variance models for randomized block designs and split-plot experiments and models for repeated measurement data are special cases of mixed effects models. Hyp作者: allude 時(shí)間: 2025-3-28 15:03
https://doi.org/10.1007/978-3-319-06278-5lustrates the analysis of unbalanced data where the model contains more than one random effect. First, the classical analysis of variance approach with a less-than-full-rank effects model is presented. This is followed with an analysis using a program designed to handle mixed models.作者: 俗艷 時(shí)間: 2025-3-28 21:43 作者: 地名詞典 時(shí)間: 2025-3-29 02:27
Review of Simple Regression,This chapter reviews the elementary regression results for a linear model in one variable. The primary purpose is to establish a common notation and to point out the need for matrix notation. A light reading should suffice for most students.作者: 長(zhǎng)處 時(shí)間: 2025-3-29 03:55 作者: 針葉 時(shí)間: 2025-3-29 07:39 作者: Insatiable 時(shí)間: 2025-3-29 14:47 作者: 種類 時(shí)間: 2025-3-29 17:42 作者: 工作 時(shí)間: 2025-3-29 22:02
Infrared Sensors and Ultrasonic Sensors,ates the application of least squares regression to a problem involving five independent variables. The full model is fit and then the model is simplified to a two-variable model that conveys most of the information on ..作者: 成份 時(shí)間: 2025-3-30 01:21
Overview of Autonomous Unmanned Systems, the use of transformations of variables to simplify relationships, to stabilize variances, and to improve normality. Weighted least squares and generalized least squares are presented as methods of handling the problems of heterogeneous variances and lack of independence.作者: 優(yōu)雅 時(shí)間: 2025-3-30 06:18
Romy Escher,Melanie Walter-Roggquares when collinearity is a problem. The correlational structure is then analyzed using principal component analysis and Gabriel’s biplots. Finally, principal component regression is used and its limitations for the objective of this study are discussed.作者: 使服水土 時(shí)間: 2025-3-30 10:51 作者: Feedback 時(shí)間: 2025-3-30 12:41 作者: Delirium 時(shí)間: 2025-3-30 17:02
https://doi.org/10.1007/978-3-319-06278-5lustrates the analysis of unbalanced data where the model contains more than one random effect. First, the classical analysis of variance approach with a less-than-full-rank effects model is presented. This is followed with an analysis using a program designed to handle mixed models.作者: 態(tài)學(xué) 時(shí)間: 2025-3-30 23:00 作者: 夸張 時(shí)間: 2025-3-31 04:12
Multiple Regression in Matrix Notation,icated models..This chapter presents the model, and develops the normal equations and solution to the normal equations for a general linear model involving any number of independent variables. The matrix formulation for the variances of linear functions is used to derive the measures of precision of the estimates.作者: GULP 時(shí)間: 2025-3-31 07:38
Analysis of Variance and Quadratic Forms, distributed random variables if . was normally distributed..This chapter develops the distributional results for all quadratic functions of .. The distribution of quadratic forms is used to develop tests of hypotheses, confidence interval estimates, and joint confidence regions for β.作者: 極深 時(shí)間: 2025-3-31 11:05
Case Study: Five Independent Variables,ates the application of least squares regression to a problem involving five independent variables. The full model is fit and then the model is simplified to a two-variable model that conveys most of the information on ..作者: Buttress 時(shí)間: 2025-3-31 16:59 作者: 全等 時(shí)間: 2025-3-31 19:33
Case Study: Collinearity Problems,quares when collinearity is a problem. The correlational structure is then analyzed using principal component analysis and Gabriel’s biplots. Finally, principal component regression is used and its limitations for the objective of this study are discussed.作者: HPA533 時(shí)間: 2025-3-31 22:08
Case Study: Response Curve Modeling,eiball model to characterize the seed yield response to ozone are combined, the residuals are inspected, and the response variable is transformed as indicated by the analysis. The response models are fit to the transformed data.作者: PET-scan 時(shí)間: 2025-4-1 04:43 作者: 陳舊 時(shí)間: 2025-4-1 09:40
