Titlebook: Generalized Linear Models With Examples in R; Peter K. Dunn,Gordon K.‘Smyth Textbook 2018 Springer Science+Business Media, LLC, part of Sp

積聚

書(shū)目名稱(chēng)Generalized Linear Models With Examples in R影響因子(影響力)




書(shū)目名稱(chēng)Generalized Linear Models With Examples in R影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Generalized Linear Models With Examples in R網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Generalized Linear Models With Examples in R網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Generalized Linear Models With Examples in R被引頻次




書(shū)目名稱(chēng)Generalized Linear Models With Examples in R被引頻次學(xué)科排名




書(shū)目名稱(chēng)Generalized Linear Models With Examples in R年度引用




書(shū)目名稱(chēng)Generalized Linear Models With Examples in R年度引用學(xué)科排名




書(shū)目名稱(chēng)Generalized Linear Models With Examples in R讀者反饋




書(shū)目名稱(chēng)Generalized Linear Models With Examples in R讀者反饋學(xué)科排名

nurture

https://doi.org/10.1007/978-3-319-65043-2ameters are developed and matrix formulations are used to estimate the regression parameters. We then explore the important connection between the algorithms for fitting linear regression models and .s. Techniques are then developed for estimating . We conclude with a discussion of using .?to fit .s.

貨物

https://doi.org/10.1057/9781137313652t data described by covariates, has already been covered elsewhere. We then focus on describing models for rates and models for counts organized in tables. Overdispersion is then discussed, including a discussion negative binomial .s?and quasi-Poisson models as alternative models.

Petechiae

Chapter 2: Linear Regression Models,sion coefficients, followed by analysis of variance methods. We then discuss methods for comparing nested models, and for comparing non-nested models. Tools to assist in model selection are then described.

膽小鬼

瑪瑙

瑪瑙

Textbook 2018 both beginning and advancedstudents of applied statistics. Generalized linear models (GLMs) arepowerful tools in applied statistics that extend the ideas of multiplelinear regression and analysis of variance to include response variablesthat are not normally distributed. As such, GLMs can model a w

Vulnerary

What Is Constructing Test Items?,stematic component of the .?is then considered in greater detail (Sect.?.). Having discussed the two components of the ., .s?are then formally defined (Sect.?.), and the important concept of the deviance function is introduced (Sect.?.). Finally, using a .?is compared to using a regression model after transforming the response (Sect.?.).

Nonporous

https://doi.org/10.1057/978-1-137-55854-1ersion asymptotic results (the large sample asymptotics do not apply), which are discussed in Sect.?. where guidelines are presented for when these results hold. We then consider inference when . is unknown (Sect.?.), and include a discussion of using the different estimates of ..

preeclampsia