Multiple linear regression.
This chapter describes multiple linear regression, a statistical approach used to describe the simultaneous associations of several variables with one continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit. The special cases of regression with interactions among the variables, polynomial regression, regressions with categorical (grouping) variables, and separate slopes models are also covered. Examples in microbiology are used throughout.
Eberly
Lynn E
LE
Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.
D000970:Antineoplastic Agents / Q000627:therapeutic use; D015972:Gene Expression Regulation, Neoplastic / Q000276:immunology; D006801:Humans; D007694:Killer Cells, Natural / Q000276:immunology; D016014:Linear Models; D008175:Lung Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000276:immunology; D015999:Multivariate Analysis
D000970:Antineoplastic Agents