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Samples in periodicals archive:
After that, we get a gradual introduction to linear regression and data distributions in the context of algorithms.
Session 15 describes how to perform a simple two-variable linear regression analysis.
It explains basic statistical concepts, the principles of statistical modeling, the linear regression model, the different types of covariates that may arise and their preliminary screening using graphical techniques, multiple linear regression, logistic regression, Poisson regression analysis, time-to-event regression, building a parsimonious regression model, studies that involve repeated measures, the use of regression trees to identify homogenous subgroups, and model structure, multi-level models, and fractional polynomials.
2) Linear regression may be adequate for many purposes but some variables in optometry and vision sciences may not be connected by such a simple relationship.
Finally, linear regression analysis was performed to determine whether total intensity of physical activity was predicted by revision hip arthroplasty.
Stepwise linear regression analyses were completed to determine if anthropometric or fitness variables were significant predictors of the CVD risk score.
EDGEWORTH EXPANSION FOR LINEAR REGRESSION PROCESSES WITH LONG-MEMORY ERRORS.
The discussion below assumes a basic understanding of linear regression (for more details, see box, "Further Reading.