Linear Regression, GLMs and GAMs with R Assignment Help

Linear Regression, GLMs, and GAMs with R shows how to use R to specify, model, and interpret the results of generalised linear (GLMs) and generalised additive (GAMs) models that extend the basic assumptions and constraints of linear regression. The course demonstrates GLM and GAM estimation by working through a series of practical examples from Simon N. Wood’s book Generalized Additive Models: An Introduction with R (Chapman & Hall/CRC Texts in Statistical Science, 2006). A univariate response is modelled as a linear function of predictor variables in linear statistical models, and a zero mean random error term is included. A critical (and limiting) feature is the assumption of linearity. The assumption of linearity is relaxed in generalised linear models (GLMs). They allow the response variable’s expected value to be a smoothed (e.g. non-linear) monotonic function of the linear predictors.

KEY FEATURE

Understand the assumptions of linear regression using ordinary least squares (OLS). R is used to specify, estimate, and interpret linear (regression) models.

Understand how the OLS regression assumptions are changed in order to specify, estimate, and interpret generalised linear models. R can be used to specify, estimate, and interpret GLMs.

Linear regression is a statistical method for modelling the relationship between a scalar response and one or more explanatory variables.

Relationships are modelled using linear predictor functions whose unknown model parameters are estimated from data in linear regression.

Linear Regression, GLMs and GAMs with R Assignment Help

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    GLMs also allow for multiple distributions, which loosens the assumption that the response variable is normally distributed. GAMs are generalised additive models that are extensions of GLMs. GAMs can be used to estimate regression coefficients in the form of non-parametric smoothers. Lowess (locally weighted scatterplot smoothing) is a nonparametric smoother that fits a smooth curve to data using localised subsets of the data. This course provides an overview of GLM and GAM modelling in R. GLMs, particularly GAMs, have evolved into standard statistical methodologies with a high degree of flexibility. The course covers cutting-edge approaches to modelling, estimating, and interpreting GAMs. The course focuses on modelling and interpreting GLMs, particularly GAMs, using R. The use of the free R software demonstrates the utility of linear, generalised linear, and generalised additive models.

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