This course will introduce linear, generalized linear and time-to-event regression models that are commonly used in epidemiologic research, community needs assessment and public health program/policy ...

When the independent variables in a linear regression are highly correlated, model fitting issues and ... users of all experience levels to perform advanced statistical analysis.

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience ...

Linear regression takes the logic of the correlation coefficient and extends it to a predictive model of that relationship. Some key advantages of linear regression are that it can be used to predict ...

Linear and logistic regression models are essential tools for quantifying the relationship ... These models also form the building blocks for more advanced statistical techniques taught in other short ...

The model is created using a stepwise linear regression procedure to select significant predictors. The predictive model includes a prediction confidence interval to quantify the uncertainty. The ...

Linear regression models the relationship between a dependent and independent variable(s). A linear regression essentially estimates a line of best fit among all variables in the model.