In the worked example we already considered above, if we run the multiple linear regression, we would generate a 95% confidence interval (CI) around the regression coefficient for age, which is a ...

10.1 Kitchen sink model We can extend the lm (y~x) function to construct a more complicated “formula” for the multi-dimensional model: lm (y ~ x1 + x2 + ... + xn ). This tells R to find the best model ...

Estimating Coefficients and Predicting Values The equation y = mx +b represents the most basic linear regression equation: x is the predictor or independent variable y is the dependent variable or ...

For example, you might want to predict an employee's salary based on age, height, years of experience, and so on. There are approximately a dozen common regression techniques. The most basic technique ...

Linear regression can be used for two closely related, but slightly different purposes. You can use linear regression to predict the value of a single numeric variable (called the dependent variable) ...

R 2 (R-squared) is a statistical measure of the goodness of fit of a linear regression model (from 0.00 to 1.00), also known as the coefficient of determination.

Functional linear regression is a useful extension of simple linear regression and has been investigated by many researchers. However, the functional variable selection problem when multiple ...