Final answer:
Ridge regression is a technique used to address multicollinearity in linear regression models. The objective function can be rewritten with the help of the design matrix, observed response variables, regression coefficients, and regularization parameter.
Step-by-step explanation:
Ridge regression is a technique used in statistics and machine learning to address the issue of multicollinearity in linear regression models. The objective function for ridge regression is typically defined as the sum of the squared residuals plus a penalty term. It can be rewritten in the form shown in the question, where m is the number of observations, y is the vector of observed response variables, X is the design matrix of predictors, b is the vector of regression coefficients, and lambda is the regularization parameter.