Current Slide

• Linear regression: Y = w X + b
• Two regression coefficients, w and b, specify the line and are to be estimated by using the data at hand
• Using the least squares criterion to the known values of Y1, Y2, …, X1, X2, ….
• Multiple regression: Y = b + b1 X1 + b2 X2
• Many nonlinear functions can be transformed into the above
• Log-linear models:
• Approximate discrete multidimensional probability distributions
• Estimate the probability of each point (tuple) in a multi-dimensional space for a set of discretized attributes, based on a smaller subset of dimensional combinations
• Useful for dimensionality reduction and data smoothing

Regress Analysis and Log-Linear Models