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  • 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

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