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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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