Logistic regression is typically used when the response Y is a probability or a binary value (0 or 1). For instance, the chance for an email message to be spam, based on a number of features such as suspicious keywords or IP address. In matrix notation, the model can be written as where X is the observations matrix, b is the parameter vector that needs to be estimated, and e is a white noise. T…
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