10 types of regressions. Which one to use?

Linear regression: Oldest type of regression, designed 250 years ago; computations (on small data) could easily be carried out by a human being, by design. Can be used for interpolation, but not suitable for predictive analytics; has many drawbacks when applied to modern data, e.g. A better solution is piecewise-linear regression, in particular for time series. Logistic regression: Used extensively in clinical trials, scoring and fraud detection, when the response is binary (chance of succeeding or failing, e.g. for a new tested drug or a credit card transaction). Suffers same drawbacks as linear regression (not robust, model-dependent), and computing regression coeffients involves using complex iterative, numerically unstable algorithm.