10.1 Overview

We have already met examples of multiple parameter estimation in the case of unknown uncertainty, where we have to estimate both the “true” value, $\mu$, and the uncertainty, $\sigma$. In this chapter, we introduce the model of linear regression, which has multiple “true” value parameters and their uncertainty. In the simple cases, we can calculate the estimates by hand and apply the same testing procedures as described in Chapter 8 (Common Statistical Significance Tests on page 159). In the more complex cases we will have to rely on the computer to give us the estimates, but we can still interpret them in the same way as before.

Adapted from Statistical Inference for Everyone, by Brian Blais (Bryant University), licensed under CC BY-SA 4.0 (dual-licensed under the GNU FDL 1.2 or later; this adaptation uses the CC BY-SA grant). Changes were made; this adaptation is distributed under the same license. License: CC-BY-SA-4.0.