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📚 Statistical Inference for Everyone
Chapters ▾
1 Introduction to Probability2 Applications of Probability3 Random Sequences and Visualization4 Introduction to Model Comparison5 Applications of Model Comparison6 Introduction to Parameter Estimation7 Priors, Likelihoods, and Posteriors8 Common Statistical Significance Tests9 Applications of Parameter Estimation and Inference10 Multi-parameter Models11 Introduction to MCMC12 Concluding Thoughts13 Appendix A: Computational Analysis14 Appendix B: Notation and Standards15 Appendix C: Common Distributions and Their Properties16 Appendix D: Tables

Chapter 10: Multi-parameter Models

We have already met examples of multiple parameter estimation in the case of unknown uncertainty, where we have to estimate both the “true” value, μ, and the uncertainty, σ. 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.