Advertisement

Linear Regression Models

  • Thomas Haslwanter
Chapter
Part of the Statistics and Computing book series (SCO)

Abstract

After an introduction to Pearson’s, Spearman’s, and Kendall’s correlation coefficients, this chapter describes how to implement and solve linear regression models in Python. The resulting model parameters are discussed, as well as the assumptions of the models and interpretations of the model results. Since bootstrapping can be helpful in the evaluation of some models, the final section in this chapter shows a Python implementation of a bootstrapping example.

Keywords

Predictor Variable Akaike Information Criterion Bayesian Information Criterion Linear Regression Model Simple Linear Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Duda, R. O., Hart, P. E., & Stork, D. G. (2004). Pattern classification (2nd ed.). Hoboken: Wiley-Interscience.zbMATHGoogle Scholar
  2. Kaplan, D. (2009). Statistical modeling: A fresh approach. St Paul: Macalester College.Google Scholar
  3. Nuzzo, R. (2014). Scientific method: Statistical errors. Nature, 506(7487):150–152. doi:10.1038/506150a. http://www.nature.com/news/scientific-method-statistical-errors-1.14700 CrossRefGoogle Scholar
  4. Wilkinson, G. N., & Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics, 22:, 392–399.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Thomas Haslwanter
    • 1
  1. 1.School of Applied Health and Social SciencesUniversity of Applied Sciences Upper AustriaLinzAustria

Personalised recommendations