Linear Regression Models

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


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.


Predictor Variable Akaike Information Criterion Bayesian Information Criterion Linear Regression Model Simple Linear Regression 
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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

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