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.
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Notes
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This section has been taken from Wikipedia https://en.wikipedia.org/wiki/Linear_regression, last accessed 21 Oct 2015.
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The following is based on the blog of Connor Johnson (http://connor-johnson.com/2014/02/18/linear-regression-with-python/), with permission from the author.
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This section and the next chapter are based on Wikipedia https://en.wikipedia.org/wiki/Linear_regression, last accessed 21 Oct 2015.
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References
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Kaplan, D. (2009). Statistical modeling: A fresh approach. St Paul: Macalester College.
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
Wilkinson, G. N., & Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics, 22:, 392–399.
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Haslwanter, T. (2016). Linear Regression Models. In: An Introduction to Statistics with Python. Statistics and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-28316-6_11
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DOI: https://doi.org/10.1007/978-3-319-28316-6_11
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