An Introduction to Statistics with Python pp 183-220 | Cite as
Linear Regression Models
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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
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References
- Duda, R. O., Hart, P. E., & Stork, D. G. (2004). Pattern classification (2nd ed.). Hoboken: Wiley-Interscience.zbMATHGoogle Scholar
- Kaplan, D. (2009). Statistical modeling: A fresh approach. St Paul: Macalester College.Google Scholar
- 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
- Wilkinson, G. N., & Rogers, C. E. (1973). Symbolic description of factorial models for analysis of variance. Applied Statistics, 22:, 392–399.CrossRefGoogle Scholar
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