Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using Python
Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using Python
Julian J. Faraway is a professor of statistics in the Department of Mathematical Sciences at the University of Bath. His research focuses on the analysis of functional and shape data with particular application to the modeling of human motion. He earned a PhD in statistics from the University of California, Berkeley.
Inhaltsangabe
1.Introduction 2.Estimation 3.Inference 4.Prediction 5.Explanation 6.Diagnostics 7.Problems with the Predictors 8.Problems with the Errors 9.Transformation10.Model Selection 11.Shrinkage Methods 12.Insurance Redlining -A Complete Example 13.Missing Data 14.Categorical Predictors 15.One Factor Models 16.Models with Several Factors 17.Experiments with Blocks 18.About Python
1.Introduction 2.Estimation 3.Inference 4.Prediction 5.Explanation 6.Diagnostics 7.Problems with the Predictors 8.Problems with the Errors 9.Transformation10.Model Selection 11.Shrinkage Methods 12.Insurance Redlining -A Complete Example 13.Missing Data 14.Categorical Predictors 15.One Factor Models 16.Models with Several Factors 17.Experiments with Blocks 18.About Python
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