Foundations of statistics for data scientists : with R and Python
著者
書誌事項
Foundations of statistics for data scientists : with R and Python
（Texts in statistical science）
CRC Press, 2022
- : hbk.
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注記
Includes bibliographical references (p.447-448) and index
内容説明・目次
内容説明
Shows the elements of statistical science that are highly relevant for students who plan to become data scientists less emphasis on probability theory and methods of probability such as combinatorics, derivations of probability distributions of transformations of random variables (except for explanations of t, chi-squared, and F constructions)
Formal statements and proofs of theorems, and decision theory
Introduces some modern topics that do not normally appear in "math stat" texts but are especially relevant for data scientists, such as generalized linear models for non-normal responses (e.g., logistic regression)
Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python)
目次
1. Introduction to Statistical Science 2. Probability Distributions 3. Sampling Distributions 4. Statistical Inference: Estimation Skip Product Menu 5. Statistical Inference: Significance Testing 6. Linear Models and Least Squares 7. Generalized Linear Models 8. Classification and Clustering 9. Statistical Science: A Historical Overview Appendices
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