2022_BEC_Flipbooks

Taddy-Modern_Business_Analytics

Issue link: https://www.mheducation.com/highered/ideas/i/1445670

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Page 5 of 127

vi BRIEF CONTENTS About the Authors . . . . . . . . . . . . . . . . . . . . v Preface . . . . . . . . . . . . . . . . . . . . . . . . x Guided Tour . . . . . . . . . . . . . . . . . . . . . . xi Acknowledgments . . . . . . . . . . . . . . . . . . xvii 1 Regression . . . . . . . . . . . . . . . . . . . . 1 2 Uncertainty Quantification . . . . . . . . . . . . . 55 3 Regularization and Selection . . . . . . . . . . . 100 4 Classification . . . . . . . . . . . . . . . . . . 151 5 Causal Inference with Experiments . . . . . . . . 175 6 Causal Inference with Controls . . . . . . . . . . 215 7 Trees and Forests . . . . . . . . . . . . . . . . 259 8 Factor Models . . . . . . . . . . . . . . . . . 282 9 Text as Data . . . . . . . . . . . . . . . . . . 316 10 Deep Learning . . . . . . . . . . . . . . . . . 345 Appendix: R Primer . . . . . . . . . . . . . . . 383 Bibliography 419 Glossary 424 Acronyms 433 Index 436

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