Stocks to riches : insights on investor behaviour

By:

Parikh, Parag

Material type: Text

TextPublication details: Chennai McGraw Hill Education India Pvt Ltd 2006Description: xvi, 112pISBN:

9780070597716 (pbk.)

Subject(s):

DDC classification:

332.6019 PAR

Summary: Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Plaksha University Library	Economics	332.6019 PAR (Browse shelf(Opens below))	Checked out	19/09/2023	004140

https://www.goodreads.com/book/show/6857031-stocks-to-riches?ref=nav_sb_ss_1_13

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

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