Logo

Introduction to Python for Econometrics, Statistics and Numerical Analysis

Small book cover: Introduction to Python for Econometrics, Statistics and Numerical Analysis

Introduction to Python for Econometrics, Statistics and Numerical Analysis
by


Number of pages: 281

Description:
Python is a widely used general purpose programming language, which happens to be well suited to Econometrics and other more general purpose data analysis tasks. These notes provide an introduction to Python for a beginning programmer. They may also be useful for an experienced Python programmer interested in using NumPy, SciPy, and matplotlib for numerical and statistical analaysis.

Home page url

Download or read it online for free here:
Download link
(1.8MB, PDF)

Similar books

Book cover: The Elements of Input-Output AnalysisThe Elements of Input-Output Analysis
by - Random House Inc
This volume is designed to give the reader an understanding of how the input-output system works; it is not a guide to the construction of an interindustry transactions table. Most of this book deals with a static, open input-output model.
(10076 views)
Book cover: Financial EconometricsFinancial Econometrics
by - BookBoon
This is a step-by-step guide to financial econometrics using EViews 6.0 statistical package. It contains brief overviews of econometric concepts, models and data analysis techniques followed by examples of how they can be implemented in EViews.
(16051 views)
Book cover: Optimal Regulation: The Economic Theory of Natural MonopolyOptimal Regulation: The Economic Theory of Natural Monopoly
by - The MIT Press
Optimal Regulation addresses the central issue of regulatory economics -- how to regulate firms in a way that induces them to produce and price 'optimally'. It synthesis an extensive theoretical literature on what constitutes optimality.
(11532 views)
Book cover: EconometricsEconometrics
by - BookBoon
This book covers the most basic concepts in econometrics. Subjects as basic probability and statistics, statistical inference with the simple and multiple regression model, dummy variables and auto correlation are explained.
(17028 views)