Applied Statistics and Econometrics

This accessible textbook introduces the foundations of applied econometrics and statistics for undergraduate students. It covers key topics in econometrics by using step-by-step examples in Gretl and R, providing a guide to using statistical software and the tools for econometric analysis in one self-contained resource.

Taking a concise, non-technical approach, the book covers topics including simple regression and hypothesis testing, multiple regression with control variables and isolating effects, instrumental variables, dummy variables, non-linear effects, probability models, heteroskedasticity, time series analysis, and other applied statistical tools such as t-tests and chi squared tests. The book uses small data sets to easily facilitate students' transition from manual statistical calculations to using and understanding statistical software, including step-by-step examples of regression analysis, as well as additional chapters to aid with econometric notation and mathematical prerequisites, and accompanying online exercises and data sets. This book will be a valuable resource for upper undergraduate students taking courses in introductory econometrics and statistics, as well as students in business administration and other fields of study in social sciences utilising quantitative methods. Graduate students may also benefit from the book.

Introduces students to the basics of applied statistics and econometrics in an accessible, non-technical manner Demonstrates how to use statistical software to interpret data, focusing on Gretl and R specifically Simple, step-by-step computational examples of regression built into the text

Autorentext

Bjørnar Karlsen Kivedal is a Professor at the Faculty of Computer Science, Engineering and Economics at Østfold University College, Norway. He has over 15 years' experience of teaching statistics, mathematics and econometrics. He is also a researcher at Housing Lab at Oslo Metropolitan University.

Inhalt

1. Introduction 2.- Start using Gretl and R 3.- Basic Material 4.- Hypothesis testing 5.- Simple linear regression 6.- Multiple regression 7.- Regression using dummy variables 8.- Non linear models 9.- Time series analysis 10.- Other statistical tools.