Data Analysis for Business, Economics, and Policy

It could just be a reflection of the existing paradigm in which the subject of data analysis has been and is still being taught today, but I've been repeatedly surprised by the clarity and order brought to each subject by this unique book - a clarity and order I've never experienced - and always sought - from every other data analysis/statistics text/class I've utilized.Though I studied some math and statistics in grad school, because I really got into programmatic data analysis in the work force most of my knowledge and skills are self-taught, so that's my perspective. But I've complained for years to anyone who would listen 🙂 that I simply didn't understand why these subjects are taught as they are: as a collection of methods/calculations which address particular analytical challenges. My experience has been that this teaching paradigm both limits my understanding and recall of the use of these methods because there's no overarching structure within which I can understand their relevance. Here's a book on regression, another on exploratory data analysis, another on prediction, another on hypothesis tests - under what conditions, to address which challenges and in what order are these techniques employed? I've felt largely left to cobble together my own methodology. This book however unifies these methods into an understandable process, allowing me to appreciate their relevance and use at different stages of a data analysis project. Because I understand their relevance, the methods make more sense to me and are more deeply ingrained into my memory. And because I'm viewing a start-to-finish process which has some order and clear objective, I can replicate that framework for my own data analysis projects. Of course I understand that no analytical project - or any advanced methodology - can be a strictly A-to-Z affair, and as one's understanding matures they are able to recognize when digressions from a framework are warranted. But, for me at least, and particularly in this subject, a unified understanding of the objective and general process is critical for me to be able to make informed choices about which methodology or technique to use under what conditions and with which objectives.Also, and I also can't emphasize this enough, there are almost no errors (OK, a couple of simple typos)!!!! This in an era when books (particularly these books) are published with seemingly little to no editing! I don't know whether authors who allow their work to be published with errors understand how that might corrode the confidence of their readers, but that's certainly the case for me. On a couple of occasions - perhaps trained by my prior textbooks - I've thought myself close to catching the authors in a mistake or omission: "Ah - they forgot to mention that! Aha!", or "Here we are, they left out a description of that just like all the other books!", only to find the next page held the relevant answers/explanation in clear text. Many subjects which I've found are seemingly taken for granted in other textbooks - weighting, rationale and nature of natural log transformations, use of regression residuals, even a simple understanding of the difference between parametric and nonparametric methods - all of these subjects are given thoughtful and clear treatment in this text.Finally, the R code! I read through the text, then work through the code for each chapter's case study in R Studio, and the code on its own is worth (perhaps more than) the price of the book. Besides further enhancing my understanding of the text, it demonstrates good code-writing technique and R usage, and also gives me a plethora of templates I can refer to in my own data analytics work. I'm thinking about how much effort it must have taken people on the authors' team to develop the R visualization templates and all the R code - not to mention the code in the other languages (Python and Stata code is also included). It seems it must have been a monumental undertaking.In summary, I have tens, perhaps close to a hundred of the yellow Springer books and all their relatives from the different publishing houses adorning my bookshelves and this text is the first to really make me start to feel (I'm only a third through the book) I can do this stuff on my own because I understand what I'm supposed to do - not a detached method or a technique but an entire process and framework. For anyone who is learning this subject on their own, is in school and would like a more comprehensive and systematic explanation of this craft, or out of school working and would appreciate a one-stop overview of the data analysis process and rationale, this text is a clear, thorough, and I believe invaluable learning tool.

The book is the legacy of late Kézdi who unfortunately passed away a couple of months after the book was published - at the young age of 50. The topics the authors chose as case studies show their academic and personal interests: Kézdi researched discrimination on the workplace, this is reflected in cases on gender and age difference in earnings. Later he turned to health economics and this is shown in case studies on life expectancy, smoking as a health risk, and effects of immunization of children. Békés researched international trade and competitiveness and real estate prices, this is reflected in cases on hotel and home prices.The book is very well written so it is easy to read. I checked the cases on finance with great detail as this is a topic I myself did some research on and found them very accurate. I was especially pleased that they noted that when you convert a variable to logs you should not forget to correct for the half of the variance otherwise the estimation of the mean will be biased – this was missing from my econometrics books when I did my studies. I also liked the “Under the hood” parts as I am the type who wants to dig deeper into theory to understand why formulas work.All in all, this is a great book I can recommend both for current students and practitioners who want to brush up their knowledge with the latest methods in econometrics or rather … data analysis.

This book is amazing and it's most definitely going to be so for years to come.A bit about my background I'm not studying data analysis formally such as in a college or university but rather with my own curriculum we could say. So anyone that maybe feels intimidated by the idea that this book might be just for college students shouldn't be at all 😀. Mostly because the book is extremely accessible for a beginner with the way it builds on previous chapters which is something the authors seemed to have mastered.Before purchasing the book I read through the preview (something I recommend everyone does for every single book they are looking to purchase) and I fell in love with the teaching style and also having skimmed the table of contents after, I knew I had to purchase the book since what would be covered was highly relevant to me.Now 9 months later it helped me get my first data analysis job. I also haven't even read the book completely through I got to ch.16 (more or less) and stopped to study some other things. Then when I came back I decided to start from the beginning and absorbed even more and now I'm in the process of finishing it . Then I plan on reading it again lol since there's much information contained in the book including references to external works.So for me I really I have no complaints, I was just kind of sad you had to be a university professor to get access to the answers for the questions but the fact the code for the book is freely available on GitHub more than makes up for it.

I teach two MBA courses, and this book has become a focal point in both. The case studies are not only effective but also engaging, and the inclusion of codes in STATA, R, and Python has generated positive feedback from students. It's a standout feature that adds significant value to their learning experience.

Data Analysis for Business, Economics and Policy is an excellent and much needed companion for practitioners in various policy fields. It is not simply a textbook on a broad range of methods, but a hands-on guide for data analysis in practice. It guides the reader firmly through the process of actual empirical analysis, demonstrating how to answer policy and business questions through a broad set of case studies. While covering a large variety of topics, the book never looses focus of the most acute problems practitioners are likely to face. It should be definitely included in the welcome package of every junior data analyst.