Data analysis for business and economics

Abstract The reason why we have a chapter dedicated to Data Analysis into a book of Mathematical Economics (with Excel) is mostly because, practically speaking, any business (or pure) economist always needs to perform statistical or econometric analysis, in order to draw some conclusions about the data and the models that are being studied. Excel nowadays, under this respect, undoubtably offers a wide range of powerful tools already built-in, that we should all know, in order to perform the required data analysis for business and economics. The fields of data analysis, data science, and forecasting are so vast that going into more details than this chapter will do is out of the scope of the book. On the other hand, these fields are so important that we could not skip some key topics that could likely fit a business analyst, an operations research analyst, as well as an economist analyst. The idea of this chapter is therefore to lightly cover topics like the descriptive business data analysis, showing how to prepare a dynamic dashboard using the Pivot Tables, together with the Pivot Charts, being this particularly useful within the business reporting of a company. Then, this chapter will cover the standard descriptive statistics and the basic linear models in econometrics, with some inferential statistics on the regression models. The theory of economics always deals with theoretical functions (e.g., the Cobb–Douglas, Demand, Macroeconomic Consumption, etc.) and we should be all aware that all these functions somehow originated from the actual observation of reality, leading then to general economic law formulations that need to be tested. This chapter may therefore represent an important link with the theoretical economic analysis because we can see how the theoretical economic functions can be estimated and tested, using the economic raw data and the inferential statistics.