How can you use statistical software to improve economic research?

Software like R and Python libraries as Pandas and Scikit-learn are transformative for economic research. They simplify data manipulation, enabling quick analysis of large datasets. Classical techniques like regression analysis become more efficient and accurate. Machine learning algorithms offer new ways to predict trends and identify hidden patterns. Robust data cleaning features improve the quality of the data, which is crucial for research validity. Visualization tools facilitate better data interpretation and effective communication of findings. Version control systems like Git make collaborative research easier and more transparent. Overall, these tools significantly elevate the quality, efficiency, and impact of economic research.

Imagine you're an economist studying a small town's economic trends. You've collected a mountain of data, from income levels to local business growth. You're overwhelmed until you turn to statistical software. With a few clicks, you organize the data, spot correlations, and create predictive models. Suddenly, patterns emerge – you discover that when the local bakery does well, the entire town thrives. Armed with these insights, you recommend town investments that boost the bakery's success, ultimately stimulating the entire local economy. Statistical software transforms raw data into actionable economic insights, helping economists like you make informed decisions that drive prosperity and growth.

I use statistical software for basic documentation, calculation; creating infographics, graphs and tables. It makes my research process accurate, organised and much easier. However, based on my experience, choosing statistical softwares wisely is paramount. Selection should be based on the research question, bulk of data and technical expertise. Choosing software wisely can also help in reducing errors in the reasearch.

I want to add three more software to this list; SPSS, Minitab, and Statistica. I used them and all of them are very useful for research if you don't have a large amount of data.

Statistical software is very much helpful in analysing the data both in qualitative and quantitative form. The researcher only has to know the representation of data. Variables and their relationship

Though different statistical software packages may have different uses and clear differences at high levels, when you're first learning they're all going to perform similarly. Whatever package you start out learning doesn't have to be the one that you stick with for the rest of your research needs. Syntax may differ across packages, but the skills and methodology picked up are easily transferable across packages.

Learn the basics. Expand on the knowledge gleaned. Apply it accordingly, prudently, and stringently—critically assessing, analyzing, and objectively providing accurate and reliable data…free from bias and subjective interpretation. Let the data speak for itself ⭐️✨⭐️✨✨✨✨

Identify your purpose and the set of available tools/frameworks that suit you. Look for a use case to experiment with that mimics your complex multi-layer project or portion of it (you can start with your project if it’s simple). Then, while iterating you will find out the best method and framework, it’s all about doing in the beginning after that you’ll build intelligence to predict the best way just by exploring the use case at hand.

Data exploration through statistics can be classified into 4 zones: 1) Descriptive statistics: Summarising and describing the main features of any dataset through mean, median, mode, standard dev and range. This helps explain the nature of distribution and spread of data. 2) Hypothesis testing: Understanding whether data behaves a particular way or not by testing a population parameter for a sample. 3) Regression: Modelling which variables have stronger correlations and finding the root cause independent variables. 4) Forecasting: Time series analysis for a period or a moment and using past data to predict future values. Having a very good knowledge of these 4 statistical methods is pertinent for quality data exploration.

Before jumping into data analysis it is important to define a clear set of goals: What are you trying to achieve from your dataset? What problem or situation are you trying to solve or understand? What are you trying to infer from the data? Once you have clear goals, you can determine the statistical techniques useful for your work. If you are looking to obtain a simple overview on the nature of data, then descriptive statistics will suffice. If you are trying to answer the validity of a specific question or situation, then do hypothesis testing. If you want to establish relationships between variables or find root cause, then use regression. Lastly, if you are trying to predict future values then use forecasting and time series analysis.

Visualisation begins at the analysis stage itself, and is an iterative process because it helps identify patterns, trends and outliers in data. While statistical softwares greatly enhance your capabilities to explain your data in many novel forms, their relevance is only established if that visualisation is able to infer results that matter to your audience. Thus, it is very important to define who your audience is and what kind of insights are useful for them, regarding the nature of data, questions/situations, relationships, and prescriptive measures. Based on the density of information, define your core variables. For any visualisation talk only about variables central to your theme. Do not talk about more than 3 variables at once.

Having ground context and knowledge about the users, processes, or market, of the industry, subject, or variables you are trying to analyse is as important or even more important than the knowledge of statistical methods and software. During analysis, having general knowledge about the variables and specific knowledge about the context of your data, greatly enhances your capability to filter out the noise, focus on the right questions, target the right data points, and use precision methods. Therefore, one must read as much as possible about the dataset they are working on, because that is the single most important factor in enhancing the accuracy of the results and insights of your analysis.