Introduction to Statistics and Data for Economics
Students enrolled in the economic and business studies taking a basic course in macroeconomics will come across statistics, probability, and data. This is important because making business decisions for investment other processes requires reliable data. Before opening a business anywhere, one has to carry out market research, which may involve using statistical data from existing materials or taken straight from the field. Even those who have studied statistics in the past may still want to remind themselves of what it is before embarking a project. One important thing to note is that leaning statistics from scratch is possible with such a simple material. However, you may want to dig deeper with more careful reading for a more compact understanding.
In this article, we shall be looking at the general concept of statistics and probability. Data is a general aspect that comes through automatically once one you start learning the two terms. Business information is vital for making investment decisions. Besides, it tells a firm whether what they are doing is beneficial or not. When carrying out a market survey, marketing, and advertising, the emerging results usually define the firm's next step.
Also, governments used statistical data to estimate the consumption rate for particular products within a given period, leading to an understanding of the economic growth rate. When looking at GDP and GNP, it is, for instance, data from the past years that indicate how much the country has grown. For instance, they may look at the consumption of locally manufactured products in relation to the imported good and use that to determine where more input is needed.
Studying these subjects is not only beneficial for academic success but for general live applications too. Students become knowledgeable in making decisions based on data, which encourages personal growth and development. The world economy depends on young and innovative minds when it gives students taking economics a better position in the job market. Statistics and probability are two crucial subjects for current and future economies.
When you talk about statistics, many will only think about the numerical data – referring to the rate of unemployment in the past few years, the total government expenditure over a given period, the crime rate in a given city, the number of goods imported and exported, and many other things. There is nothing wrong with this view since statistics is all about creating and using data. However, we need to look at it from a more detailed perspective. Looking it like professionals means we consider it as a methodology of collection, classifying, making summery, put into organized groups, presenting, analysis, and interpretation of numerical information.
Statistics in economics
Firms follow statistical methods and ideology to decide on which product they should produce and what they should do with it – how much they spend on advertising, evaluating employees, and how much to sell the product for. Also, a firm may be thinking of spending on new machinery and equipment, before committing, they will need to learn the market process, and know whether the returns will reflect what they are investing. When there is nothing much to look for, a business is likely to make the wrong decisions. Besides, companies have to keep taps on their inventories, to understand their volume, as well as every aspect of running their operations. And for each of these needs, they will require numbers, since in business, "numbers don't lie." The motivation for using statistics in business is somehow different from other subjects—the main aim, particularly for using statistics in economics to understand social and economic systems operations. While leaning the approach to statistics and its uses in economics, it is also vital to consider how businesses use statistics. This is because most of the exercises you will be handling in your textbooks will concentrate on business.
Let's think about views and interpretations of how things work in the business world – theories. An economy theory attempts to explain how business processes work, and they are made up of two aspects: the logical structure which is true by definition (tautological), and the second which involves a set of principles and parameters in the logical aspect with presents the empirical content on the thought. The logical part of a theory, which is true by definition, does not carry any interest, except where it helps users build prepositions that can be tested, in learning the operations of a business system. If we find facts that can be tested successfully, and give out provable results of the theory, then that thought will be accepted as true until new evidence comes out that shows otherwise. If the logic within the idea is consistent with other theories, then it is valuable – but only if the other theory is also established as true. It must be consistent with the possibility of being rejected, but available evidence (which is consistent too).
Look at the statement, "People maximize utility." This is very true because we define behavior by what people do. On the other hand, a utility is defined as what people do maximum when they choose one thing over another at the cost of the other. This is the definition linked with the maximization of utility approach, which can be tested for empirical prepositions. Hence, a person may choose to use the parameters in the tautological consumption maximizing structure, whereby the minimal consumption of a product reduces directly proportional to the minimum consumption of other goods. The quantity of a good or service consumer increases with the number of other goods. These processes create a downward sloping curve, which makes it to test the idea that "demand curves slow down empirically." This is called the theory of demand, which one can either support or vote against, depending on the evidence that comes up with from price date, quantities, and income for the involved groups of individuals. The set of truthful definitions from utility maximization is critical since they are consistent internally, which leads to the creation of testable results.
Now consider the statement "Australia is beautiful." This is a statement that may seem true, but it is not testable. The definition of the term "beautiful" varies depending on what each individual thinks. If, for instance, consider the flush toilets per capita, that say, India, then you can test such. But when you consider the analytical structure built around this statement, then the information is not helpful because there is another logical structure from which the definition of beautiful can be made.
Another good example is, "The rich get richer as the poor get poorer." You can taste this idea because we all know what it means to be rich and what it means to be poor. However, there is something interesting about this statement, in relation to some theories on how economic functions in wealth creation and distribution. In such a theory, there will be automatically something to do with how economic systems can be altered to prevent inequalities.
Statistics is one of the methods that economists use to face some theories, like the demanding idea. It also leads us to meet some testable prepositions using existing facts. In this case, statistics become a set of principles and concepts on who basis be build the acceptance or rejection of an idea. Their procedures and intellectual processes have become an essential aspect of modern business. We cannot never just accept a theory because others have found it applicable, rather we use the existing statistical data to look and the truthfulness within the theory and whether it can be tested with data. Statistics help us to decide what to believe and what to put aside as heresy. In other words, it forms the basis of human knowledge,
Note that theories are never strictly true. The logical prepositions within them must be true, but not the theory itself. This is because a theory is merely an accepted truth that accepted because of being consistent. And there is some evidence at a particular point that supports the statement. Looking at how consistent an idea is, it may not be proper for a government, or an individual to act as if the theory were true. There would be very tough consequences if they took it as true, and then it turns out not to be true. Theories always have opposing ideas, and as empirical evidence continues to pile against it, eventually, the 'other' theory will be accepted in its stead. It is all about the availability of evidence.
Statistics stands as the analytical tool for testing theories; hence, an essential aspect of a scientific process. Theories are ideas suggested by individual observers, which can change at ant time. On the other hand, statistics goes much deeper, as a systematic investigation corresponds to theories in real life. We can therefore say, a theory is virtual, inexistent without statistical evidence to give it flesh. The truth about a particular theory will be accepted or rejected based on the truth from data. It is not easy to design public policy. It is a complex process that takes a lot of ideas, whereby some members of a society will gain, while others will lose. There are also advocacy groups involved in the development, which come with special interests in showing that the policy actions they are interested in are also in the public interest.
It is vital to understand how to define, understand, and draw conclusions from data. It can help to sort out the conflicting ideas of farmers, consumers, labor unions, and many other players in the policy issues.
Businesses also face problems that are different from policy issues. It is important that all involved in business solutions can contribute positively to a measurable solution that can maximize the profits of a firm. Individual workers in an enterprise tap into their own utility (which may not be the object of the enterprise), just like they follow their own ultimate goals. It is all about having enough information to make substantial decisions.
Statistical thinking takes place following two critical sets of processes—the first one of the descriptions and presentation of information. Then, there is the process of using data to make inferences on aspects of the environment where the data comes from – which can also involve the underlying methods that created the data ( like the current functions of economy, or accounting system, or perhaps the production line in a company. As such, we get two methods called descriptive statistics, and the second is inferential statistics.
In descriptive statistics, there is the utilization of numeral and geographical methods to identify patterns in data. It is then followed by a summary of the information, which is presented later in a meaningful manner. On the other hand, inferential statistics about the use of data create estimations, decisions, predictions, and other general ideas concerning the background of data.
The use of statistical inferences helps in acquiring data population, or process. It involves the use of a sample of elements from the population, or from the process. While doing this, experts have to define a population or a process. A population is usually a set of units, mostly people, objects, transactions or events, that the person is interested in learning about. A process is a method that produces output. If a firm is interested in the items from a specific assembly line, that is perhaps defective; it could mean the process is defective. An economic expert may be interested in learning how the rate of unemployment changes with change in monetary and fiscal policies.
Statistics is very critical to making business and firm decisions. And it all comes down to data sets. Economists use three types of data sets, which include cross-sectional, time-series, and panel. And within these are quantitative and qualitative data sets. Qualitative is about a natural numerical scale, whereas qualitative data cannot be measured. All these types of data can be used in a single process to determine a business process and eventually make a decision. Statistics and data are important contributors to economic analysis and growth.
Author: James Hamilton