Describe the Differences Between Dependency Techniques and Interdependency Techniques
Integration theories usually either implicitly or explicitly assume that regional integration is driven by intraregional economic interdependence, which allows for the utilisation of economies of scale or comparative cost advantages within the region. However, following the new regionalism of the 1990s, it has become clear that regional integration may also be used by the respective member states to improve their standing in the global economy, to become more attractive for foreign direct investment and development aid, or to be more powerful in international trade negotiations.
In most cases, multivariate analysis is conducted by data scientists using advanced statistical software. Most information on these analysis techniques is written with these experts in mind, while business owners, sales managers, marketing managers and investors are usually dismissed as consumers of these products and services. Yet, even if you don't know the difference between a variable and a variant or the difference between metric and nonmetric data, it's helpful to have some understanding of what multivariate analysis techniques are and what they can do for you. The biggest problem with multivariate analysis is that it often gives you the results that you want to find, rather than the results you need. There are two reasons for this. First, the answers you get depend on the type of analysis you conduct. Secondly, because there are often many variables, the way you manipulate these variables can skew your results. Whenever you analyze data, it's always important to remember that correlation does not equal causation. (Write this on a sticky-note and post it above your computer screen.) If sales doubled last week, you can't assume it was because you changed the font in your ad. It could be the reason, or it could be the fact that, unknown to you, your competitor was out of stock that week. Multiple regression, or multiple linear regression, is the most widely used multivariate technique and is often used in forecasting. It's something you can do yourself using Microsoft Excel's Analysis ToolPak add-in. If you've ever done linear regression in Excel using a scatter plot chart, then you understand that Excel adds a line to the chart to predict what will happen based on changes in a single independent variable. That is a regression line showing the relationship between the variables. If advertising increases, for example, the regression line typically shows that sales increase. Multiple regression does the same thing. However, it has two or more independent variables instead of one. For example, instead of showing only the relationship between sales and advertising, it can show other variables, such as price, the day of the week or changes to the GDP. Multiple regression can show you which of these variables, or a combination of variables, is most closely tied to increases in sales. iscriminant function analysis is used to classify observations or people into different groups. A good example of this is classifying potential customers based on how much they are likely to spend. Suppose you want to identify who is more likely to buy from you within the next year after subscribing to your weekly newsletter. Using discriminant function analysis, you can apply several variables to your current customers, such as their gender, age and income level, all compared to the amount they spent in the past year. This analysis should give you different combinations of variables that make one person more likely to become a major customer than another. Then, when you get new subscribers who are in the "big spender" category, you can offer them different incentives than someone who is in the "little spender" category.
Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed (Gil Pascual JA., 2003). There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use. With the development of information technology and communication, it is now easier to make processes of collection, storage and transportation of large—both in volume and complexity—databases from observation or experimentation. A multivariate approach will help to illuminate the inter interrelatedness between and within sets of variables. Multivariate analysis in a broad sense is the set of statistical methods aimed simultaneously analyze datasets. That is, for each individual or object being studied, analyzed several variables. The essence of multivariate thinking is to expose the inherent structure and meaning revealed within these sets if variables through application and interpretation of various statistical methods (Afifi A, May S, 2011).
To summarize, we grow up in a world that instills us with competitive, individualistic values. Our educational systems and our culture teach us how to develop as independent individuals, where the more you can be “stronger, richer and faster” than others, the more you will succeed. Such an approach was arguably suitable for a certain period in history, but as we develop into a new era marked by tightening global interdependence—from the immense exponential human population growth to the worldwide interconnection of our economies, technologies and cultures—the more we see that a competitive, individualistic approach in an interdependent reality leads to crises on all scales: personal, social, global and ecological.
Gil Pascual JA. Methods worth of research in education. Multivariate Analysis 2003.
Grimm LG, Yarnold PR. eds. Reading and understanding multivariate statistics. Washington, DC: American Psychological Association Washington, 2011.
Rencher AC, Christensen WF. eds. Methods of Multivariate Analysis. New Jersey: Wiley, 2012.
Afifi A, May S, Clark VA. Practical Multivariate Analysis, Fifth Edition. Boca Raton, FL: CRC Press, 2011.