Describe the Differences Between Dependency Techniques and Interdependency Techniques
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 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).
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.
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Afifi A, May S, Clark VA. Practical Multivariate Analysis, Fifth Edition. Boca Raton, FL: CRC Press, 2011.