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Computer scientists have used the powerful lever of computing power to make significant contributions to a variety of fields, including statistics and economics. The specific techniques that rival regression are part of machine learning. A recent hot topic that covers most of these techniques is the realm of data science. While regression analysis is a traditional tool that has been in use since the early 19 th century, there are many other tools. We are not going into details on this topic, but in the remainder of this presentation we will use the term regression to include the nonlinear case (see Smart 2017 for more details). In the spacecraft and defense industry it is more common to see nonlinear relationships between cost and cost drivers, e.g., Y = aX b. Galton found a positive correlation between these heights but found a tendency to return or “regress” toward the average height, hence the term “regression analysis.” Francis Galton later applied this technique to find linear predictive relationships between various phenomena, such as the relationship between the heights of fathers and sons. This method was first developed by the mathematicians Legendre and Gauss in the early 19th century, who used it to predict the orbits of heavenly bodies using observed data.
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For example, linear regression finds the “best fit” by finding the parameters a and b that minimize the sum of the squares of the residuals. Residuals or “errors” are an important consideration in modeling since they often drive the methods used for parameter calculation. This is also referred to as the “error” term since it is the difference between the actual cost and the estimated cost. We provide pros and cons of alternatives to regression, and a cross-sectional example that illustrates the similarities and differences of a variety of techniques outside of the traditional methodology. In this paper, we look at a variety of methods for predictive analysis for cost estimating, including other supervised methods such as neural networks, deep learning, and regression trees, as well as unsupervised methods and reinforcement learning. Indeed, traditional linear and nonlinear regression is a small subset of supervised machine learning methods. However, regression analysis is only one of many tools in data science and machine learning. In this paper we look at a variety of methods for predictive analysis for cost estimating, including other supervised methods such as neural networks, deep learning, and regression trees, as well as unsupervised methods and reinforcement learning.Ībraham Maslow famously wrote in his classic book The Psychology of Science, “I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.”Ĭost estimating has relied primarily upon regression analysis for parametric estimating. However, regression analysis is only one of many tools in data science and machine learning and is a small subset of supervised machine learning methods. Cost estimating has relied primarily upon regression analysis for parametric estimating.