Category: Statistical Analysis

Premier Insights - Statistical Analysis

Maintaining Proper Perspective of Statistical Methods for Fair Lending Evaluation

We have discussed previously in various posts some of the challenges of using regression and other statistical methods for fair lending reviews as well as the current emphasis on quantitative methods.   Over the last 10 years, the regulatory and enforcement agencies as well as institutions have been gravitating more and more towards statistical methods when […]

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Unraveling the (Seeming) Complexity of CECL

The new CECL standard is presumably designed to enhance the stability of the financial sector by providing more accurate assessments of loan losses.  It also requires a change from the current estimates of loan losses that are produced by most institutions today to projections or forecasts.

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Exploring Statistical Correlation: Causation and Non-Linearity

What does it mean that two variables are correlated? In this video, we explore the meaning of correlation and remind you that, as the old adage says, correlation does not equal causation. Two variables may appear to be correlated when, in fact, they are unrelated, and the apparent correlation is the result of random chance. [...] Read More

Are My Fair Lending Statistical Regression Results Meaningful?

Your fair-lending regression results indicate a statistically significant disparity… now what? In our last blog post, we discussed the importance of a common-sense approach to statistical analysis. One common error in statistical analysis is to assume that a result is practically meaningful just because a result is statistically different from zero. This in not always […]

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Understanding Statistical Significance

As fair lending analysis becomes increasingly technical, industry practitioners have had to familiarize themselves with the terminology of statistical analysis. Statistical significance is one of the most common and foundational concepts to successfully navigating these new waters. Moreover, it is a concept that, when misunderstood, may result in serious error.

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Understanding What is Not in the Data: A CECL Illustration

In our last post, we discussed the importance of understanding both what is and what is not included in the data for regression analysis. In this post, we further emphasize this point with an illustration relevant to a common CECL methodology – the probability of default/loss given default method (PD/LGD).

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The Importance of Sample Segmentation for Regression Analysis

One of the first questions before beginning any type of statistical analysis is what data are included and how should the sample or samples be formulated and segmented. In previous posts, we have addressed various nuances in regard to regression modeling and how the inappropriate application of regression and modeling techniques to real world issues […]

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Statistical Thinking

Economist Karl Popper referred to science as the “art of systematic over-simplification.” Indeed, if science is discovery and knowledge creation, that certainly cannot take place through “systematic over-complication.” Knowledge can only be created by that which is understood, and often the pathway there is through simplification.

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Proper Application of Regression for Fair Lending Analysis

For the last decade the regulatory and enforcement agencies have been increasingly using statistical methods such as regression to evaluate fair lending compliance. With the passage of Dodd-Frank and the new emphasis on modeling and quantification, there has been a fervor to apply econometric techniques to a wide array of issues in the financial industry. […]

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What Does “Statistical Significance” Mean Anyway? Part 2 of 2

In our previous post, we started introducing the concept of statistical significance. We began with making two important points. First, statistical methods are applied in order to estimate or measure an unknown. A sample of data is analyzed which is then used to draw conclusions about a larger population. This is known as statistical inference. […]

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