UNDERSTANDING REGRESSION TO THE MEANĬiting Kahneman’s example, imagine a group of depressed children that have been treated for eight weeks with an energy drink. This is because since all the stores are similar in size and merchandise selection, but their sales differ because of location, competition, and random factors, those stores that did extremely well in 2018 are likely to have a lower sales growth in 2019 than the rest and those that did very poorly are likely to have a higher sales growth in 2019 than the rest. In the above example, predicting 10% sales for each of the stores is an error of judgment because your forecasts need to be regressive (i.e., adding more than 10% to the low-performing branches and adding less (or even subtracting) to the high-performing ones). Regression to the mean, simply put, is the natural tendency of extreme scores to come back to their mean scores. In this chapter, Kahneman clearly explains what regression to the mean is. The above example is adapted from Max Bazerman’s text Judgment in Managerial Decision Making and it appears in the last page of chapter 17 of the book Thinking Fast and Slow by Daniel Kahneman. If you analysis was based on adding 10% to the sales of each store you were most likely wrong, but… why? Keep on reading. You have been instructed to accept the overall forecast of economists that sales will increase overall by 10%. ![]() You are given the results for 2018 and asked to forecast sales for 2019. All stores are similar in size and merchandise selection, but their sales differ because of location, competition, and random factors. Picture this situation: You are the business analyst for a department store chain. ![]() ![]() My attempt to illustrate regression to the mean
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