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Accuracy of Prediction How accurate are predictions based on a correlation? Accuracy depends on rXY If we know nothing about an individual (e.g., we try to predict the IQ of a randomly selected person), we should guess the mean. If we always guess the mean, then the variance tells us the average “cost” of our guesses. However, if we use X to predict Y, we can reduce this cost by r-squared. On Sale: How Accurate? By squaring the correlation, we know what percentage of variance will be reduced by using X to predict Y. If r = 1 or r = -1, the squared value is 1. These are both cases of perfect prediction, like 100% off. If r = ½ or r = -½, the squared correlation is ¼ or .25. This means that a correlation of .5 only reduces the cost by 25%. Variance of Residuals: the “standard error of regression” The average squared deviation between the guess and the actual value of Y is called the variance of residuals (errors) You compute it by multiplying the original variance of Y by (1 – r2), where r is the correlation between X and Y. The standard error of regression is the square root of this variance. Sample Problem Suppose we use sister’s IQ to predict brother’s IQ. The means of X and Y are both 100, and the standard deviations are both 15. The variance of predicting Joe’s IQ if we don’t know Jane’s IQ is 225. The correlation is .5, so the variance of the residuals is (1-.25)(225) = 168.75. Standard Deviation of Errors Take the square root of the variance of residuals to compute the standard error of regression, i.e., the standard deviation of differences between predicted and obtained. For our problem, the square root is 12.99, approximately 13. Knowing Sister’s IQ reduces the standard deviation of residuals from 15 to 13. Summary If Jane has an IQ of 130, we predict her brother to have an IQ of 115. However, not all brothers of sisters with such IQ will be exactly 115. On average, they will have a mean IQ of 115, with a standard deviation of 13. The probability that Joe has a higher IQ than his sister is only about 12%.