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Accuracy of Prediction

Accuracy of Prediction. How accurate are predictions based on a correlation?. Accuracy depends on r XY. 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.

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Accuracy of Prediction

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  1. Accuracy of Prediction How accurate are predictions based on a correlation?

  2. 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.

  3. 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%.

  4. 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.

  5. 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.

  6. 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.

  7. 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%.

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