mba 522 11 Regression-mixing up X and Y

# mba 522 11 Regression-mixing up X and Y - In forecasting...

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Unformatted text preview: In forecasting, the variable to be predicted (estimated, forecasted) is always Y. Below you see closing prices for Standard & Poor's 500 and Dow Jones Industrial Average. Suppose the closing price for the DJIA is 11,000. Estimate the closing price for the S&P 500. You must note that since S&P is to be estimated, it should be treated as Y variable in generating the regression equation. If you do it the other way (S&P set to be X), you get totally different results: If S&P is set to be Y, and DJIA is set to be X, your computer output would include the following: interecpt = -137.63, slope = 0.149 (I have rounded both). Hence the regression equation is: Y= -137.63 + 0.149X (see below for output). That means: S&P = -137.63 + 0.149 (DJIA) By using DJIA value of 11,000, you can then compute and estimate of S&P closing price. On the other hand, if you set X=S&P, and Y=DJIA, you get very different results. The regression equation will be: Y= 2440.9 + 5.7X This equation estimates Y (which is DJIA). So, by plugging a value for S&P closing price, you can estimate DJIA.This equation estimates Y (which is DJIA)....
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## This note was uploaded on 11/13/2011 for the course MBA 522 taught by Professor Nabavi during the Spring '08 term at Bellevue.

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