Linear Regression 2 Notes Spring

# Linear Regression 2 Notes Spring - Linear regression 7 of...

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Unformatted text preview: Linear regression 7 of 11 [ 1 ] 1.1767 e+13 R: y . bar = mean(y) R: y . bar [ 1 ] 2.816 e+22 (b) Find the slope using equation 7 : R: b1 = sum(( x- x . bar ) * (y- y . bar ) )/sum(( x- x . bar ) ˆ2) R: b1 [ 1 ] 1339313079 (c) Find the y-intercept using equation 8 : R: b0 = y . bar- b1 * x . bar R: b0 [ 1 ] 1.2400 e+22 Thus our linear model for this relationship is: ˆ y = (1 . 24 e + 22) + (1 . 34 e + 09) · x Question 3 . Since the slope should indicate the age of the universe, what is the universe’s age? MAKING PREDICTIONS To predict the average value of the response variable for a given x : plug in the specific value of x that you wish to make a prediction for in the regression equation. Cautions when making predictions • Stay within the scope of the data. Don’t predict outside the range of sample x values. • Ensure your model is applicable for what you wish to predict. Is it the same population? Is the data current? When the linear correlation is NOT significant If the linear correlation is not significant, you should not use the regression model for predictions. In this case, the best point estimate for y is ¯ y . Question 4 . For our Hubble data model, what is the maximum velocity that we can use to make a prediction?...
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## This note was uploaded on 02/10/2012 for the course MAT 1670 taught by Professor Tanbakuchi during the Spring '11 term at University of Florida.

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Linear Regression 2 Notes Spring - Linear regression 7 of...

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