math171 lab7 3-13-08

math171 lab7 3-13-08 - y =-0.00821362(2728 48.7393 = 26.33...

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Lab 7 2. Weight seems to be the most strongly correlated with MPG, because its correlation (-0.869) 2 is closer to 1 than any of the other correlation coefficients. 3. Yes, this scatterplot shows a strong negative correlation, with the data points mostly along a straight line. 4.
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This analysis is based on 91 cars. The equation of the regression line is: y = -0.00821362 x + 48.7393. The slope is the amount of MPG gained for every 1-pound increase in weight. R 2 is the accuracy of the regression model in relation to the data points. The Geo
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Unformatted text preview: : y = -0.00821362(2728) + 48.7393 = 26.33 expected MPG. The Geo Prizm is 8.37 mpg higher than expected. Model Success: The distance between each point and the line tells how far above or below the expected fuel efficiency for its weight for each vehicle. Quest for a Better Model: This new model is better than the original. The R 2 is a higher value using GpHM rather than MPG, and the scatterplot of the residuals is a more random distribution, without the slight curve of the MPG residuals plot....
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math171 lab7 3-13-08 - y =-0.00821362(2728 48.7393 = 26.33...

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