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In a study to determine the effect of vehicle weight in tons (x1) and engine displacement in in³ (x2) on fuel economy in miles per gallon (y), these quantities were measured for ten automobiles. The full quadratic model y = h, +ß,x, + b;x; + hr¡ + Px; + Psx;xz + € Was fit to the data, and the sum of squares for error was SSE = 62.068. Then the reduced model y = Bo + Bix1 + Bzx2 + e was fit, and the sum of squares for error was SSE = 66.984. Is it reasonable to use the reduced model, rather than the full quadratic model, to predict fuel economy? Explain.

In a study to determine the effect of vehicle weight in tons (x1) and engine displacement in in³ (x2) on fuel economy in miles per gallon (y), these quantities were measured for ten automobiles. The full quadratic model y = h, +ß,x, + b;x; + hr¡ + Px; + Psx;xz + € Was fit to the data, and the sum of squares for error was SSE = 62.068. Then the reduced model y = Bo + Bix1 + Bzx2 + e was fit, and the sum of squares for error was SSE = 66.984. Is it reasonable to use the reduced model, rather than the full quadratic model, to predict fuel economy? Explain.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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In a study to determine the effect of vehicle weight in tons (x1) and engine displacement in in³ (x2) on fuel economy in miles per gallon (y), these quantities were measured for ten automobiles. The full quadratic model y = h, +ß,x, + b;x; + hr¡ + Px; + Psx;xz + € Was fit to the data, and the sum of squares for error was SSE = 62.068. Then the reduced model y = Bo + Bix1 + Bzx2 + e was fit, and the sum of squares for error was SSE = 66.984. Is it reasonable to use the reduced model, rather than the full quadratic model, to predict fuel economy? Explain.
Transcribed Image Text:In a study to determine the effect of vehicle weight in tons (x1) and engine displacement in in³ (x2) on fuel economy in miles per gallon (y), these quantities were measured for ten automobiles. The full quadratic model y = h, +ß,x, + b;x; + hr¡ + Px; + Psx;xz + € Was fit to the data, and the sum of squares for error was SSE = 62.068. Then the reduced model y = Bo + Bix1 + Bzx2 + e was fit, and the sum of squares for error was SSE = 66.984. Is it reasonable to use the reduced model, rather than the full quadratic model, to predict fuel economy? Explain.
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