Explanation Given information: The data points are ( − 4 , 2 ) , ( − 3 , 1 ) , ( 0 , 0 ) , ( 1 , − 3 ) , ( 2 , − 1 ) , ( 3 , − 2 ) . Calculation: The expression for regression line is, y = m x + b …… (1) For the data points ( − 4 , 2 ) , ( − 3 , 1 ) , ( 0 , 0 ) , ( 1 , − 3 ) , ( 2 , − 1 ) , ( 3 , − 2 ) , m = n ∑ k = 1 n x k y k − ( ∑ k = 1 n x k ) ( ∑ k = 1 n y k ) n ∑ k = 1 n x k 2 − ( ∑ k = 1 n x k ) 2 = [ 6 [ ( − 4 ) ( 2 ) + ( − 3 ) ( 1 ) + ( 0 ) ( 0 ) + ( 1 ) ( − 3 ) + ( 2 ) ( − 1 ) + ( 3 ) ( − 2 ) ] − [ { ( − 4 ) + ( − 3 ) + ( 0 ) + ( 1 ) + ( 2 ) + ( 3 ) } { ( 2 ) + ( 1 ) + ( 0 ) + ( − 3 ) + ( − 1 ) + ( − 2 ) } ] 6 [ ( − 4 ) 2 + ( − 3 ) 2 + ( 0 ) 2 + ( 1 ) 2 + ( 2 ) 2 + ( 3 ) 2 ] − [ ( − 4 ) + ( − 3 ) + ( 0 ) + ( 1 ) + ( 2 ) + ( 3 ) ] 2 ] = − 0

7th Edition

ISBN: 9781524916817

Author: SMITH KARL J, STRAUSS MONTY J, TODA MAGDALENA DANIELE

Publisher: Kendall Hunt Publishing

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Chapter 11.7, Problem 32PS

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The least square regression line.

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The least square regression line.

Solution Summary: The author calculates the least square regression line, y=0.5794x-0.5966.

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