Explanation Given information: It is given that the regression line associated with the points ( a , b ) and ( c , d ) is same as the regression line passing through the points ( a , b ) and ( c , d ) . Consider the given information is based on the regression line. When a model has a collection of data ( x 1 , y 1 ) , ( x 2 , y 2 ) , ...... , ( x n , y n ) with a linear equation y ^ = m x + b , then the residuals which are the differences of observed and predicted values of y is, ( y 1 − y ^ 1 ) , ( y 2 − y ^ 2 ) , ........ , ( y n − y ^ n ) . And the sum-of-squares error (SSE) which is the sum of square of the residuals is given by, S S E = ( y 1 − y ^ 1 ) 2 + ( y 2 − y ^ 2 ) 2 + ........ + ( y n − y ^ n ) 2 The regression line is the line which gives the minimum sum-of-square error for the data points ( x 1 , y 1 ) , ( x 2 , y 2 ) ,……, ( x n , y n ) and given by, y = m x + b Next, the line is associated with the points ( a , b ) and ( c , d )

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7th Edition

ISBN: 9781337652742

Author: Stefan Waner, Steven Costenoble

Publisher: Cengage Learning

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Chapter 1.4, Problem 37E

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The reason behind that the regression line associated with the points (a,b) and (c,d) is same as the regression line passing through the points (a,b) and (c,d), Where a≠c.

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Chapter 1.4, Problem 36E

Chapter 1.4, Problem 38E

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WebAssign Printed Access Card for Waner/Costenoble's Applied Calculus, 7th Edition, Single-Term

Ch. 1.1 - In Exercises 1-4, evaluate each expression based...Ch. 1.1 - In Exercises 1-4, evaluate each expression based...Ch. 1.1 - In Exercises 1-4, evaluate each expression based...Ch. 1.1 - In Exercises 1-4, evaluate each expression based...Ch. 1.1 - Prob. 5E Ch. 1.1 - Prob. 6E Ch. 1.1 - In Exercises 5-8, use the graph of the function f...Ch. 1.1 - Prob. 8E Ch. 1.1 - In Exercises 9-12, say whether or not f(x) is...Ch. 1.1 - In Exercises 9-12, say whether or not f(x) is...

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The reason behind that the regression line associated with the points ( a , b ) and ( c , d ) is same as the regression line passing through the points ( a , b ) and ( c , d ) , Where a ≠ c .

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The reason behind that the regression line associated with the points (a,b) and (c,d) is same as the regression line passing through the points (a,b) and (c,d), Where a≠c.

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