small Regression Model Summary Adjusted R Śquare Std. Error of the Estimate Model R R Square .491ª .241 .240 1 12.3562 a. Predictors: (Constant), Do you smoke?, Body Mass Index Coefficients Standardized Coefficients Unstandardized Coefficients Model B Std. Error Beta Sig. 1 (Constant) 100.00 2.000 50.000 .000 Body Mass Index -2.00 .100 -.459 -20.000 .000 Do you smoke? -10.00 1.250 -.189 -8.000 .000

small Regression Model Summary Adjusted R Śquare Std. Error of the Estimate Model R R Square .491ª .241 .240 1 12.3562 a. Predictors: (Constant), Do you smoke?, Body Mass Index Coefficients Standardized Coefficients Unstandardized Coefficients Model B Std. Error Beta Sig. 1 (Constant) 100.00 2.000 50.000 .000 Body Mass Index -2.00 .100 -.459 -20.000 .000 Do you smoke? -10.00 1.250 -.189 -8.000 .000

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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A study wants to access the relationship between volume and weight of batch of logs of wood (100 logs). It turns out that the scale used to measure the weight is not working appropriately, and every time a log of wood is measured, the next mesaurement will be 1 lb more than it should be (eg. for the 10th log, the scale will read 10 lb more than their actual weight). Identify the assumption of regression that would be violated in this situation. If the experiemnters figured out this problem at the 100th measurement, can anything be done such that the data might still be used?
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