1. David was comparing the number of vocabulary words children know about transportation at various ages. He fit a least-squares regression line to the data. The residual plot and part of the computer output are shown below. 9E Ceof Prediotor Constant AGE Coef 3.371 2.1143 0.2321 1.337 2.88 9.11 0.065 0.001 RESID 9- 0.9710 R-9q = 95.4% R-Sq (adj) = 94.3% 36 4.8 60 7.2 Age (a) What is the equation of the LSRL? (b) What is the predicted number of words for a child of 7.5 years of age? (c) Interpret the slope of the regression line in the context of the problem. (d) Is the line an appropriate model for these data? Explain.
1. David was comparing the number of vocabulary words children know about transportation at various ages. He fit a least-squares regression line to the data. The residual plot and part of the computer output are shown below. 9E Ceof Prediotor Constant AGE Coef 3.371 2.1143 0.2321 1.337 2.88 9.11 0.065 0.001 RESID 9- 0.9710 R-9q = 95.4% R-Sq (adj) = 94.3% 36 4.8 60 7.2 Age (a) What is the equation of the LSRL? (b) What is the predicted number of words for a child of 7.5 years of age? (c) Interpret the slope of the regression line in the context of the problem. (d) Is the line an appropriate model for these data? Explain.
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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