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.
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.2: The Least Square Line
Problem 8E
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Transcribed Image Text: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.
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