Simple linear regression results: Dependent Variable: Head Circumference (inches), y Independent Variable: Height (inches), x Head Circumference (inches), y = 12.493169 + 0.18273245 Height (inches), x Sample size: 11 R (correlation coefficient) 0.91107273 R-sq 0.83005352 Estimate of error standard deviation: 0.095384195

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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 A pediatrician wants to determine the relationship that exists between a
child’s height, x, and head circumference, y. She randomly selects 11 children from her practice, measures their heights and head circumferences, and conducts the least-squares regression analysis with the simple linear model using StatCrunch. The output is given below:

(a) Write down the equation of the least-squares regression line treating height as the explanatory variable and head circumference as the response variable.


(b) Interpret the slope and y-intercept, if appropriate.


(c) Use the regression equation to predict the head circumference of a child who is 25 inches tall. Assume that the regression model is applicable.

(d) It is observed that one child who is 25 inches tall has a head circumference of 17.5 inches. Is the observed value above or below average among all children with heights of 25 inches?

Simple linear regression results:
Dependent Variable: Head Circumference (inches), y
Independent Variable: Height (inches), x
Head Circumference (inches), y = 12.493169 + 0.18273245 Height (inches), x
Sample size: 11
R (correlation coefficient) 0.91107273
R-sq 0.83005352
Estimate of error standard deviation: 0.095384195
Transcribed Image Text:Simple linear regression results: Dependent Variable: Head Circumference (inches), y Independent Variable: Height (inches), x Head Circumference (inches), y = 12.493169 + 0.18273245 Height (inches), x Sample size: 11 R (correlation coefficient) 0.91107273 R-sq 0.83005352 Estimate of error standard deviation: 0.095384195
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