Would it be reasonable to use the least-squares regression line to predict the final grade for a student who has missed 15 classes? Why or why not?

MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
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4. Would it be reasonable to use the least-squares regression line to predict the final grade for a student
who has missed 15 classes? Why or why not?
Transcribed Image Text:4. Would it be reasonable to use the least-squares regression line to predict the final grade for a student who has missed 15 classes? Why or why not?
absences<-c(0,1,2,3,4,5,6,7,8,9)
grade<-c(89.2,86.4,83.5,81.1,78.2,73.9,64.3,71.8,65.5,66.2)
xyplot(absences-grade,
xlab="Absences",
ylab="Final Grade",
type=c("p","r"))
Final Grade
8
2
0
65
70
75
0
Absences
80
cor(absences,grade)
## [1] -0.947395
pricemodel<-lm(grade~absences)
msummary(pricemodel)
85
90
##
Estimate Std. Error t value Pr(>|t|)
## (Intercept) 88.7327
1.8028 49.219 3.21e-11 ***
## absences -2.8273 0.3377 -8.372 3.14e-05 ***
##
## Residual standard error: 3.067 on 8 degrees of freedom
## Multiple R-squared: 0.8976, Adjusted R-squared: 0.8848
## F-statistic: 70.09 on 1 and 8 DF, p-value: 3.143e-05
Transcribed Image Text:absences<-c(0,1,2,3,4,5,6,7,8,9) grade<-c(89.2,86.4,83.5,81.1,78.2,73.9,64.3,71.8,65.5,66.2) xyplot(absences-grade, xlab="Absences", ylab="Final Grade", type=c("p","r")) Final Grade 8 2 0 65 70 75 0 Absences 80 cor(absences,grade) ## [1] -0.947395 pricemodel<-lm(grade~absences) msummary(pricemodel) 85 90 ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 88.7327 1.8028 49.219 3.21e-11 *** ## absences -2.8273 0.3377 -8.372 3.14e-05 *** ## ## Residual standard error: 3.067 on 8 degrees of freedom ## Multiple R-squared: 0.8976, Adjusted R-squared: 0.8848 ## F-statistic: 70.09 on 1 and 8 DF, p-value: 3.143e-05
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