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?
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?
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 5E
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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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F35038f16-94a1-4399-bb46-35e628254d59%2Ff7325639-cb62-463b-9344-d5f2f52a2f86%2Fd4mg0j_processed.jpeg&w=3840&q=75)
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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