To examine whether there is any association between the performances on the two sests, T1 and T2 (test T1 was conducted before T2), and whether T2 scores could be predicted in terms of T1 scores. A random sample of 150 students in Statistics major vas selected. Let the scores be denoted S1 and S2 for tests T1 and T2, respectively. The tests had different maximum points. The following outputs using R codes were obtained. it<-lm(S2~S1) summary(fit) o8 Call: Lm (formula = S2 * S1) 20 25 30 35 40 45 50 Residuals: Score: S1 Min 1Q Median 3Q Max -13.0214 -3.0934 0.2952 2.9379 14.5996 Coefficients: Estimate Std. Error t value Pr(>]t]) Intercept) 0.10152 51 2.51003 0.04 0.968 0.26723 0.01921 13.91 <2e-16 *** --- Signif. codes: O ***' 0.001 **' 0.01 *' 0.05 '.’ 0.1 ' 1 Residual standard error: 4.472 on 148 degrees of freedom lultiple R-squared: 0.5667, Adjusted R-squared: 0.5638 -statistic: 193.6 on 1 and 148 DF, p-value: < 2.2e-16 lot (fit$fitted,fit$residuals, xlab="fitted values", ylab="residuals") aqnorm(fit$residuals) Normal Q-Q Plot 15 10 5. 00 00 88o 00 e 00 00 god 00 0. 00 우 25 30 35 40 45 2 -1 2 Sample Quantiles
To examine whether there is any association between the performances on the two sests, T1 and T2 (test T1 was conducted before T2), and whether T2 scores could be predicted in terms of T1 scores. A random sample of 150 students in Statistics major vas selected. Let the scores be denoted S1 and S2 for tests T1 and T2, respectively. The tests had different maximum points. The following outputs using R codes were obtained. it<-lm(S2~S1) summary(fit) o8 Call: Lm (formula = S2 * S1) 20 25 30 35 40 45 50 Residuals: Score: S1 Min 1Q Median 3Q Max -13.0214 -3.0934 0.2952 2.9379 14.5996 Coefficients: Estimate Std. Error t value Pr(>]t]) Intercept) 0.10152 51 2.51003 0.04 0.968 0.26723 0.01921 13.91 <2e-16 *** --- Signif. codes: O ***' 0.001 **' 0.01 *' 0.05 '.’ 0.1 ' 1 Residual standard error: 4.472 on 148 degrees of freedom lultiple R-squared: 0.5667, Adjusted R-squared: 0.5638 -statistic: 193.6 on 1 and 148 DF, p-value: < 2.2e-16 lot (fit$fitted,fit$residuals, xlab="fitted values", ylab="residuals") aqnorm(fit$residuals) Normal Q-Q Plot 15 10 5. 00 00 88o 00 e 00 00 god 00 0. 00 우 25 30 35 40 45 2 -1 2 Sample Quantiles
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
Related questions
Question
Transcribed Image Text:(vii)
vs fitted value" plot? Support your observation.
Is there any departure from the model assumptions indicated by the "residual
(viii)
quantile plot"? Support your observation.
Is there any departure from the model assumptions indicated by "normal
![To examine whether there is any association between the performances on the two
tests, T1 and T2 (test T1 was conducted before T2), and whether T2 scores could be
predicted in terms of T1 scores. A random sample of 150 students in Statistics major
was selected. Let the scores be denoted S1 and S2 for tests T1 and T2, respectively.
The tests had different maximum points. The following outputs using R codes were
obtained.
fit<-lm (S2~S1)
summary(fit)
on
Call:
Im (formula = S2 *
S1)
20
25
30
35
40
45
50
Residuals:
Score: S1
Min
1Q
Median
3Q
Маx
-13.0214
-3.0934
0.2952
2.9379
14.5996
Coefficients:
Estimate Std. Error t value Pr(>[t])
(Intercept) 0.10152
2.51003
0.04
0.968
S1
0.26723
0.01921
13.91
<2e-16 ***
Signif. codes:
O ***' 0.001 **' 0.01 (*' 0.05 '.' 0.1 '? 1
Residual standard error: 4.472 on 148 degrees of freedom
Multiple R-squared:
F-statistic: 193.6 on 1 and 148 DF,
0.5667, Adjusted R-squared:
0.5638
p-value: < 2.2e-16
plot (fit$fitted,fit$residuals, xlab="fitted values", ylab="residuals")
qqnorm (fit$residuals)
Normal Q-Q Plot
oo
00
00
00
O Go a
god
00
00 0.
of
25
30
35
40
45
-2
2
fitted values
Theoretical Quantiles
esiduals
15
Score:
Sample Quantiles
091](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3d9f0c79-5948-4e41-a6e8-a52a7ef0de61%2Fc4ef10f0-d339-46d8-837c-283192d7ec1a%2Fn8eq72u_processed.png&w=3840&q=75)
Transcribed Image Text:To examine whether there is any association between the performances on the two
tests, T1 and T2 (test T1 was conducted before T2), and whether T2 scores could be
predicted in terms of T1 scores. A random sample of 150 students in Statistics major
was selected. Let the scores be denoted S1 and S2 for tests T1 and T2, respectively.
The tests had different maximum points. The following outputs using R codes were
obtained.
fit<-lm (S2~S1)
summary(fit)
on
Call:
Im (formula = S2 *
S1)
20
25
30
35
40
45
50
Residuals:
Score: S1
Min
1Q
Median
3Q
Маx
-13.0214
-3.0934
0.2952
2.9379
14.5996
Coefficients:
Estimate Std. Error t value Pr(>[t])
(Intercept) 0.10152
2.51003
0.04
0.968
S1
0.26723
0.01921
13.91
<2e-16 ***
Signif. codes:
O ***' 0.001 **' 0.01 (*' 0.05 '.' 0.1 '? 1
Residual standard error: 4.472 on 148 degrees of freedom
Multiple R-squared:
F-statistic: 193.6 on 1 and 148 DF,
0.5667, Adjusted R-squared:
0.5638
p-value: < 2.2e-16
plot (fit$fitted,fit$residuals, xlab="fitted values", ylab="residuals")
qqnorm (fit$residuals)
Normal Q-Q Plot
oo
00
00
00
O Go a
god
00
00 0.
of
25
30
35
40
45
-2
2
fitted values
Theoretical Quantiles
esiduals
15
Score:
Sample Quantiles
091
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