Is a linear relationship between S1 and S2 appropriate? Justify your response. Is the role of S1 significant in predicting S2, at α = 0.001? What is the sample correlation between S1 and S2?

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  1.  Is a linear relationship between S1 and S2 appropriate? Justify your response.

    1. Is the role of S1 significant in predicting S2, at α = 0.001?

      1. What is the sample correlation between S1 and S2?

 

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$residuals)
Normal Q-Q Plot
oo
00
00
00
O Go a
god
00
00 0.
of
25
30
35
40
45
-2
2
fitted values
Theoretical Quantiles
esiduals
15
Score:
Sample Quantiles
091
Transcribed Image Text:To examine whether there is any association between the performances on the two tests, T1 and T2 (test T1 was conducted before T2), and whether T2 scores could be predicted in terms of T1 scores. A random sample of 150 students in Statistics major was selected. Let the scores be denoted S1 and S2 for tests T1 and T2, respectively. The tests had different maximum points. The following outputs using R codes were obtained. fit<-lm (S2~S1) summary(fit) on Call: Im (formula = S2 * S1) 20 25 30 35 40 45 50 Residuals: Score: S1 Min 1Q Median 3Q Маx -13.0214 -3.0934 0.2952 2.9379 14.5996 Coefficients: Estimate Std. Error t value Pr(>[t]) (Intercept) 0.10152 2.51003 0.04 0.968 S1 0.26723 0.01921 13.91 <2e-16 *** Signif. codes: O ***' 0.001 **' 0.01 (*' 0.05 '.' 0.1 '? 1 Residual standard error: 4.472 on 148 degrees of freedom Multiple R-squared: F-statistic: 193.6 on 1 and 148 DF, 0.5667, Adjusted R-squared: 0.5638 p-value: < 2.2e-16 plot (fit$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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