Matakuliah
Tahun
: L0104 / Statistika Psikologi
: 2008
Pengujian Parameter Regresi dan
Korelasi
Pertemuan 20
Learning Outcomes
Pada akhir pertemuan ini, diharapkan
mahasiswa akan mampu :
Mahasiswa akan dapat menghitung dugaan
parameter regresi dan menguji keberartiannya.
3
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Outline Materi
•
•
•
•
Inferensia parameter regresi
Koefisien korelasi
Koefisien determinasi
Inferesia koefisien korelasi
4
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Testing for Significance
• To test for a significant regression relationship,
we must conduct a hypothesis test to determine
whether the value of β1 is zero.
• Two tests are commonly used
– t Test
– F Test
• Both tests require an estimate of σ 2, the
variance of e in the regression model.
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Testing for Significance
• An Estimate of σ 2
The mean square error (MSE) provides the
estimate
of σ 2, and the notation σ2 is also used.
s2 = MSE = SSE/(n-2)
where: SSE 
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2
2
ˆ
(
y

y
)

(
y

b

b
x
)
 i i  i 0 1i
Testing for Significance
• An Estimate of σ
– To estimate σ we take the square root of σ 2.
– The resulting s is called the standard error of the
estimate.
SSE
s  MSE 
n2
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Testing for Significance: t Test
• Hypotheses
H0: β1 = 0
Ha: β1 ≠ 0
• Test Statistic
t
b1
sb1
• Rejection Rule
Reject H0 if t < -tor t > t
where t is based on a t distribution with
n - 2 degrees of freedom.
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Contoh Soal: Reed Auto Sales
• t Test
– Hypotheses
H0: β1 = 0
Ha: β1 ≠ 0
– Rejection Rule
For α = .05 and d.f. = 3, t.025 = 3.182
Reject H0 if t > 3.182
– Test Statistics
t = 5/1.08 = 4.63
– Conclusions
Reject H0
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Confidence Interval for β1
• We can use a 95% confidence interval
for β1 to test the hypotheses just used in
the t test.
• H0 is rejected if the hypothesized value
of β1 is not included in the confidence
interval for β1.
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Confidence Interval for
• The form of a confidence interval for
1
1 is:
b1  t / 2 sb1
where
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b1 is the point estimate
t / 2 sb1 is the margin of error
t / 2 is the t value providing an area
of α/2 in the upper tail of a
t distribution with n - 2 degrees
of freedom
Contoh Soal: Reed Auto Sales
b1  t / 2 sb1
• Rejection Rule
Reject H0 if 0 is not included in the
confidence interval for β1.
• 95% Confidence Interval for β1
= 5 +- 3.182(1.08) = 5 +- 3.44
/
or 1.56 to 8.44/
• Conclusion
Reject H0
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Testing for Significance: F Test

Hypotheses
H 0 : 1 = 0
H a : 1 = 0

Test Statistic
F = MSR/MSE

Rejection Rule
Reject H0 if F > F
where F is based on an F distribution with 1 d.f. in
the numerator and n - 2 d.f. in the denominator.
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Example: Reed Auto Sales

F Test
• Hypotheses
• Rejection Rule
H 0 : 1 = 0
H a : 1 = 0
For  = .05 and d.f. = 1, 3: F.05 = 10.13
Reject H0 if F > 10.13.
• Test Statistic
F = MSR/MSE = 100/4.667 = 21.43
• Conclusion
We can reject H0.
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Some Cautions about the
Interpretation of Significance Tests
• Rejecting H0: β1 = 0 and concluding that the
relationship between x and y is significant does
not enable us to conclude that a cause-andeffect relationship is present between x and y.
• Just because we are able to reject H0: β1 = 0
and demonstrate statistical significance does not
enable us to conclude that there is a linear
relationship between x and y.
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Using the Estimated Regression Equation
for Estimation and Prediction

Confidence Interval Estimate of E(yp)
y p  t /2 s y p

Prediction Interval Estimate of yp
yp + t/2 sind
where the confidence coefficient is 1 -  and
t/2 is based on a t distribution with n - 2 d.f.
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Contoh Soal: Reed Auto Sales
• Point Estimation
If 3 TV ads are run prior to a sale, we expect the
mean number of cars sold to be:
y = 10 + 5(3) = 25 cars
^
• Confidence Interval for E(yp)
95% confidence interval estimate of the mean
number of cars sold when 3 TV ads are run is:
25 + 4.61 = 20.39 to 29.61 cars
• Prediction Interval for yp
95% prediction interval estimate of the
number of cars sold in one particular week
when 3 TV ads are run is:
25 + 8.28 = 16.72 to 33.28 cars
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Residual Analysis
• Residual for Observation i
yi – yi
• Standardized Residual for Observation i
where:
y^i  y^i
sy^i  y^i
syi  yi  s 1  hi
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Contoh Soal: Reed Auto Sales
• Residuals
Observation
1
2
3
4
5
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Predicted Cars Sold
15
25
20
15
25
Residuals
-1
-1
-2
2
2
Contoh Soal: Reed Auto Sales
• Residual Plot
TV Ads Residual Plot
3
Residuals
2
1
0
-1
-2
-3
0
1
2
TV Ads
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3
4
Residual Analysis
• Detecting Outliers
– An outlier is an observation that is unusual in
comparison with the other data.
– Minitab classifies an observation as an outlier if its
standardized residual value is < -2 or > +2.
– This standardized residual rule sometimes fails to
identify an unusually large observation as being an
outlier.
– This rule’s shortcoming can be circumvented by using
studentized deleted residuals.
– The |i th studentized deleted residual| will be larger
than the |i th standardized residual|.
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