2. Pengujian Parameter Regresi dan Korelasi Pertemuan 20
Matakuliah : L0104 / Statistika Psikologi
Tahun : 2008
Pengujian Parameter Regresi dan Korelasi Pertemuan 20

3. 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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4. Inferensia parameter regresi Koefisien korelasi Koefisien determinasi
Outline Materi
Inferensia parameter regresi
Koefisien korelasi
Koefisien determinasi
Inferesia koefisien korelasi 4
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5. 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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6. 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:
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7. 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.
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8. Testing for Significance: t Test
Hypotheses
H0: β1 = 0
Ha: β1 ≠ 0
Test Statistic
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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9. 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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10. 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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11. Confidence Interval for 1
The form of a confidence interval for 1 is:
where b1 is the point estimate
is the margin of error
is the t value providing an area
of α/2 in the upper tail of a
t distribution with n - 2 degrees
of freedom
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12. Contoh Soal: Reed Auto Sales
Rejection Rule
Reject H0 if 0 is not included in the confidence interval for β1.
95% Confidence Interval for β1
= (1.08) =
/ or to 8.44/
Conclusion
Reject H0
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13. Testing for Significance: F Test
Hypotheses
H0: 1 = 0
Ha: 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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14. Example: Reed Auto Sales
F Test
Hypotheses H0: 1 = 0
Ha: 1 = 0
Rejection Rule
For  = .05 and d.f. = 1, 3: F.05 = 10.13
Reject H0 if F >
Test Statistic
F = MSR/MSE = 100/4.667 = 21.43
Conclusion
We can reject H0.
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15. 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-and-effect 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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16. Using the Estimated Regression Equation for Estimation and Prediction
Confidence Interval Estimate of E(yp)
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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17. 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 = (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:
= to 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: = to cars ^
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18. Residual Analysis Residual for Observation i yi – yi
Standardized Residual for Observation i
where: ^ ^ ^ ^
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19. Contoh Soal: Reed Auto Sales
Residuals
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20. Contoh Soal: Reed Auto Sales
Residual Plot
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21. 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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22. Selamat Belajar Semoga Sukses
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