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1)
Suppose that Y = 6.0 – 3.5X + e is an estimated regression line. The slope of the line is
a)
6.0
b)
2.5
c)
3.5
d)
–3.5
2)
Suppose that Y = 6.0 – 3.5X + e is an estimated regression line. Then the predicted value of
Y given that X = 2 is
a)
6.0
b)
1.0
c)
1.0 + e
d)
–1.0
3)
Suppose we test the null hypothesis, H
0
:
β
1
= 0, versus the alternative H
A
:
β
1
≠
0 at the 10%
significance level. The pvalue of the test statistic is 0.22. Then
a)
The probability of a type I error is 10% and we should reject the null hypothesis
b)
The probability of a type I error is 34% and we should fail to reject the null hypothesis
c)
The probability of a type I error is 10% and we should fail to reject the null hypothesis
d)
The probability of a type I error is 34% and we should reject the null hypothesis
4)
In the estimated regression line, Y = b
0
+ b
1
X + e, what is the name given to e?
a)
The error term
b)
The standard error
c)
The residual or observed error
d)
The predicted value
5)
The sum of squared errors (for a regression of Y on X) is calculated as
a)
(
±
)
YY
ii
i
N
−
=
∑
2
1
b)
()
i
i
N
−
=
∑
2
1
c)
(
±
)
i
N
−
=
∑
2
1
d)
(
±
)
YYY
i
N
−−
=
∑
2
1
6)
When conducting hypothesis tests concerning the parameters of the population regression
line, the standard error of b
1
a)
Is the same as the standard error of b
0
b)
Is the same as the mean square error (MSE) of the regression
c)
Is equal to
σ
/(N2)
d)
Is equal to
s
SS
x
7)
When we plot the residuals in a regression analysis, we know that the linear model makes
sense only when
a)
We see a ushaped pattern in the residual plot
b)
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 Spring '08
 BethIngram
 Statistics, Linear Regression, Regression Analysis, TA, Teaching assistant, TA Jane

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