Part I. [21 points] Multiple Choice: the following questions has 0, 1, 2, 3 or 4 correct
answers. Each question is worth 3points.
Question 1  The null and alternative hypothesis setting.
Question 2 
The OLS estimator cannot be computed in this situation.
Question 31 – Option C 
is a random variable, it can take on many different values and so if it is
̂
centered around the true , we can have confidence that doing hypothesis testing regarding is valid.
Question 32 – Option C  Minimizing the standard error estimates for
.
̂ ̂
Question 33 – Option B 
The part of the model that explains the predicted movement in the
independent variable
Question 34 – Option D – R square must greater than 0.2
Question 35 – Option C – 1.40
Part II. Please respond to every part of every question, and show your work where possible.
Question 1 is worth 21 points, question 2 is worth 15 points, and question 3 is worth 24
points.
1)
[21 points]
Suppose we’ve collected a sample of
47
observations of hourly wages in US
dollars (
HrWage
) and years of tenure (
Tenure
) at current sales related jobs in 2008. We wish to
estimate the parameters of the following model:
Using Ordinary Least Squares regression, we obtain the following results:
s.e (1.242) (0.267)
Number of Observations = 47
Mean
HrWage
= 12.885
Mean
Tenure
= 2.808
Sum of Squared Residuals = 2072.26
Total Sum of Squares = 2259.02
Explained Sum of Squares = 186.02
Given the information above, answer the following questions:
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a)
[3 pts] Interpret,
in full
, the estimate for the coefficient of tenure above (yes, I know
it’s the slope but what does is tell us)?
The slope for HrWage is 0.538 says that for an increase in HrWage the tenure is increased by
0.538.
[4 pts] Compute the variance of the regression,
and Var ( ).
̂
The variance of the regression = Sum of Squared Residuals/n2 = 2072.26/45 = 46.05022
c) [4 pts] Compute the R2 for this regression. In general, what does R2 tell us (interpret
what this statistic tells us in words)?
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 Spring '10
 RCOLLIER
 Regression Analysis, Statistical hypothesis testing, critical value, test statistic value

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