Let {−0.8, −0.4, −0.2, 0.6} be a sample from a uniform distribution on [−1, 1]. Based on
this sample, please generate (simulate) a sample which is from an exponential
distribution 0.5e−x / 2 .(10%)
Multiple regression was used to explain stock returns using the following variables:
Dependent variable:
RET = annual stock returns (%)
Independent variables:
MKT = Market capitalization = Market capitalization / $1.0 million
IND = Industry quartile ranking (IND = 4 is the highest ranking)
FORT = Fortune 500 firm, where {FORT = 1 if the stock is that of a Fortune 500
firm, FORT = 0 if not a Fortune 500 stock}
The regression results are presented in the tables below
The expected amount of the stock return attributable to it being a Fortune 500 stock
is closest to:
0.522.
0.046
0.710
0.900
Multiple regression was used to explain stock returns using the following variables:
Dependent variable:
RET = annual stock returns (%)
Independent variables:
MKT = Market capitalization = Market capitalization / $1.0 million
IND = Industry quartile ranking (IND = 4 is the highest ranking)
FORT = Fortune 500 firm, where {FORT = 1 if the stock is that of a Fortune 500
firm, FORT = 0 if not a Fortune 500 stock}
The regression results are presented in the tables below
The expected return on the stock of a firm that is not in the Fortune 500, has a
market capitalization of $5 million, and is in an industry with a rank of 3 is closest to:
2.88%.
3.98%.
1.42%.
2.10%
Multiple regression was used to explain stock returns using the following variables:
Dependent variable:
RET = annual stock returns (%)
Independent variables:
MKT = Market capitalization = Market capitalization / $1.0 million
IND = Industry quartile ranking (IND = 4 is the highest ranking)
FORT = Fortune 500 firm, where {FORT = 1 if the stock is that of a Fortune 500
firm, FORT = 0 if not a Fortune 500 stock}
The regression results are presented in the tables below
Does being a Fortune 500 stock contribute significantly to stock returns?
Yes, at a 10% level of significance.
Yes, at a 5% level of significance.
No, not at a reasonable level of significance.
No, not at a 15% level of significance.
Multiple regression was used to explain stock returns using the following variables:
Dependent variable:
RET = annual stock returns (%)
Independent variables:
MKT = Market capitalization = Market capitalization / $1.0 million
IND = Industry quartile ranking (IND = 4 is the highest ranking)
FORT = Fortune 500 firm, where {FORT = 1 if the stock is that of a Fortune 500
firm, FORT = 0 if not a Fortune 500 stock}
The regression results are presented in the tables below
The p-value of the Breusch-Pagan test is 0.0005. The lower and upper limits for the
Durbin-Watson test are 0.40 and 1.90, respectively. Based on this data and the
information in the tables, there is evidence of:
only multicollinearity.
only serial correlation.
serial correlation and heteroskedasticity.
only heteroskedasticity.
Multiple regression was used to explain stock returns using the following variables:
Dependent variable:
RET = annual stock returns (%)
Independent variables:
MKT = Market capitalization = Market capitalization / $1.0 million
IND = Industry quartile ranking (IND = 4 is the highest ranking)
FORT = Fortune 500 firm, where {FORT = 1 if the stock is that of a Fortune 500
firm, FORT = 0 if not a Fortune 500 stock}
The regression results are presented in the tables below
Ron Working incorrectly uses the standard error of estimate instead of the standard
error of the forecast in his calculation of the confidence interval for the predicted
value from a simple linear regression with 26 observations. All else equal, the
confidence interval he calculates will be: (10%)
the same as the correct confidence interval.
wider than the correct confidence interval.
narrower than the correct confidence interval.
wider than the correct confidence interval only if the R2 is greater than 0.50.
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