Answer-first summary for fast verification
Answer: 0.6330 + 0.0840(MCP) + 0.5101(SEF) + 0.7(FMR)
## Explanation The correct regression equation is constructed using the **coefficient** values from the table, not the standard errors. The standard errors are used for hypothesis testing and constructing confidence intervals. From the table: - **Intercept coefficient** = 0.6330 - **MCP coefficient** = 0.0840 - **SEF coefficient** = 0.5101 - **FMR coefficient** = 0.7000 Therefore, the regression equation is: **ASR = 0.6330 + 0.0840(MCP) + 0.5101(SEF) + 0.7000(FMR)** This corresponds to option B. **Why the other options are incorrect:** - **Option A**: Missing the intercept term (0.6330) - **Option C**: Uses the standard error of the intercept (1.11) instead of the coefficient (0.6330) - **Option D**: Uses all standard errors instead of coefficients In regression analysis, the coefficients represent the estimated effect of each independent variable on the dependent variable, while standard errors measure the precision of these estimates.
Author: Nikitesh Somanthe
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An analyst uses the following regression model to explain stock returns:
Dependent variable:
ASR = Annual stock returns (%)
Independent variables:
MCP = Market capitalization (divided by $1 million to simplify modeling)
SEF = Stock exchange firm, where SEF = 1 if the stock is that of a firm listed on the New York Stock Exchange and SEF = 0 if not listed
FMR = Forbes magazine ranking (FMR = 4 is the highest ranking)
The following table presents the regression results:
| Coefficient | Standard error |
|---|---|
| Intercept | 0.6330 |
| MCP | 0.0840 |
| SEF | 0.5101 |
| FMR | 0.7000 |
Based on the results in the table above, which of the following is the correct regression equation?
A
0.0840(MCP) + 0.5101(SEF) + 0.7(FMR)
B
0.6330 + 0.0840(MCP) + 0.5101(SEF) + 0.7(FMR)
C
1.11 + 0.0840(MCP) + 0.5101(SEF) + 0.7(FMR)
D
1.11 + 0.0130(MCP) + 0.1235(SEF) + 0.3241(FMR)