本帖最后由 ww07ty 于 2014-5-15 23:06 编辑
Exhibit 1 Garfield’s First Regression Model Summary Output Regression of HighTech Returns on NASDAQ Index Returns, 2005–2009
Regression Statistics | | | | Multiple R | 0.737399823 | | | R-squared | 0.543758499 | | | Standard error of estimate | | | | Observations | 60 | | | | ANOVA | Degrees of Freedom (DF) | Sum of Squares (SS) | Mean Square (MS) | Regression | 1 | 0.214743645 | 0.214743645 | Residual | 58 | 0.180181024 | 0.003106569 | Total | 59 | 0.394924669 | | | | Coefficient | Standard Error | p-value | Intercept | 0.001795002 | 0.007209589 | 0.804260285 | NASDAQ return | 1.086005661 | 0.130620835 | 0.000000000 |
Garfield presents the regression results to the investment committee with the following three conclusions: 1.
The regression intercept is statistically significant. 2.
The model explains more than half of the variation in HighTech’s returns. 3.
The NASDAQ index return and the HighTech return are positively correlated.
Which of Garfield’s conclusions to the investment committee about the findings from his first model (Exhibit 1) is least likely correct? Conclusion:
The p-value of 0.80 for the intercept implies that there is about an 80% chance that the true value of the intercept is not significantly different from zero. Thus, Conclusion 1 is incorrect.
求助：官方给的答案自己没看明白，一直没搞懂P value是什么意思 还有为什么0.8就not significantly from zero呢 希望大家帮帮我 谢了 | 
发表于 2014-5-15 23:03
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