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Consider an equation to explain salaries of CEOs in terms of annual firm
sales, return on equity (roe, in % form), return in firm stocks ( ros, in %
firm):
假設有個式子可以解釋CEO的年度銷售薪水, 回報在股票 (roe, 用%的方式表示)以及公司
配股 firm stock (ros, 用%表示):
log (salary): β0 +β1log(sales) +β2roe +β3ros+u
(1) In terms of model parameters, state the null hypothesis that, after
controlling for sales and roe, ros has no effect on CEO salary. State the
alternative that better stock market performance increases a CEO’s salary.
在變數模式中, 設定虛無假設.在控制銷售以及roe和ros之後,並不會影響CEO的薪水. 請
找出相對的好的股市表現以增加CEO的薪水
(2) using the data in CEOSAL1.RAW, the following equation was obtained by OLS
根據CEOSAL1.RAW,下列公式根據OLS成立:
log(salary)= 4.32 + 2.80 log(sales) + 0.174 roe + 0.00024 ros
( 0.32) (0.035) (0.0041) (0.00054)
n=209, R^2 =0.283
, by what percentage is salary predicted to increase if ros increases by 50
points? Does ros have a practically large effect on salary?
當ros增加50個unit,薪水的漲幅預計會是多少? 根據得出的結果,ros對薪水的影響相對比
較大嗎?
(3) Test the null hypothesis that ros has no effect on salary against the
alternative that ros has a positive effect. Carry out the test at the 10%
significance level.
試驗兩個假設: 一,若ros對CEO薪水沒有影響, 相對於此,另試驗ros對CEO 薪水有正面影
響. 試驗數據: 以10%為準
(4) Would you include ros in a final model explaining CEO compensation in
terms of firm performance? Explain.
在用最後根據公司表現解釋CEO薪水時,你會不會將ros 包括在最後模型裡?請解釋
感激不盡,兄弟初學統計,很多東西實在搞不懂!
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