问题如下图：
答案里的模型是怎么建出来的？
选项：
A. 
B. 
C. 
解释：
NO.PZ2015120204000013问题如下Baseon past research, Hansen selects the following inpennt variables to preIPO initireturns: Unrwriter rank = 1–10, where 10 is highest rankPre-offer priaustment (Expressea cimal) = (Offer pri– Initifiling price)/Initifiling priceOffer size ($ millions) = Shares sol× Offer priceFraction retaine(Expressea cimal) = Fraction of totcompany shares retaineinsirsHansen’s Regression Results pennt Variable: IPO InitiReturn (Expressein cimForm, i.e., 1% = 0.01)The upcoming IPO hthe following characteristics:l unrwriter rank = 6;l pre-offer priaustment = 0.04;l offer size = $40 million;l fraction retaine= 0.70.Baseon Hansen’s regression, the precteinitireturn for the upcoming IPO is closest to:A.0.0943.B.0.1064.C.0.1541.C is correct.The precteinitireturn (IR) is:IR = 0.0477 + (0.0150 × 6) + (0.435 × 0.04) – (0.0009 × 40) + (0.05 × 0.70) = 0.1541老师好！如题，a=1%对应检验的t值是2.58左右，为什么不用将每个变量系数的t值与它对比以判断每个自变量是否有效/显著？谢谢
NO.PZ2015120204000013 0.1064. 0.1541. C is correct. The precteinitireturn (IR) is: IR = 0.0477 + (0.0150 × 6) + (0.435 × 0.04) – (0.0009 × 40) + (0.05 × 0.70) = 0.1541同上，关于对t检验的考量，当题中出现这个条件
NO.PZ2015120204000013 如上请问题目中的40m，为什么就用40来算，而不是40000000
NO.PZ2015120204000013 算法我理解为什么答案是0.1541啊，这个式子算出来不是0.1384吗
所以上面说的一堆公式其实没啥用？我看了半天？？？
