Gary Hansen is a securities analyst for a mutual fund specializing in small-capitalization growth stocks. The fund regularly invests in initial public offerings (IPOs). If the fund subscribes to an offer, it is allocated shares at the offer price. Hansen notes that IPOs frequently are underpriced, and the price rises when open market trading begins. The initial return for an IPO is calculated as the change in price on the first day of trading divided by the offer price. Hansen is developing a regression model to predict the initial return for IPOs. Based on past research, he selects the following independent variables to predict IPO initial returns: Underwriter rank = 110, where 10 is highest rank Pre-offer price adjustmenta = (Offer price Initial filing price)/Initial filing price Offer size ($ millions) = Shares sold Offer price Fraction retaineda = Fraction of total company shares retained by insiders aExpressed as a decimal Hansen collects a sample of 1,725 recent IPOs for his regression model. Regression results appear in Exhibit 1, and ANOVA results appear in Exhibit 2. Exhibit 1 Hansens Regression Results Dependent Variable: IPO Initial Return (Expressed in Decimal Form, i.e., 1% = 0.01) Variable Coefficient (bj ) Standard Error t-Statistic Intercept 0.0477 0.0019 25.11 Underwriter rank 0.0150 0.0049 3.06 Pre-offer price adjustment 0.4350 0.0202 21.53 Offer size 0.0009 0.0011 0.82 Fraction retained 0.0500 0.0260 1.92 Exhibit 2 Selected ANOVA Results for Hansens Regression Degrees of Freedom (df) Sum of Squares (SS) Regression 4 51.433 Residual 1,720 91.436 Total 1,724 142.869 Multiple R-squared = 0.36 Hansen wants to use the regression results to predict the initial return for an upcoming IPO. The upcoming IPO has the following characteristics: underwriter rank = 6; pre-offer price adjustment = 0.04;