Given a text T, and several options, according to the question posed in the text T to choose the most appropriate option from the options as the answer, please directly output the most appropriate option, do not need to output the explanation.

Doris Honoré is a securities analyst with a large wealth management firm. She and her colleague Bill Smith are addressing three research topics: how investment fund characteristics affect fund total returns, whether a fund rating system helps predict fund returns, and whether stock and bond market returns explain the returns of a portfolio of utility shares run by the firm.To explore the first topic, Honoré decides to study US mutual funds using a sample of 555 large-cap US equity funds. The sample includes funds in style classes of value, growth, and blend (i.e., combining value and growth characteristics). The dependent variable is the average annualized rate of return (in percent) over the past five years. The independent variables are fund expense ratio, portfolio turnover, the natural logarithm of fund size, fund age, and three dummy variables. The multiple manager dummy variable has a value of 1 if the fund has multiple managers (and a value of 0 if it has a single manager). The fund style is indicated by a growth dummy (value of 1 for growth funds and 0 otherwise) and a blend dummy (value of 1 for blend funds and 0 otherwise). If the growth and blend dummies are both zero, the fund is a value fund. The regression output is given in Exhibit 1.
| Exhibit 1Multiple Regression Output for Large-Cap Mutual Fund Sample |
| Coefficient | Standard Error | t-Statistic |
| Intercept | 10.9375 | 1.3578 | 8.0551 |
| Expense ratio(%) | -1.4839 | 0.2282 | -6.5039 |
| Portfolio turnover(%) | 0.0017 | 0.0016 | 1.0777 |
| In(fund size in$) | 0.1467 | 0.0612 | 2.3976 |
| Manager tenure(years) | -0.0098 | 0.0102 | -0.9580 |
| Multiple manager dummy | 0.0628 | 0.1533 | 0.4100 |
| Fund age(years) | -0.0123 | 0.0047 | -2.6279 |
| Growth dummy | 2.4368 | 0.1886 | 12.9185 |
| Blend dummy | 0.5757 | 0.1881 | 3.0611 |

| ANOVA | df | SS | MSS |
| Regression | 8 | 714.169 | 89.2712 |
| Residual | 546 | 1583.113 | 2.8995 |
| Total | 554 | 2297.282 |
| Multiple R | 0.5576 |
| R2 | 0.3109 |
| Adjusted R 2 | 0.3008 |
| Standard error(%) | 1.7028 |
| Observations | 555 |


Based on the results shown in Exhibit 1, Honoré wants to test the hypothesis that all of the regression coefficients are equal to zero. For the 555 fund sample, she also wants to compare the performance of growth funds with the value funds.Honoré is concerned about the possible presence of multicollinearity in the regression. She states that adding a new independent variable that is highly correlated with one or more independent variables already in the regression model, has three potential consequences:1. The R2 is expected to decline.2. The regression coefficient estimates can become imprecise and unreliable.3. The standard errors for some or all of the regression coefficients will become inflated.Another concern for the regression model (in Exhibit 1) is conditional heteroskedasticity. Honoré is concerned that the presence of heteroskedasticity can cause both the F-test for the overall significance of the regression and the t-tests for significance of individual regression coefficients to be unreliable. She runs a regression of the squared residuals from the model in Exhibit 1 on the eight independent variables and finds the R2 is 0.0669.As a second research project, Honoré wants to test whether including Morningstar’s rating system, which assigns a one- through five-star rating to a fund, as an independent variable will improve the predictive power of the regression model. To do this, she needs to examine whether values of the independent variables in a given period predict fund return in the next period. Smith suggests three different methods of adding the Morningstar ratings to the model:Method 1: Add an independent variable that has a value equal to the number of stars in the rating of each fund.Method 2: Add five dummy variables, one for each rating.Method 3: Add dummy variables for four of the five ratings.As a third research project, Honoré wants to establish whether bond market returns (proxied by returns of long-term US Treasuries) and stock market returns (proxied by returns of the S&P 500 Index) explain the returns of a portfolio of utility stocks being recommended to clients. Exhibit 2 presents the results of a regression of 10 years of monthly percentage total returns for the utility portfolio on monthly total returns for US Treasuries and the S&P 500.
| Exhibit 2.Regression Analysis of Utility Portfolio Returns |
| Coefficient | Standard | t-Statistic | p-Value |
| Error |
| Intercept | -0.0851 | 0.2829 | -0.3008 | 0.7641 |
| US Treasury | 0.4194 | 0.0848 | 49474 | Smith asks whether Honoré should have estimated the models in Exhibit 1 and Exhibit 2 using a probit or logit model instead of using a traditional regression analysis.
Based on her estimated Durbin-Watson statistic, Honoré should:
Option A:fail to reject the null hypothesis.
Option B:reject the null hypothesis because there is significant positive serial correlation.
Option C:reject the null hypothesis because there is significant negative serial correlation.

Answer: A