A research analyst is trying to determine whether a firm's price-earnings (PE) and price-sales (PS) ratios can explain the firm's stoch performance over the past year. A PE ratio is calculated as a firm's share price compared to the income or profit earned by the firm share. Generally, a high PE ratio suggests that investors are expecting higher earnings growth in the future compared to companie with a lower PE ratio. The PS ratio is calculated by dividing a firm's share price by the firm's revenue per share for the trailing 12 months. In short, investors can use the PS ratio to determine how much they are paying for a dollar of the firm's sales rather than a dollar of its earnings (PE ratio). In general, the lower the PS ratio, the more attractive the investment. The accompanying table shov portion of the year-to-date returns (Return in \%) and the PE and PS ratios for 30 firms. Click here for the Excel Data File a-1. Estimate: Return =b0+b1PE+b2PS+. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) a-2. Are the signs on the coefficients as expected? Yes No b. Interpret the slope coefficient of the PS ratio. As the PS ratio increases by 1 unit, the predicted return of the firm increases by 3.42%, holding PE constant. As the PS ratio decreases by 1 unit, the predicted return of the firm decreases by 33.24%, holding PE constant. As the PS ratio increases by 1 unit, the predicted return of the firm decreases by 3.42%, holding PE constant. As the PS ratio increases by 1 unit, the predicted return of the firm decreases by 33.24%, holding PE constant. c. What is the predicted return for a firm with a PE ratio of 10 and a PS ratio of 2 ? (Negative value should be indicated by a minus sign. Do not round intermediate calculations. Round final answer to 2 decimal places.) d. What is the standard error of the estimate? (Round your answer to 2 decimal places.) e. Interpret R2. 40.75% of the sample variation in y is explained by the sample regression equation. 40.75% of the sample variation in x is explained by the sample regression equation. 63.83% of the sample variation in x is explained by the sample regression equation. 36.36% of the sample variation in y is explained by the sample regression equation