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If we run a regression where y (bankruptcy) = f (factors potentially predicting bankruptcy), what is the dependent variable?
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- Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn't evidence that the advertising is impacting sales. What side would you take and why?A company wants to use regression analysis to forecast the demand for the next quarter.In such a regression model, demand would be the independent variable. True or false?a. Trueb. FalseHow do you interpret the R-squared obtained from running this regression?
- 10. Residual analysis Consider a regression of y on several independent variables, and the resulting predicted values of the dependent variable. The residual for the ith observation Consider a data set for a large sample of professional basketball players. Each observation contains the salary, as well as various performance statistics such as points, rebounds, and assists for each player. Suppose a regression of salary on all performance statistics is run, and the residuals are obtained. The player with the lowest (most negative) resid represents which of the following? (Assume the regression reasonably predicts salaries in most cases.) The most fairly paid player relative to her on-court performance The most overpaid player relative to her on-court performance The highest-paid player, regardless of her on-court performance The most underpaid player relative to her on-court performancedoes the simple regression of log (price) on log (nox) produce an upward or a downward biased estimator of β1?What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?
- What are the measures of fit that are commonly used for multiple regressions? How can an adjusted R2 take on negative values?QUESTION 1 Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. This table is used for Questions 1-6. Dependent variable: In(Price) Regressor (1) (2) (3) (4) (5) 0.00042 (0.000038) Size In(Size) 0.57 (2.03) 0.69 0.68 0.69 (0.055) (0.054) (0.087) In(Size)² 0.0078 (0.14) Bedrooms 0.0036 (0.037) Рol 0.082 0.071 0.071 0.071 0.071 (0.032) (0.034) (0.034) (0.036) (0.035) 0.037 0.027 0.026 0.027 0.027 (0.030) View (0.029) (0.028) (0.026) (0.029) Pool x View 0.0022 (0.10) 0.12 (0.035) Condition 0.13 0.12 0.12 (0.035) 0.12 (0.045) (0.035) (0.036) 6.63 (0.53) Intercept 10.97 6.60 7.02 6.60 (0.069) (0.39) (7.50) (0.40) Summary Statistics SER 0.102 0.098 0.099 0.099 0.099 R? 0.72 0.74 0.73 0.73 0.73 Variable definitions: Price = sale price ($); Size = house size (in square feet); Bedrooms = number of bedrooms; Pool = binary variable (1 if house has a swimming pool, 0…QUESTION 17 I am trying to figure out how to measure an athlete's productivity. So, I have run a linear regression of a NBA player's salary (dependent variable) on a player's statistics including average points, assists, rebounds per game, and turnovers per game (the independent variables). The final model is: Salary = 1,000,000 * Points per game + 50,000 * Assists per game + 20,000 * Rebounds per game - 30,000 * Turnovers per game %3! Last year, Lebron James averaged 25 points per game, 8 assists per game, 8 rebounds per game and 4 turnovers per game. What is Lebron's predicted salary?
- (2)What would the consequence be for a regression model if theerrors were not homoscedastic?Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married respondents who are not female?Stores commonly offer a cheaper unit price for large quantity purchases. Quantity 1 2 5 10 20 Unit Price $100.00 $80.00 $70.00 $50.00 $40.00 a. Use regression to find a logarithmic equation to model the data. Round the numbers in your equation to 2 decimal places. y = a + bln(z) with You b b. Use your equation to find an appropriate unit price for a customer who purchases 15 items. c. Use your equation to find an appropriate unit price for a customer who purchases 25 items. $