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A researcher estimates a model of CEO salary. She regresses this variable on CEO tenure and CEO tenure squared. The coefficient on those two variables are 20.29 and -1.14, respectively. Calculate the value of CEO tenure at which the impact of this variable on CEO salary is optimised..
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- Consider the housing construction industry. Assume the industry is perfectly competitive in input and output markets. Suppose that, through collective bargaining, a labor union successfully negotiates an industry-wide wage for various kinds of labor (electricians, plumbers, and so on). In particular, it succeeds in negotiating a wage increase for carpenters from $9 to $12 per hour. The following graph shows the demand for labor and the supply of labor for the entire industry. On the following graph, use the purple points (diamond symbol) to plot the new industry supply curve. Then use the black point (plus symbol) to show the effect of the union negotiations on the quantity of labor demanded in the industry. WAGE RATE 18 15 N 12 6 3 0 0 5 10 15 20 QUANTITY OF LABOR Supply Demand 25 30 Demand Supply ++ Quantity Negotiations ♦ New supply of labor Quantity Advertising ?Suppose researchers use observational data to study the effect of class size and teacher experience on student score. Which of the following is a confounding variable? Student score 0000 Family income Class size Teacher experiencedoes the simple regression of log (price) on log (nox) produce an upward or a downward biased estimator of β1?
- QUESTION 7 Which is NOT true about the coefficient of determination? As you add more variables, the R-square generally rises. As you add more variables, the adjusted R-square can fall. If the R-square is above 50%, the regression is considered significant. The R-square gives the percent of the variation in the dependent variable that is explained by the independent variables. The higher is the R-square, the better is the fit.Ypsilanti Market Research conducted a survey to find out whether people who earn more money purchase more expensive goods. The following graph indicates the relationship between income the survey subjects earned and the price of the car that they purchased. PRICE (Thousands of dollars per car) 50 45 40 5 0 0 10 20 30 40 50 60 80 70 INCOME (Thousands of dollars per year) 90 100Ypsilanti Market Research conducted a survey to find out whether people who earn more money purchase more expensive goods. The following graph indicates the relationship between income the survey subjects earned and the price of the home that they purchased. PRICE (Thousands of dollars per home) 500 450 400 350 300 250 200 150 100 50 L 0 10 20 30 40 50 60 70 80 INCOME (Thousands of dollars per year) 90 100
- 13. Collinearity in a multiple regression analysis Suppose you want to examine the effects of a training program on future earnings using the following model: earn98= 4.64 +2.376train +0.371earn96 +0.366educ- 1.86 age +2.534 married (1.14) (0.43) (0.016) (0.062) (0.013) (0.4) where earn 98- 1998 earnings, in thousands of dollars train -1 if the individual participated in the training program, and =0 otherwise earn 96- 1996 earnings, in thousands of dollars educ years of education age = age, in years married-1 if the individual is married, and -0 otherwise Suppose that there is a high degree of correlation (but not perfect) between earnings in 1996, education, age, and marital status. True or False: We should be concerned about this high degree of correlation because it affects our ability to reliably estimate the impact of the training program on 1998 earnings, T. True FalseConsider the following regression estimates (ID6) Source Model Residual Total wage educ exper exper2 _cons inverse u-shaped SS C. linear d. exponential 1927.87673 5232.53756 7160.41429 df -.0046123 -3.96489 642.625576 3 522 10.0240183 525 Coef. Std. Err. .5953429 .0530251 .268287 .0368969 .000822 .7521526 MS 13.6388844 Number of obs F(3, 522) Prob > F R-squared Adj R-squared Root MSE t P>|t| 0.000 11.23 7.27 0.000 -5.61 0.000 -5.27 0.000 = .4911741 .1958023 -.006227 -5.442508 = = = 526 64.11 0.0000 0.2692 0.2650 3.1661 [95% Conf. Interval] .6995118 .3407717 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper). According to these estimates, what is the functional form of the relationship between experience and predicted wage when we hold education constant? a. u-shaped b.i -.0029975 -2.487272Compute the R² using the following information. What does this number mean? Wage = 4.43 +0.61Edu + e n = 1089, SST = 37211.6, SSE 37211.6, SSE 19848.2 R² = = = 0.4666. 46.66% of the variation in wage can be explained by education. R² = 0.5334. 53.34% of the variation in wage can be explained by education. R² = 0.61.61% of the variation in wage can be - explained by education. R² = 0.3043. 30.43% of the variation in wage can be explained by education.
- 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?6. A study analyzing college faculty salaries obtained the results shown below. The dependent variable is academic year salary in dollars. Independent variables Intercept Number of textbooks published Number of articles published Number of dissertations supervised Rating of public service Number of university committees served on Years of experience Index of teacher ratings Coefficient 21,155 23,568 586 489 389 156 2,189 530 -9,293 -6,410 21,919 Dummy var. for being an English professor Dummy var. for being female Dummy var. for having a Ph.D. R-squared = 0.68, n = 596 a) Precisely, interpret what the articles published coefficient tells us. c) The R² is 0.68. Precisely, what does this mean? T-statistic 2.35 7.21 5.37 66.85 5.65 10.02 126.92 0.01 18.75 0.80 10.01 b) Do these results support the hypothesis that there is gender discrimination in the academic community? Briefly, why or why not?Interpret the coefficients and calculate the price elasticity of soft drink demand.