This problem is inspired by a study of the "gender gap" in earnings in top corporate jobs [Bertrand and Hallock (2001)]. The study compares total compensation among topexecutives in a large set of U.S. public corporations in the 1990s. (Each year these publicly traded corporations must report total compensation levels for their top fiveexecutives.)Let Female be an indicator variable that is equal to 1 for females and 0 for males A regression of the logarithm of earnings onto Female yieldsIn(Earnings) = 6.42 -0 45Female, SER=2.87(0 01) (0.05)Calculate the average hourly earnings for top male and female executives.The hourly earnings for top male executives is $ per hour. (Round your response to two decimal places.)The hourly earnings for top female executives is $per hour. (Round your response to two decimal places)What is the estimated average difference between earnings of top male executives and top female executives?The estimated average difference between earnings of top male executives and top female executives is $ per hour. (Round your response to two decimal places.)What is the estimator of the standard deviation of the regression error?The estimator of the standard deviation of the regression error is(Round your response to two decimal places)Calculate the t-statistic for FemaleThe t-statistic for Female is (Round your response to two decimal places.)Looking at the t-statistic, does this regression suggest that female top executives earn less than top male executives?O A. No.O B. Yes.Does this imply that there is gender discrimination?O A. No.O B. Yes.Two new variables, the market value of the firm (a measure of firm size, in millions of dollars) and stock return (a measure of firm performance, in percentage points), areadded to the regressionIn(Earnings) = 3.86 - 0.28Female +0.37In(MarketValue) + 0.004Return,(0.03) (0.04)(0.004)(0.003) Does this imply that there is gender discrimination?O A. No.O B. Yes.Two new variables, the market value of the firm (a measure of firm size, in millions of dollars) and stock return (a measure of firm performance, in percentage points), areadded to the regression:in(Earnings) = 3.86 - 0.28Female +0.37In(MarketValue) +0.004Return,(0.003)(0.03) (0.04)(0.004)hen= 46,670, R = 0.345If MarketValue increases by 3.68%, what is the increase in earnings?otIf MarketValue increases by 3.68%, earnings increase by(Round your response to two decimal places.)The coefficient on Female is now - 0.28. Why has it changed from the first regression?O A. The first regression suffered from omitted variable bias.O B. Female is correlated with the two new included variablesOC. MarketValue is important for explaining In(Eamings).O D. All of the above.Assume that the coefficient estimated in the second regression is correct. Forget about the effect of the Return variable, whose effect seems small and statisticallyinsignificant. Calculate the correlation between Female and In(MarketValue) using the omitted variable bias equation.47ot Let X = Female, u = MarketValue, and= 0.46.The correlation between Female and In(MarketValue), pxu, is (Round your response to three decimal places.)Are large firms more likely to have female top executives than small firms?O A. There is no relationship between the genders,O B. NoO C. Yes

Hello! As you have posted more than 3 sub parts, we are answering the first 3 sub-parts. In case…

Answered: This problem is inspired by a study of…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

5. Management of a home appliance store wants tounderstand the growth pattern of the monthly sales of a new technology device over the past two years.The managers have recorded the relevant data inthe file P13_05.xlsx. Have the sales of this devicebeen growing linearly over the past 24 months? Byexamining the results of a linear trend line, explainwhy or why not.
4
The index of leading economic indicators, compiled and published by the U.S. National Bureau of Economic Research, is composed of 12 time series, such as the average work hours of production in manufacturing, manufacturers’ new orders, and money supply. This index and similar indexes are designed to move up or down before the economy begins to move the same way. Thus, an economist has statistical evidence to forecast future trends.You want to construct a leading indicator for Erie County in upstate New York. The index is to be based on 2000 data. Because of the time and work involved, you decide to use only four time series. As an experiment, you select these four series: unemployment in the county, a composite index of county stock prices, the County Price Index, and retail sales. Here are the figures for 2000 and 2018. The weights you assign are unemployment rate 20%, stock prices 40%, County Price Index 25%, and retail sales 15%. (Round your answers to 1 decimal place.) a. Using…
The following table contains yearly data on the price of beef from 2022 to 2024: Year Price of Beef ($/Kg) 3 5 8 2022 2023 2024 Forecast the price of chicken for 2025 using the following methods: 1. A 3-Year moving average method; 2. A simple exponential smoothing method (use a=0.5); 8. A simple linear trend model.
.A new, miracle diabetes drug that diminishes major symptoms of diabetes has been approved by the FDA. Health care professionals and researchers believe that the new drug will prolong lifespan of diabetes patients. If the population is in steady state and the incidence is constant, what will the effect of this new drug be on the prevalence of diabetes in the population? Explain.
Two new variables, the market value of the firm (a measure of firm size, in millions of dollars) and stock return (a measure of firm performance, in percentage points), are added to the regression: In(Earnings) = 3.86 – 0.28Female + 0.37In(MarketValue) + 0.004Return, (0.03) (0.04) (0.004) (0.003) n = 46,670, R = 0.345. If MarketValue increases by 1.88%, what is the increase in earnings? If MarketValue increases by 1.88%, earnings increase by 0.70 % (Round your response to two decimal places.) The coefficient on Female is now – 0.28. Why has it changed from the first regression? O A. Female is correlated with the two new included variables. O B. MarketValue is important for explaining In(Earnings). O C. The first regression suffered from omitted variable bias. OD. All of the above. Assume that the coefficient estimated in the second regression is correct. Forget about the effect of the Return variable, whose effect seems small and statistically insignificant. Calculate the correlation…
Product failure behavior. An article in Hotwire (Dec. 2002) discussed the length of time till failure of a product pro- duced at Hewlett-Packard. At the end of the product’s life- time, the time till failure is modeled using an exponential distribution with mean 500 thousand hours. In reliability jargon, this is known as the “wear-out” distribution for the product. During its normal (useful) life, assume the prod- uct’s time till failure is uniformly distributed over the range 100 thousand to 1 million hours. a. At the end of the product’s lifetime, find the probability that the product fails before 700 thousand hours. b. During its normal (useful) life, find the probability that the product fails before 700 thousand hours. c. Show that the probability of the product failing before 830 thousand hours is approximately the same for both the normal (useful) life distribution and the wear-out distribution
13. In the regression equation Log(Cars_Sold) = 1500 + 2.3*Advertisement, what is the most appropriate interpretation of the slope coefficient? OA. a. $1 increase in advertisement, increases cars sold by 230% O B. b. $1 increase in advertisement increases cars sold by 2.3 OC. c. 1% increase in advertisement increases cars sold by 0.023 OD. d. 1% increase in advertisement increases cars sold by 230%
1. It has been noted that there is a positive correlation between the U.S. economy and the height of women's hemlines (distance from the floor of the bottom of a skirt or dress) with shorter skirts corresponding to economic growth and lower hemlines to periods of economic recession. Comment on the conclusion that economic factors cause hemlines to rise and fall. # of Absences (in weeks) Exam Grade 1 2 345 23 014 90 85 95 92 80
9. A wildlife researcher is interested in predicting a mammal’s lifespan (in years) based on its average number of hours of sleep per night. Data was obtained from a large random sample of mammals, and the regression equation turned out as follows: Predicted lifespan = 36.93 – 1.64 (average hours of sleep) Which one of the following statements is a correct interpretation of this equation? 1. Approximately 1.64% of the variability in lifespan can be explained by the regression equation. 2. As lifespan increases by one year, average hours of sleep is predicted to decrease by 1.64 hours. 3. As lifespan increases by one year, average hours of sleep is predicted to increase by 36.93 hours. 4. As average hours of sleep increases by one hour, lifespan is predicted to increase by 36.93 years. 5. As average hours of sleep increases by one hour, lifespan is predicted to decrease by 1.64 years.
In a study investigating maternal risk factors for congenital syphilis, syphilis is treated as a binary outcome variable, where 1 represents the presence of disease in a newborn and 0 represents absence of disease. The estimated coefficients from a logistic regression model containing the predictors cocaine or crack use, marital status, number of prenatal visits to a doctor, alcohol use and level of education are included in the table below. The estimated intercept is not included in the table. a. As an expectant mother’s number of prenatal visits to the doctor increases, does the probability that her child will be born with congenital syphilis increase or decrease?b. Marital status is a binary variable, where 1 indicates that a woman is unmarried and 0 indicates that she is married. What are the estimated relative odds that a newborn will suffer from syphilis for unmarried versus married mothers after holding the other variables in the model constant?c. Cocaine or crack use is also a…

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS