How should I write a regression that models the wage as a function of: 1. S...
How should I write a regression that models the wage as a
function of:
1. S - schooling
2. EXP - years of experience where the marginal effect of
experience may have diminishing returns on the wage.
3. MALE - gender
4. Race - 3 categories: ETHWHITE, ETHBLAC, ETHHISP.
5. Region - 4 categories: REGNE, REGNC, REGS, REGW
A. WAGE = b1 + b2S +b3EXP + b4EXP2 +b5ETHBLAC + b6ETHHISP + b7ETHWHITE + b8REGNE + b9REGW + b10REGNC +b11REGS
B. WAGE = b1 + b2S +b3EXP + b4EXP2 +b5ETHBLAC + b6ETHHISP + b7REGS + b8REGNE + b9REGW
C. WAGE = b1 + b2S +b3EXP + b4EXP2 +b5ETHBLAC + b6ETHHISP + b7REGS + b8REGNE + b9REGW + b10REGS
D. WAGE = b1 + b2S +b3EXP +b5ETHBLAC + b6ETHHISP + b7REGS + b8REGNE + b9REGW
I believe that the answer is B, but would like some clarification as to why
Homework Answers
S is a continuous variable, it'll have one slope coefficient B2.
As experience has diminishing effect, it will be best represented with a polynomial of power 2 (EXP2).
The categorical variables will have coefficients each which take values 0 or 1.
All of these are satisfied in option B. Therefore, B is the correct option.
Add Answer to:
How should I write a regression that models the wage as a
function of:
1. S...
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0....
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0.132) (0.009) (0.005) (0.196) n-526 R-square With all the variables described as follows: logiwage)-log of average hourly wage: female is a dummy variable equal to 1 if the observed person is a female, and 0 if male; edu female is an interaction variable equal to education 'female; edu is the number of years of schooling exp is the number...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ (1), where wage is hourly wage, measured in dollars, and educ years of formal education. According to (1), a person with no education has a predicted hourly wage of [wagehat] dollars. (NOTE: Write your answer in number format, with 2 decimal places of precision level; do not write your answer as a fraction. Add a leading minus sign symbol, a leading zero and trailing...
in studying the effect of work experience and gender on wage,the equations below were estimated.They have...
in studying the effect of work experience and gender on wage,the equations below were estimated.They have individual wage wagei dependent variable, and work experience experi and a dummy variable womani [ indicating the gender of individual i ] wagei=0+1 experi+2 womani +ui wagei=0+1experi+2womani +3womani * experi+ui a. How should the parameters 0,1,2 and 3 in the models be interpreted? b. What is the marginal effect of experience in each model? c. How does the second equation relate to estimating the...
Question 4 We will look at the possible effects of gender of an individual on educationol attainment. In the dataset is S years of schooling, ASVABC is composite score on the cognitive tests, SM is y...
Question 4 We will look at the possible effects of gender of an individual on educationol attainment. In the dataset is S years of schooling, ASVABC is composite score on the cognitive tests, SM is years of schooling of the respondent's mother, SF is years of schooling of the respondent's father, MALE is a dummy variable equal to 1 if the respondent was a male Some of the regression output has been deliberately hidden. Source I df MS Number of...
its 8.17 the one that is highlighted and I have also attached the models. Xi2: 0 1 0 a. Explain how each regression coefficient in model (8.33) is interpreted hene. b. Fit the regression model...
its 8.17 the one that is highlighted and I have also
attached the models.
Xi2: 0 1 0 a. Explain how each regression coefficient in model (8.33) is interpreted hene. b. Fit the regression model and state the estimated regression function. c. Test whether the X2 variable can be dropped from the regression model; use α 01 St ate the alternatives, decision rule, and conclusion. d. Obtain the residuals for regression model (8.33) and plot them against XiXz. Is there...
Question 1 First run the regression: EARNINGSi = β1 + β2ASVABCi + β3Si + ui Then...
Question 1
First run the regression:
EARNINGSi = β1 + β2ASVABCi + β3Si + ui
Then run the regression with experience:
EARNINGSi = β1 + β2ASVABCi + β3Si + β4EXPi + ui
Compare the results from these two regressions, do you get an
indication that the previous estimate of schooling without EXP was
biased? If so, in which direction? And why is that?
Question 2
Add gender dummy variable to the regression (the one running
regression of EARNINGS on ASVABC,...
3) Consider the following linear regression: y =a + Bx + Show that minimizing the sum...
3) Consider the following linear regression: y =a + Bx + Show that minimizing the sum of squared residuals ( - ) to obtain OLS estimators of the slope and the intercept results in the following algebraic properties a) b) Ex = 0 = 0 4) You run the following regression: TestScore = a + (Female) + where TestScore is measured on a scale from 400 to 1000, and female is an indicator for the gender of the student. You...
1. Which of the following conditions will lead to a smaller variance for the intercept estimator...
1. Which of the following conditions will lead to a smaller variance for the intercept estimator for your multiple regression model? (A) X values cluster far from the origin of the X axis (B) X values closely pack around the mean of X in your sample (C) Small sample sizes (D) High correlation among the explanatory variables (E) Small error variance in the population regression function 2. R-squared (A) measures the proportion of variability of the dependent variable that is...
1. For each of the following regression models, write down the X matrix and 3 vector....
1. For each of the following regression models, write down the X matrix and 3 vector. Assume in both cases that there are four observations (a) Y BoB1X1 + B2X1X2 (b) log Y Bo B1XiB2X2+ 2. For each of the following regression models, write down the X matrix and vector. Assume in both cases that there are five observations. (a) YB1XB2X2+BXE (b) VYBoB, X,a +2 log10 X2+E regression model never reduces R2, why 3. If adding predictor variables to a...
what type of discrimination is this and what laws does it violate Topic: Compare the wage...
what type of discrimination is this and what laws does it violate Topic: Compare the wage gap between the sexes by adding a layer of race to the complex issues that face women when entering the workforce. The objective I’m hoping to accomplish during this case is to show how discrimination based on sex and race affect women while highlighting many issues that women face in the workforce. The topic I want to investigate is a wage gap on a...
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0.132) (0.009) (0.005) (0.196) n-526 R-square With all the variables described as follows: logiwage)-log of average hourly wage: female is a dummy variable equal to 1 if the observed person is a female, and 0 if male; edu female is an interaction variable equal to education 'female; edu is the number of years of schooling exp is the number...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ (1), where wage is hourly wage, measured in dollars, and educ years of formal education. According to (1), a person with no education has a predicted hourly wage of [wagehat] dollars. (NOTE: Write your answer in number format, with 2 decimal places of precision level; do not write your answer as a fraction. Add a leading minus sign symbol, a leading zero and trailing...
Question 4 We will look at the possible effects of gender of an individual on educationol attainment. In the dataset is S years of schooling, ASVABC is composite score on the cognitive tests, SM is years of schooling of the respondent's mother, SF is years of schooling of the respondent's father, MALE is a dummy variable equal to 1 if the respondent was a male Some of the regression output has been deliberately hidden. Source I df MS Number of...
its 8.17 the one that is highlighted and I have also
attached the models.
Xi2: 0 1 0 a. Explain how each regression coefficient in model (8.33) is interpreted hene. b. Fit the regression model and state the estimated regression function. c. Test whether the X2 variable can be dropped from the regression model; use α 01 St ate the alternatives, decision rule, and conclusion. d. Obtain the residuals for regression model (8.33) and plot them against XiXz. Is there...
Question 1
First run the regression:
EARNINGSi = β1 + β2ASVABCi + β3Si + ui
Then run the regression with experience:
EARNINGSi = β1 + β2ASVABCi + β3Si + β4EXPi + ui
Compare the results from these two regressions, do you get an
indication that the previous estimate of schooling without EXP was
biased? If so, in which direction? And why is that?
Question 2
Add gender dummy variable to the regression (the one running
regression of EARNINGS on ASVABC,...
3) Consider the following linear regression: y =a + Bx + Show that minimizing the sum of squared residuals ( - ) to obtain OLS estimators of the slope and the intercept results in the following algebraic properties a) b) Ex = 0 = 0 4) You run the following regression: TestScore = a + (Female) + where TestScore is measured on a scale from 400 to 1000, and female is an indicator for the gender of the student. You...
1. For each of the following regression models, write down the X matrix and 3 vector. Assume in both cases that there are four observations (a) Y BoB1X1 + B2X1X2 (b) log Y Bo B1XiB2X2+ 2. For each of the following regression models, write down the X matrix and vector. Assume in both cases that there are five observations. (a) YB1XB2X2+BXE (b) VYBoB, X,a +2 log10 X2+E regression model never reduces R2, why 3. If adding predictor variables to a...
Most questions answered within 3 hours.
-
Where is the error in this code sequence?
String s1 = "Hello";
String s2 = "ello";...
asked 10 months ago -
Financial data for Joel de Paris, Inc., for last year
follow:
Joel de Paris, Inc.
Balance...
asked 10 months ago -
Consider this reaction:
Al2(SO4)3 (aq)+ BaCl3
(aq) Al2Cl6 (aq)- +
3BaSO4(s) . What is the...
asked 10 months ago -
Suppose that Savneet is considering increasing her
recent random sample from 20 car rentals to 40...
asked 10 months ago -
Trucks arrive at an unloading terminal at an average rate of 120
per hour.
Trucks arrive...
asked 10 months ago -
Why are methanol and ethanol completely soluble in water while
octanol is not very little soluble....
asked 10 months ago -
A facilities manager at a university reads in a research report
that the mean amount of...
asked 10 months ago -
When the CuSO4 is rehydrated by adding water to the anhydrous
compound, is this an endothermic...
asked 10 months ago -
A ray of sunlight is passing from diamond into crown glass; the
angle of incidence is...
asked 10 months ago -
A block of mass 0.249 kg is placed on top of a light, vertical
spring of...
asked 10 months ago -
how do the kidneys compensate in the presences of acidosis
a) trigger hyperventilate
b) reserve acid...
asked 10 months ago -
Question 501 pts
The rental rate of capital to the firm increases. Which of the
following...
asked 10 months ago