Questions 1-4 are based on the following scenario: 700 income-earning individuals from a district were randomly selected and asked whether they were employed by the government (Gov = 1) or failed their entrance test (Gov = 0); data were also collected on their gender (Male = 1) if male and = 0 if female) and their years of schooling (Schooling, in years). The following table summarizes several estimated models. (Standard errors are in parentheses). Dependent Variable: Gov Linear Linear Probit Logit Probability Probit Logit Probability Probit (1) (2) (3) (4) (5) (6) (7) Schooling 0.272 0.551 0.035 0.548 (0.029) (0.062) (0.003) (0.091) -0.242 (0.125) --0.455 (0.234) -0.050 (0.025) 4.352 (1.291) Male Male x Schooling -344 (0.096) -8.146 (0.800) -0.172 (0.027) -1.027 (0.098) -1.717 (0.179) 0.152 (0.021) -7.702 (1.238) Constant -4.107 (0.358) Choose the correct statement: O a. The large difference between the estimates in column (1) and the ones in column (2) implies that predicted probabilities would be highly sensitive to whether the Logit model is employed or the Probit model is employed. O b. Column (1) suggests that the government would hire a job candidate with 16 years of education with 24.5% probability, i.e., 0.245 = 0.272 X 16 - 4.107. O C. Column (2) suggests that the government would hire a job candidate with 16 years of education with 67.0% probability, i.e., 0.670 = 0.551 X 16 - 8.146. O d. Column (3) suggests that the government would hire a job candidate with 16 years of education with 38.8% probability, i.e., 0.388 = 0.035 X 16 - 0.172. O e. Column (4) suggests that the government would not hire anyone because the estimated coefficients are all negative.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
![QUESTION 1
Questions 1-4 are based on the following scenario:
700 income-earning individuals from a district were randomly selected and asked whether they were employed by the government (Gov = 1) or failed their entrance test (Gov = 0); data were also collected on
their gender (Male = 1) if male and = 0 if female) and their years of schooling (Schooling, in years). The following table summarizes several estimated models. (Standard errors are in parentheses).
Dependent Variable: Gov
Linear
Linear
Probit
Logit
Probability
Probit
Logit
Probability
Probit
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Schooling
0.272
0.551
0.035
0.548
(0.029)
(0.062)
(0.003)
(0.091)
Male
-0.242
-0.455
-0.050
4.352
(0.125)
(0.234)
(0.025)
(1.291)
Male x Schooling
-344
(0.096)
-4.107
(0.358)
Constant
-8.146
-0.172
-1.027
-1.717
0.152
-7.702
(0.800)
(0.027)
(0.098)
(0.179)
(0.021)
(1.238)
Choose the correct statement:
O a. The large difference between the estimates in column (1) and the ones in column (2) implies that predicted probabilities would be highly sensitive to whether the Logit model is employed or the Probit
model is employed.
O b. Column (1) suggests that the government would hire a job candidate with 16 years of education with 24.5% probability, i.e., 0.245 = 0.272 X 16 - 4.107.
O C. Column (2) suggests that the government would hire a job candidate with 16 years of education with 67.0% probability, i.e., 0.670 = 0.551 X 16 - 8.146.
O d. Column (3) suggests that the government would hire a job candidate with 16 years of education with 38.8% probability, i.e., 0.388 = 0.035 X 16 - 0.172.
O e. Column (4) suggests that the government would not hire anyone because the estimated coefficients are all negative.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F7aea5f17-4d8f-475f-a835-e28ffd3d801f%2F84efd5d9-6412-4361-94a2-b9918e76c858%2Fpzua1h_processed.png&w=3840&q=75)
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