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To Examine the Differences Between Salaries of Male and Female

question 66

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To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered: Salary = the monthly salary (excluding fringe benefits and bonuses) ,
Educ = the number of years of education,
Exper = the number of months of experience,
Train = the number of weeks of training,
Gender = the gender of an individual; 1 for males, and 0 for females.
Excel partial outputs corresponding to these models are available and shown below.
Model A: Salary = β0 + β1Educ + β2Exper + β3Train + β4Gender + ε To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered: Salary = the monthly salary (excluding fringe benefits and bonuses) , Educ = the number of years of education, Exper = the number of months of experience, Train = the number of weeks of training, Gender = the gender of an individual; 1 for males, and 0 for females. Excel partial outputs corresponding to these models are available and shown below. Model A: Salary = β<sub>0</sub> + β<sub>1</sub>Educ + β<sub>2</sub>Exper + β<sub>3</sub>Train + β<sub>4</sub>Gender + ε   Model B: Salary = β<sub>0</sub> + β<sub>1</sub>Educ + β<sub>2</sub>Exper + β<sub>3</sub>Gender + ε   Using Model B, what is the regression equation found by Excel for females? A)    = 4,713.26 + 139.5366Educ + 3.3488Exper + 609.25Gender B)    = 5,322.51 + 139.5366Educ + 3.3488Exper C)    = 4,713.26 + 139.5366Educ + 3.3488Exper D)    = 4,663.31 + 140.6634Educ + 3.3566Exper Model B: Salary = β0 + β1Educ + β2Exper + β3Gender + ε To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered: Salary = the monthly salary (excluding fringe benefits and bonuses) , Educ = the number of years of education, Exper = the number of months of experience, Train = the number of weeks of training, Gender = the gender of an individual; 1 for males, and 0 for females. Excel partial outputs corresponding to these models are available and shown below. Model A: Salary = β<sub>0</sub> + β<sub>1</sub>Educ + β<sub>2</sub>Exper + β<sub>3</sub>Train + β<sub>4</sub>Gender + ε   Model B: Salary = β<sub>0</sub> + β<sub>1</sub>Educ + β<sub>2</sub>Exper + β<sub>3</sub>Gender + ε   Using Model B, what is the regression equation found by Excel for females? A)    = 4,713.26 + 139.5366Educ + 3.3488Exper + 609.25Gender B)    = 5,322.51 + 139.5366Educ + 3.3488Exper C)    = 4,713.26 + 139.5366Educ + 3.3488Exper D)    = 4,663.31 + 140.6634Educ + 3.3566Exper Using Model B, what is the regression equation found by Excel for females?


Definitions:

Social Engineering

Getting around security systems by tricking computer users inside a company into revealing sensitive information or gaining unauthorized access privileges.

Confidential

Information or material designated as private or secret, requiring careful handling and restricted access to protect privacy or integrity.

Credit Bureaus

Institutions that collect and compile individual credit histories, providing credit reports to lenders for decision-making purposes.

Appealing Fix

A solution or repair to a problem that is attractive or satisfying because it is effective, easy, or beneficial.

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