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To Examine the Differences Between Salaries of Male and Female

question 107

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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 for males? 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 for males? 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 for males?


Definitions:

Position

The rank or specific role an individual occupies or holds within a structure or organization.

Area

A quantification of the size of a 2D shape or surface, represented in units squared.

Standard Normal Curve

The standard normal curve is a type of normal curve with a mean of 0 and a standard deviation of 1, used as a reference in probability and statistics.

Probability

A measure of the likelihood that an event will occur, expressed as a number between 0 and 1 where 0 indicates impossibility and 1 indicates certainty.

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