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To Examine the Differences Between Salaries of Male and Female

question 89

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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   Assuming the same years of education and months of experience, what is the null hypothesis for testing whether the mean salary of males is greater than the mean salary of females using Model B? A)  H<sub>0</sub>: β<sub>3</sub> ≤ 0 B)  H<sub>0</sub>: β<sub>3</sub> ≥ 0 C)  H<sub>0</sub>: β<sub>3</sub>> 0 D)  H<sub>0</sub>: β<sub>3</sub> = 0 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   Assuming the same years of education and months of experience, what is the null hypothesis for testing whether the mean salary of males is greater than the mean salary of females using Model B? A)  H<sub>0</sub>: β<sub>3</sub> ≤ 0 B)  H<sub>0</sub>: β<sub>3</sub> ≥ 0 C)  H<sub>0</sub>: β<sub>3</sub>> 0 D)  H<sub>0</sub>: β<sub>3</sub> = 0 Assuming the same years of education and months of experience, what is the null hypothesis for testing whether the mean salary of males is greater than the mean salary of females using Model B?


Definitions:

Volume Variance

A measure of the difference between the budgeted and actual volume of production, impacting costs.

Overhead Variances

The difference between actual overhead costs and the budgeted or standard overhead costs.

Predetermined Overhead Rate

An estimated rate used to allocate manufacturing overhead costs to products or job orders, calculated before the costs are actually incurred.

Labor-Hour

A unit of measure representing one hour of work by an employee, often used in costing and budgeting processes.

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