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Math Help - Regression models

  1. #1
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    Regression models

    Just a little confused what's being asked.. So...
    I have these variables to play with:

    Y: Wage is the annual Wage of an employee (response variable)

    X1: Starting Wage (StWage) is the employee’s Starting Wage
    X2: Years of Experience (YrsExp) is the number of years the employee has been in the workforce
    X3: Previous Experience (PrevExp) an employee’s Years of Previous Experience.
    X4: Years of Education (YrsEd) is the total number of years of education.
    G: Gender is coded M if employee is male, F if the employee is female.
    D: Education Level is one of five categories, 1: Post Graduate, 2: Bachelor’s Degree, 3: TAFE,
    4: Completed High School, 5: High School dropout.

    I am asked 2 questions (of which confuse me a little)


    1)Estimate equation Y = β0 + β1X1 + β2X2 + β3X3 + ε with the addition of M as an explanatory variable (i.e., use Females as the base). Write out the equation, with its standard errors. Now estimate the same equation, with F as an explanatory variable (i.e., use Males as the base). Write out this equation and its standard errors also. Compare these two estimations.

    Here, I just filtered for either 'only' male or 'only' female data.. Then the regression. model - blah blah

    2)Now estimate without the Gender variable, but with the Education dummy variables (dropping D5). Also estimate this equation, but this time dropping Education level 4 (D4) instead of level 5. Write out both equations with their standard errors. Compare these two estimations.

    Now, I just used filtered 'all' data except for D5, then 'all' except D4... Then the regression model - blah blah.


    Is that wrong, for 2 it seems to make extra no sense to me.

    Thank you !
    Last edited by cellae; October 22nd 2012 at 05:05 AM.
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