Reference no: EM132461914

This question will use the data file called earnings.dta. This dataset contains 526 randomly chosen individual observations. The variables recorded in the data are an individual's average hourly wage, education, experience, and length of time with their current employer (tenure).

Run a regression of wage on experience. That is, where wage is the dependent variable. Use 4-decimals throughout your answers.

[Note, there are so many observations in this data, that the appropriate t-distribution is virtually indistinguishable from the z-distribution. Your z-table has a lot more flexibility than a t-table, so you should use it in this case. If you want to calculate your t-values on the computer rather than with a paper table, that is fine as well.]

a) How does a 5 unit increase in experience change the predicted wage? (Use 4-decimals.)

b) What is a 99% confidence interval for the beta coefficient on experience?

c) What is the p-value for a two-sided hypothesis test that the beta coefficient on experience is .0120?

 Source |      SS          df      MS      Number of obs  =      526

-------------+----------------------------------   F(1, 524)      =     6.77

      Model | 91.2751351        1 91.2751351   Prob > F       =   0.0096

   Residual | 7069.13916      524 13.4907236   R-squared      =   0.0127

-------------+----------------------------------   Adj R-squared  =   0.0109

      Total | 7160.41429      525 13.6388844   Root MSE       =    3.673

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       wage |     Coef.  Std. Err.     t   P>|t|    [95% Conf. Interval]

-------------+----------------------------------------------------------------

      exper |  .0307219  .0118111    2.60  0.010     .007519   .0539247

      _cons |  5.373305  .2569919    20.91  0.000    4.868444   5.878166