If the standard error of the estimate of 3₁ is 1.04, then the true value of ₁ lies between grows, you would expect this range to in size. and As the number of observations in a data set

ENGR.ECONOMIC ANALYSIS
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Chapter1: Making Economics Decisions
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The measure of standard error can also be applied to the parameter estimates resulting from linear regressions.
For example, consider the following linear regression equation that describes the relationship between education and wage:
WAGE: = Bo + B₁ EDUC; + &i
where WAGE; is the hourly wage of person i (i.e., any specific person) and EDUC; is the number of years of education for that same person. The
residual ₂ encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero.
Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates:
WAGE;= -10.7+ 3.1 EDUC;
If the standard error of the estimate of B₁ is 1.04, then the true value of B₁ lies between
grows, you would expect this range to
in size.
and
. As the number of observations in a data set
Transcribed Image Text:The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGE: = Bo + B₁ EDUC; + &i where WAGE; is the hourly wage of person i (i.e., any specific person) and EDUC; is the number of years of education for that same person. The residual ₂ encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates: WAGE;= -10.7+ 3.1 EDUC; If the standard error of the estimate of B₁ is 1.04, then the true value of B₁ lies between grows, you would expect this range to in size. and . As the number of observations in a data set
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