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PPPM 413 Study Guide - Final Guide: Dependent And Independent Variables, Statistical Parameter, Stata


Department
Planning, Public Policy and Management
Course Code
PPPM 413
Professor
Grant Jacobsen
Study Guide
Final

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PPPM 413 Finl Notes
Multivariate Regression
Regression with 1+ independent variable
ease with which it takes control variables into account
o Control variables helps examine if an observed relationship is a casual relationship
A type of ordinary least squares regression
Goal: Full Model Specification
o Accounting for all variables that affect a dependent variable
Model Specification
The choice of variables included in the regression
Eliminate bias in coef. of choice
o Difference between the expected estimated coef. (the value of b if there is an infinite
amount of data) and the true/casual value of parameter
o Omitted Variable Bias
Most common type of bias
Due to the omission of another relevant variable
Only exists if the omitted variable (W) is correlated with BOTH X and Y
Example:
X <-> W -> Y
If regress X -> Y ut don’t ontrol for W, then the oeffiient on X is
biased
o Bias phrasing:
Absolute values:
Overestimate: exp. value of coef. > true parameter in abs. value
Underestimate: exp. value of coef. < true parameter in abs. value
Actual values:
Positively-biased: exp. value of coef. > true parameter (both +/-)
o X <(+/-)> W <(+/-)> Y
Negatively-biased: exp. value of coef. < true parameter (mix of +/-)
o X <(-/+)> W <(+/-)> Y
Equation:
o       
o Dependent variable = independent variables (most important and other included
factors) + variables not in model (all excluded factors, residual)
Interpretation of individual coefficients (coef.)
The coef. indicates the change in the DV associated with 1 unit change in the IV, holding
constant all of the other IV included in the regression
o Estimates can be made as though other IV do not change when the IV of interest
changes
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