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Final

# 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

This preview shows half of the first page. to view the full 2 pages of the document. 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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