1. QM222 Class 15 Section D1 Review for test Multicollinearity
QM222 Fall 2016 Section D1

2. Sophia’s Model
regress hapmar agewed
      Source |       SS           df       MS      Number of obs   =    16,954
   F(1, 16952)     =      8.42
       Model |           1     Prob > F        =   
    Residual |      16,952     R-squared       =   
   Adj R-squared   =   
       Total |      16,953     Root MSE        =   
      hapmar |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
      agewed |         -2.90   0.004       
       _cons |          70.81   0.000        
In groups, go through the items listed in Q3 and identify what they are and what we learn from them.
QM222 Fall 2016 Section D1

3. Sophia’s Model
      Source |       SS           df       MS      Number of obs   =    16,954
   F(1, 16952)     =      8.42
       Model |           1     Prob > F        =   
    Residual |      16,952     R-squared       =   
   Adj R-squared   =   
       Total |      16,953     Root MSE        =   
      hapmar |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
      agewed |         -2.90   0.004       
       _cons |          70.81   0.000        
      Source |       SS           df       MS      Number of obs   =     1,441
   F(2, 1438)      =      3.32
       Model |           2     Prob > F        =   
    Residual |       1,438     R-squared       =   
   Adj R-squared   =   
       Total |        1,440     Root MSE        =   
      agewed |         -0.70   0.484       
     markids |         -2.50   0.012      
       _cons |          16.22   0.000         
In groups, calculate the missing/omitted variable bias due to the confounding factor and explain exactly why it occurs, and what we learn from about the correlation of agewed and markids.
QM222 Fall 2016 Section D1

4. Multicollinearity etc
Multicollinearity etc. What to do if you find that variables that you believe should be significant are not
If several variables are really measuring the same concept, drop one of them if its |t-stat| is less than ONE.
If you drop a variable with a |t-stat| <1, the adjusted R- squared increases.
NEVER DROP MORE THAN ONE VARIABLE AT A TIME. If you do, you might drop BOTH highly correlated variables.
If you believe that an important confounding factor is missing from the regression that might be biasing the coefficient, try to measure it, or find a proxy for it.
QM222 Fall 2015 Section D1