diff --git a/src/pg/sql/21_gwr.sql b/src/pg/sql/21_gwr.sql
index af0c96e..0596d3f 100644
--- a/src/pg/sql/21_gwr.sql
+++ b/src/pg/sql/21_gwr.sql
@@ -1,7 +1,7 @@
 CREATE OR REPLACE FUNCTION
 CDB_GWR(subquery text, dep_var text, ind_vars text[],
        bw numeric default null, fixed boolean default False, kernel text default 'bisquare')
-RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON, predicted numeric, residuals numeric, r_squared numeric, rowid bigint, bandwidth numeric)
+RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON, filtered_t_vals JSON, predicted numeric, residuals numeric, r_squared numeric, rowid bigint, bandwidth numeric)
 AS $$
 
 from crankshaft.regression import gwr_cs
diff --git a/src/py/crankshaft/crankshaft/regression/gwr_cs.py b/src/py/crankshaft/crankshaft/regression/gwr_cs.py
index a1fe9c5..7ea7f16 100644
--- a/src/py/crankshaft/crankshaft/regression/gwr_cs.py
+++ b/src/py/crankshaft/crankshaft/regression/gwr_cs.py
@@ -73,6 +73,9 @@ def gwr(subquery, dep_var, ind_vars, bw=None,
     coefficients = []
     stand_errs = []
     t_vals = []
+    filtered_t_vals = []
+    c_alpha = model.adj_alpha
+    filtered_t = model.filter_tvals(c_alpha[1])
     predicted = model.predy.flatten()
     residuals = model.resid_response
     r_squared = model.localR2.flatten()
@@ -85,10 +88,10 @@ def gwr(subquery, dep_var, ind_vars, bw=None,
                                       for k, var in enumerate(ind_vars)}))
         t_vals.append(json.dumps({var: model.tvalues[idx, k]
                                   for k, var in enumerate(ind_vars)}))
+        filtered_t_vals.append(json.dumps({var: filtered_t[idx, k]
+                                  for k, var in enumerate(ind_vars)}))
 
-    plpy.notice(str(zip(coefficients, stand_errs, t_vals,
-                        predicted, residuals, r_squared, rowid, bw)))
-    return zip(coefficients, stand_errs, t_vals,
+    return zip(coefficients, stand_errs, t_vals, filtered_t_vals,
                predicted, residuals, r_squared, rowid, bw)
 
 def gwr_predict(subquery, dep_var, ind_vars, bw=None,