diff --git a/README.Rmd b/README.Rmd
index f2a2c231..34c5a6c9 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -32,7 +32,7 @@ values).  Other input columns are possible explanatory variables
 have missing values) that the user later wants to use to predict "y".
 In practice such an input `data.frame` may not be immediately suitable
 for machine learning procedures that often expect only numeric
-explanatory variables, and may not tolerate missing value.
+explanatory variables, and may not tolerate missing values.
 
 To solve this, `vtreat` builds a transformed `data.frame` where all
 explanatory variable columns have been transformed into a number of
diff --git a/README.md b/README.md
index f07a8bbe..c26e0401 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ explanatory variables (typically numeric or categorical/string-valued,
 these columns may have missing values) that the user later wants to use
 to predict “y”. In practice such an input `data.frame` may not be
 immediately suitable for machine learning procedures that often expect
-only numeric explanatory variables, and may not tolerate missing value.
+only numeric explanatory variables, and may not tolerate missing values.
 
 To solve this, `vtreat` builds a transformed `data.frame` where all
 explanatory variable columns have been transformed into a number of
@@ -305,8 +305,8 @@ treatmentsC <- designTreatmentsC(dTrainC, colnames(dTrainC), 'y', TRUE,
                                  verbose=FALSE)
 print(treatmentsC$scoreFrame[, c('origName', 'varName', 'code', 'rsq', 'sig', 'extraModelDegrees')])
  #    origName   varName  code          rsq        sig extraModelDegrees
- #  1        x    x_catP  catP 1.559780e-01 0.22202097                 2
- #  2        x    x_catB  catB 1.142159e-05 0.99166241                 2
+ #  1        x    x_catP  catP 1.030137e-01 0.32099590                 2
+ #  2        x    x_catB  catB 1.125399e-05 0.99172381                 2
  #  3        z         z clean 2.376018e-01 0.13176020                 0
  #  4        z   z_isBAD isBAD 2.960654e-01 0.09248399                 0
  #  5        x  x_lev_NA   lev 2.960654e-01 0.09248399                 0
@@ -349,9 +349,9 @@ treatmentsN = designTreatmentsN(dTrainN, colnames(dTrainN), 'y',
                                 verbose=FALSE)
 print(treatmentsN$scoreFrame[, c('origName', 'varName', 'code', 'rsq', 'sig', 'extraModelDegrees')])
  #    origName   varName  code          rsq       sig extraModelDegrees
- #  1        x    x_catP  catP 3.558824e-01 0.1184999                 2
- #  2        x    x_catN  catN 2.663337e-02 0.6993947                 2
- #  3        x    x_catD  catD 4.226472e-02 0.6252538                 2
+ #  1        x    x_catP  catP 2.105263e-01 0.2528101                 2
+ #  2        x    x_catN  catN 3.205128e-03 0.8940756                 2
+ #  3        x    x_catD  catD 6.666667e-02 0.5369633                 2
  #  4        z         z clean 2.880952e-01 0.1701892                 0
  #  5        z   z_isBAD isBAD 3.333333e-01 0.1339746                 0
  #  6        x  x_lev_NA   lev 3.333333e-01 0.1339746                 0