diff --git a/chainladder/development/barnzehn.py b/chainladder/development/barnzehn.py
index 2975ba87..783b3645 100644
--- a/chainladder/development/barnzehn.py
+++ b/chainladder/development/barnzehn.py
@@ -22,6 +22,10 @@ class BarnettZehnwirth(TweedieGLM):
 
     Parameters
     ----------
+    drop: tuple or list of tuples
+        Drops specific origin/development combination(s)
+    drop_valuation: str or list of str (default = None)
+        Drops specific valuation periods. str must be date convertible.
     formula: formula-like
         A patsy formula describing the independent variables, X of the GLM
     response:  str
@@ -31,7 +35,9 @@ class BarnettZehnwirth(TweedieGLM):
 
     """
 
-    def __init__(self, formula='C(origin) + development', response=None):
+    def __init__(self, drop=None,drop_valuation=None,formula='C(origin) + development', response=None):
+        self.drop = drop
+        self.drop_valuation = drop_valuation
         self.formula = formula
         self.response = response
 
@@ -40,17 +46,10 @@ def fit(self, X, y=None, sample_weight=None):
             raise ValueError("Only single index/column triangles are supported")
         tri = X.cum_to_incr().log()
         response = X.columns[0] if not self.response else self.response
-        # Check for more than one linear predictor 
-        linearpredictors = 0
-        for term in ModelDesc.from_formula(self.formula).rhs_termlist[1:]:
-            if 'C(' not in term.factors[0].code:
-                linearpredictors += 1
-        if linearpredictors > 1:
-            warnings.warn("Using more than one linear predictor with BarnettZehnwirth may lead to issues with multicollinearity.")
         self.model_ = DevelopmentML(Pipeline(steps=[
             ('design_matrix', PatsyFormula(self.formula)),
             ('model', LinearRegression(fit_intercept=False))]),
-                    y_ml=response, fit_incrementals=False).fit(tri)
+                    y_ml=response, fit_incrementals=True, drop=self.drop, drop_valuation = self.drop_valuation, weighted_step = 'model').fit(X = tri, sample_weight = sample_weight)
         resid = tri - self.model_.triangle_ml_[
             self.model_.triangle_ml_.valuation <= tri.valuation_date]
         self.mse_resid_ = (resid**2).sum(0).sum(1).sum(2).sum() / (
@@ -77,10 +76,11 @@ def transform(self, X):
         X_new = X.copy()
         X_ml = self.model_._prep_X_ml(X.cum_to_incr().log())
         y_ml = self.model_.estimator_ml.predict(X_ml)
-        triangle_ml = self.model_._get_triangle_ml(X_ml, y_ml)
+        triangle_ml, predicted_data = self.model_._get_triangle_ml(X_ml, y_ml)
         backend = "cupy" if X.array_backend == "cupy" else "numpy"
         triangle_ml.is_cumulative = False
         X_new.ldf_ = triangle_ml.exp().incr_to_cum().link_ratio.set_backend(backend)
         X_new.ldf_.valuation_date = pd.to_datetime(options.ULT_VAL)
         X_new._set_slicers()
+        X_new.predicted_data_ = predicted_data
         return X_new
diff --git a/chainladder/development/glm.py b/chainladder/development/glm.py
index 9b121367..c44bb0d5 100644
--- a/chainladder/development/glm.py
+++ b/chainladder/development/glm.py
@@ -22,15 +22,16 @@ class TweedieGLM(DevelopmentBase):
 
     Parameters
     ----------
+    drop: tuple or list of tuples
+        Drops specific origin/development combination(s)
+    drop_valuation: str or list of str (default = None)
+        Drops specific valuation periods. str must be date convertible.
     design_matrix: formula-like
         A patsy formula describing the independent variables, X of the GLM
     response:  str
         Column name for the reponse variable of the GLM.  If ommitted, then the
         first column of the Triangle will be used.
-    weight: str
-        Column name of any weight to use in the GLM. If none specified, then an
-        unweighted regression will be performed.
-    power: float, default=0
+    power: float, default=1
             The power determines the underlying target distribution according
             to the following table:
             +-------+------------------------+
@@ -52,7 +53,7 @@ class TweedieGLM(DevelopmentBase):
         regularization strength. ``alpha = 0`` is equivalent to unpenalized
         GLMs. In this case, the design matrix `X` must have full column rank
         (no collinearities).
-    link: {'auto', 'identity', 'log'}, default='auto'
+    link: {'auto', 'identity', 'log'}, default='log'
         The link function of the GLM, i.e. mapping from linear predictor
         `X @ coeff + intercept` to prediction `y_pred`. Option 'auto' sets
         the link depending on the chosen family as follows:
@@ -78,10 +79,11 @@ class TweedieGLM(DevelopmentBase):
     """
 
     def __init__(self, design_matrix='C(development) + C(origin)',
-                 response=None, weight=None, power=1.0, alpha=1.0, link='log',
-                 max_iter=100, tol=0.0001, warm_start=False, verbose=0):
+                 response=None, power=1.0, alpha=1.0, link='log',
+                 max_iter=100, tol=0.0001, warm_start=False, verbose=0, drop=None,drop_valuation=None):
+        self.drop = drop
+        self.drop_valuation = drop_valuation
         self.response=response
-        self.weight=weight
         self.design_matrix = design_matrix
         self.power=power
         self.alpha=alpha
@@ -93,13 +95,18 @@ def __init__(self, design_matrix='C(development) + C(origin)',
 
     def fit(self, X, y=None, sample_weight=None):
         response = X.columns[0] if not self.response else self.response
+        if sample_weight is None:
+            weight = None
+        else:
+            weight = 'model'
         self.model_ = DevelopmentML(Pipeline(steps=[
             ('design_matrix', PatsyFormula(self.design_matrix)),
             ('model', TweedieRegressor(
                     link=self.link, power=self.power, max_iter=self.max_iter,
                     tol=self.tol, warm_start=self.warm_start,
                     verbose=self.verbose, fit_intercept=False))]),
-                    y_ml=response, weight_ml=self.weight).fit(X)
+                    y_ml=response, weighted_step = weight,
+                    drop=self.drop, drop_valuation=self.drop_valuation).fit(X = X, sample_weight = sample_weight)
         return self
 
     @property
diff --git a/chainladder/development/learning.py b/chainladder/development/learning.py
index 1705c696..041a9abc 100644
--- a/chainladder/development/learning.py
+++ b/chainladder/development/learning.py
@@ -33,9 +33,14 @@ class DevelopmentML(DevelopmentBase):
         Time Series aspects of the model. Predictions from one development period
         get used as featues in the next development period. Lags should be negative
         integers.
+    weight_step: str
+        Step name within estimator_ml that is weighted
+    drop: tuple or list of tuples
+        Drops specific origin/development combination(s)
+    drop_valuation: str or list of str (default = None)
+        Drops specific valuation periods. str must be date convertible.
     fit_incrementals:
-        Whether the response variable should be converted to an incremental basis
-        for fitting.
+        Whether the response variable should be converted to an incremental basis for fitting.
 
     Attributes
     ----------
@@ -48,11 +53,13 @@ class DevelopmentML(DevelopmentBase):
     """
 
     def __init__(self, estimator_ml=None, y_ml=None, autoregressive=False,
-                 weight_ml=None, fit_incrementals=True):
+                 weighted_step=None,drop=None,drop_valuation=None,fit_incrementals=True):
         self.estimator_ml=estimator_ml
         self.y_ml=y_ml
-        self.weight_ml = weight_ml
-        self.autoregressive=autoregressive
+        self.weighted_step = weighted_step
+        self.autoregressive = autoregressive
+        self.drop = drop
+        self.drop_valuation = drop_valuation
         self.fit_incrementals = fit_incrementals
 
     def _get_y_names(self):
@@ -124,7 +131,7 @@ def _get_triangle_ml(self, df, preds=None):
         return Triangle(
             out, origin='origin', development='valuation',
             index=self._key_labels, columns=self._get_y_names(),
-            cumulative=not self.fit_incrementals).dropna()
+            cumulative=not self.fit_incrementals).dropna(), out
 
     def _prep_X_ml(self, X):
         """ Preps Triangle data ahead of the pipeline """
@@ -139,7 +146,7 @@ def _prep_X_ml(self, X):
         df_base = X.incr_to_cum().to_frame(
             keepdims=True, implicit_axis=True, origin_as_datetime=True
             ).reset_index().iloc[:, :-1]
-        df = df_base.merge(X.cum_to_incr().to_frame(
+        df = df_base.merge(X_.to_frame(
                 keepdims=True, implicit_axis=True, origin_as_datetime=True
             ).reset_index(), how='left',
             on=list(df_base.columns)).fillna(0)
@@ -147,6 +154,17 @@ def _prep_X_ml(self, X):
         df['valuation'] = df['valuation'].map(self.valuation_encoder_)
         return df
 
+    def _prep_w_ml(self,X,sample_weight=None):
+        weight_base = (~np.isnan(X.values)).astype(float)
+        weight = weight_base.copy()               
+        if self.drop is not None:
+            weight = weight * self._drop_func(X)
+        if self.drop_valuation is not None:
+            weight = weight * self._drop_valuation_func(X)
+        if sample_weight is not None:
+            weight = weight * sample_weight.values 
+        return weight.flatten()[weight_base.flatten()>0]
+
     def fit(self, X, y=None, sample_weight=None):
         """Fit the model with X.
 
@@ -156,8 +174,8 @@ def fit(self, X, y=None, sample_weight=None):
             Set of LDFs to which the estimator will be applied.
         y : None
             Ignored, use y_ml to set a reponse variable for the ML algorithm
-        sample_weight : None
-            Ignored
+        sample_weight : Triangle-like
+            Weights to use in the regression
 
         Returns
         -------
@@ -178,10 +196,18 @@ def fit(self, X, y=None, sample_weight=None):
             (pd.Series(val).rank()-1)/{'Y':1, 'S': 2, 'Q':4, 'M': 12}[X.development_grain]))
         df = self._prep_X_ml(X)
         self.df_ = df
+        weight = self._prep_w_ml(X,sample_weight)
+        self.weight_ = weight
+        if self.weighted_step == None:
+            sample_weights = {}
+        elif isinstance(self.weighted_step, list):
+            sample_weights = {x + '__sample_weight':weight for x in self.weighted_step}
+        else:
+            sample_weights = {self.weighted_step + '__sample_weight':weight}
         # Fit model
-        self.estimator_ml.fit(df, self.y_ml_.fit_transform(df).squeeze())
+        self.estimator_ml.fit(df, self.y_ml_.fit_transform(df).squeeze(),**sample_weights)
         #return selffit_incrementals 
-        self.triangle_ml_ = self._get_triangle_ml(df)
+        self.triangle_ml_, self.predicted_data_ = self._get_triangle_ml(df)
         return self
 
     @property
@@ -206,9 +232,10 @@ def transform(self, X):
         X_new = X.copy()
         X_ml = self._prep_X_ml(X)
         y_ml=self.estimator_ml.predict(X_ml)
-        triangle_ml = self._get_triangle_ml(X_ml, y_ml)
+        triangle_ml, predicted_data = self._get_triangle_ml(X_ml, y_ml)
         backend = "cupy" if X.array_backend == "cupy" else "numpy"
         X_new.ldf_ = triangle_ml.incr_to_cum().link_ratio.set_backend(backend)
         X_new.ldf_.valuation_date = pd.to_datetime(options.ULT_VAL)
         X_new._set_slicers()
-        return X_new
+        X_new.predicted_data_ = predicted_data
+        return X_new
\ No newline at end of file
diff --git a/chainladder/development/tests/test_barnzehn.py b/chainladder/development/tests/test_barnzehn.py
index 15110b89..a1c4bba8 100644
--- a/chainladder/development/tests/test_barnzehn.py
+++ b/chainladder/development/tests/test_barnzehn.py
@@ -1,9 +1,10 @@
 import numpy as np
 import chainladder as cl
 import pytest
+from chainladder.utils.utility_functions import PTF_formula
+abc = cl.load_sample('abc')
 
 def test_basic_bz():
-    abc = cl.load_sample('abc')
     assert np.all(
         np.around(cl.BarnettZehnwirth(formula='C(origin)+C(development)').fit(abc).coef_.T.values,3).flatten()
         == np.array([11.837,0.179,0.345,0.378,0.405,0.427,0.431,0.66,0.963,1.157,1.278,0.251,-0.056,-0.449,-0.829,-1.169,-1.508,-1.798,-2.023,-2.238,-2.428])
@@ -12,4 +13,41 @@ def test_basic_bz():
 def test_multiple_triangle_exception():
     d = cl.load_sample("usauto")
     with pytest.raises(ValueError):
-        cl.BarnettZehnwirth(formula='C(origin)+C(development)').fit(d)
\ No newline at end of file
+        cl.BarnettZehnwirth(formula='C(origin)+C(development)').fit(d)
+
+def test_drops():
+    '''
+    this function tests the passing in a basic drop_valuation
+    '''
+    assert np.all(
+        np.around(cl.BarnettZehnwirth(formula='C(development)',drop_valuation='1979').fit_transform(abc).ldf_.values,3)
+        == np.around(cl.BarnettZehnwirth(formula='C(development)',drop = [('1977',36),('1978',24),('1979',12)]).fit_transform(abc).ldf_.values,3)
+    )
+    assert np.all(
+        np.around(cl.BarnettZehnwirth(formula='C(development)',drop_valuation='1979').fit(abc).triangle_ml_.values,3)
+        == np.around(cl.BarnettZehnwirth(formula='C(development)',drop = [('1977',36),('1978',24),('1979',12)]).fit(abc).triangle_ml_.values,3)
+    )
+    assert np.all(
+        np.around(cl.BarnettZehnwirth(formula='C(development)',drop_valuation='1979').fit(abc).ldf_.values,3)
+        == np.around(cl.BarnettZehnwirth(formula='C(development)',drop = [('1977',36),('1978',24),('1979',12)]).fit(abc).ldf_.values,3)
+    )
+
+
+def test_bz_2008():
+    '''
+    this function tests the drop parameter by recreating the example in the 2008 BZ paper, section 4.1
+    '''
+    exposure=np.array([[2.2], [2.4], [2.2], [2.0], [1.9], [1.6], [1.6], [1.8], [2.2], [2.5], [2.6]])
+    abc_adj = abc/exposure
+
+    origin_buckets = [(0,1),(2,2),(3,4),(5,10)]
+    dev_buckets = [(24,36),(36,48),(48,84),(84,108),(108,144)]
+    val_buckets = [(1,8),(8,9),(9,12)]
+    
+    abc_formula = PTF_formula(abc_adj,alpha=origin_buckets,gamma=dev_buckets,iota=val_buckets)
+    
+    model=cl.BarnettZehnwirth(formula=abc_formula, drop=('1982',72)).fit(abc_adj)
+    assert np.all(
+        np.around(model.coef_.values,4).flatten()
+        == np.array([11.1579,0.1989,0.0703,0.0919,0.1871,-0.3771,-0.4465,-0.3727,-0.3154,0.0432,0.0858,0.1464])
+    )
\ No newline at end of file
diff --git a/chainladder/development/tests/test_glm.py b/chainladder/development/tests/test_glm.py
index 0065d93e..34532871 100644
--- a/chainladder/development/tests/test_glm.py
+++ b/chainladder/development/tests/test_glm.py
@@ -5,3 +5,9 @@ def test_basic_odp_cl(genins):
         (cl.Chainladder().fit(genins).ultimate_ -
          cl.Chainladder().fit(cl.TweedieGLM().fit_transform(genins)).ultimate_) /
         genins.latest_diagonal).max()< 1e-2
+    
+def test_sample_weight(genins):
+    assert abs(
+        (cl.Chainladder().fit(genins).ultimate_ -
+         cl.Chainladder().fit(cl.TweedieGLM().fit_transform(genins,sample_weight=genins/genins)).ultimate_) /
+        genins.latest_diagonal).max()< 1e-2
\ No newline at end of file
diff --git a/chainladder/development/tests/test_learning.py b/chainladder/development/tests/test_learning.py
new file mode 100644
index 00000000..0b411aca
--- /dev/null
+++ b/chainladder/development/tests/test_learning.py
@@ -0,0 +1,19 @@
+import chainladder as cl
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import Pipeline
+from chainladder.utils.utility_functions import PatsyFormula
+
+def test_incremental(genins):
+    response = [genins.columns[0]]
+    model = cl.DevelopmentML(Pipeline(steps=[
+        ('design_matrix', PatsyFormula('C(development)')),
+        ('model', LinearRegression(fit_intercept=False))]),
+                y_ml=response,fit_incrementals=False).fit(genins)
+    assert abs(model.triangle_ml_.loc[:,:,'2010',:] - genins.mean()).max() < 1e2
+
+def test_misc(genins):
+    model = cl.DevelopmentML(Pipeline(steps=[
+        ('design_matrix', PatsyFormula('C(development)')),
+        ('model', LinearRegression(fit_intercept=False))]),
+                weighted_step = ['model'], fit_incrementals=False).fit(genins, sample_weight=genins/genins)
+    assert abs(model.triangle_ml_.loc[:,:,'2010',:] - genins.mean()).max() < 1e2
\ No newline at end of file
diff --git a/chainladder/utils/utility_functions.py b/chainladder/utils/utility_functions.py
index 3352f8e5..fea2a552 100644
--- a/chainladder/utils/utility_functions.py
+++ b/chainladder/utils/utility_functions.py
@@ -767,3 +767,29 @@ def model_diagnostics(model, name=None, groupby=None):
         out.index = idx
         triangles.append(out)
     return concat(triangles, 0)
+
+
+def PTF_formula(tri: Triangle, alpha: ArrayLike = None, gamma: ArrayLike = None, iota: ArrayLike = None):
+    """ Helper formula that builds a patsy formula string for the BarnettZehnwirth 
+    estimator.  Each axis's parameters can be grouped together. Groups of origin 
+    parameters (alpha) are set equal, and are specified by a ranges (inclusive). 
+    Groups of development (gamma) and valuation (iota) parameters are fit to 
+    separate linear trends, specified as tuples denoting ranges with shared endpoints.
+    In other words, development and valuation trends are fit to a piecewise linear model.
+    A triangle must be supplied to provide some critical information.
+    """
+    formula_parts=[]
+    if(alpha):
+        # The intercept term takes the place of the first alpha
+        for ind,a in enumerate(alpha):
+            if(a[0]==0):
+                alpha=alpha[:ind]+alpha[(ind+1):]
+        formula_parts += ['+'.join([f'I({x[0]} <= origin)' for x in alpha])]
+    if(gamma):
+        dgrain = min(tri.development)
+        formula_parts += ['+'.join([f'I((np.minimum({x[1]-dgrain},development) - np.minimum({x[0]-dgrain},development))/{dgrain})' for x in gamma])]
+    if(iota):
+        formula_parts += ['+'.join([f'I(np.minimum({x[1]-1},valuation) - np.minimum({x[0]-1},valuation))' for x in iota])]
+    if(formula_parts):
+        return '+'.join(formula_parts)
+    return ''
diff --git a/docs/gallery/plot_ptf_resid.ipynb b/docs/gallery/plot_ptf_resid.ipynb
index c36112fe..69454734 100644
--- a/docs/gallery/plot_ptf_resid.ipynb
+++ b/docs/gallery/plot_ptf_resid.ipynb
@@ -20,7 +20,8 @@
    "outputs": [],
    "source": [
     "import chainladder as cl\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import numpy as np"
    ]
   },
   {
@@ -35,8 +36,9 @@
     "weighted standardized residuals along the valuation of the Triangle.\n",
     "\n",
     "We fit a \"diagnostic\" model that deliberately ignores modeling the valuation\n",
-    "vector.  This is done by specifying the patsy formula ``C(origin)+C(development)``\n",
-    "which fits origin and development as categorical features.\n"
+    "vector.  This is done by specifying the patsy formula ``C(origin)+C(development)``, \n",
+    "which fits origin and development as categorical features. The data is adjusted \n",
+    "using a vector of exposures prior to performing the regression.\n"
    ]
   },
   {
@@ -51,7 +53,8 @@
    "outputs": [],
    "source": [
     "abc = cl.load_sample('abc')\n",
-    "model = cl.BarnettZehnwirth(formula='C(origin) + C(development)').fit(abc)\n",
+    "exposure = np.array([[2.2], [2.4], [2.2], [2.0], [1.9], [1.6], [1.6], [1.8], [2.2], [2.5], [2.6]])\n",
+    "model = cl.BarnettZehnwirth(formula='C(origin) + C(development)').fit(abc/exposure)\n",
     "\n",
     "plot1a = model.std_residuals_.T\n",
     "plot1b = plot1a.T.mean()\n",
@@ -69,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {
     "tags": [
      "hide-input"
@@ -78,7 +81,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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VlaX33ntPX//619vtr7S0VC+//LLxPCkpST/4wQ8UFhZmvFZfX6/nn39eW7Zs0b///e9uY+yJefPmafjw4Tpz5oxsNpu2b9+uyy67rNP2ZWVlys3NNZ6fO0Y2fPhwXXnllbrwwgs7Hac8ffq0Xn75ZSNR69lnn9V9993nks/jStnZ2cZ4nI+Pj6699lqtWLGi3RL3hw8f1uOPP66jR4+quLhYL7/8crsJiYcPHzYmcAYEBOiuu+5ScnJyh8c9efKkPv30U4WGhrrngwEAhryBW18DAAD02eTJkzVu3DjjeetlxTryySefGI8vuOCCdrOkli1bpssuu6zTJCFJmjhxou677z5jsGLDhg0dLlU20L7xjW9o4cKFnZb6NZlMmjt3ru69917jtQ8//LC/wjO88cYbxiyrMWPG6Be/+EWbJCGpucLQypUrdeONNxqvrV+/XmVlZU4d47777muXJCQ1Lz13/fXXG88//fTTXnwC1zu3WtPp06fbtYmMjNS3v/1tJSQkdDobLygoSDfeeKPS0tIkScePH9eePXtcFmdZWZkxOBcYGKgHHnhAixYt6jCeBQsW6O677zaev/7668ZMNAAAAAAAhorMzEw9//zz3f6rrKwc6FA7VF9fbyT1mM1m/c///I8uueSSDpcVmzlzpn7xi18YlbjfffddYwJfa6+//rqxjNeECRN07733tkkSkprHHdasWaPk5OQ2S5O5gp+fnxYuXGg87258ccuWLcaYxqhRozRjxow270+ePFnXX399l5MZQ0NDdccddxiJMtnZ2Tp69GhvP4Jb2O12Pffcc8ZnXbNmja666qp2SUKSNGnSJP3yl780Ens+/vhjo9p5i9bJVStWrOg0SUhqHiO86qqrNHfuXFd8FAAA2iFRCAAAD9d6+bBt27Z1mrRz6tQp5eTkGM9TUlJ6fcygoCBdcMEFkpqXKxtsN/I90TrZat++ff167DNnzuizzz4znt9www1dLv21YsUKxcTESGqutLNx48Zuj3HxxRdr4sSJnb6fkpIiHx8fSc0z2CwWi7Phu825yV11dXV92t/SpUuNx9nZ2X3aV2vr1q0zrrevfe1rbZL2OjJz5kyjMtKxY8dUVFTkslgAAAAAAPAEhYWFWr9+fbf/ampqBjrUDm3evFlnzpyR1FxJZ/r06V22Hz9+vDEGV1tbq6ysrDbv19XV6YsvvjCe33DDDZ0uNW82m3XzzTfLZDL14RN0rHVVoIKCApWWlnbatnUi0ZIlS/oUT+vxSVeO2bjCrl27dPz4cUnNVZ4uuuiiLtuHhYXpq1/9qiTJZrO1GfOT1GbMrbul3QAAcDeWHgMAwMMtWbJEr7zyihwOhyorK7Vv3z6dd9557dq1nu0TERGhmTNndrnf06dPKz8/X0ePHtWZM2fU2NjYpgJKYWGh8fjw4cNdzhIaaKWlpTp06JBOnDghi8Uiq9Xa5rO03KjX1tbq1KlTGjVqVL/EdfDgQWPG2IgRI7qdJWQ2m7V06VK99NJLkqT9+/d3e4zWM8I6EhgYqDFjxqi0tFQOh0OnTp0a8HN57syslopLnbFarSooKNDhw4dVXV2thoYG2Ww24/2WEuJSc191lczMTOPxhRde6NQ2M2fONKoa5ebmKi4uzmXxAAAAAAAA92o9FuDskuIzZ840Jnvl5uZq/vz5xnt5eXnG2FBoaGiHY3qtjR07VlOmTNHBgwd7GnqXpkyZoqioKCMxZsuWLVq9enW7dgcPHtSJEyeM560nMHaksbFR+fn5OnLkiGpqalRfX99mkmPrylGuHLNxhd6e6xa5ublauXKl8bz1eOMnn3yiZcuWdVidCACA/kCiEAAAHq4l6adl1s2WLVs6HFTYunWr8fiiiy7qdLmmo0eP6u9//7uysrKcXlJssM7y2r17t1599dUeVW6pra3tt0Sh1gMgCQkJRmWfrkydOtV4XFRUJIfD0eXMLWeSfkaMGGE8HgwVhVon9khqt0Rei7Nnz+qtt95Senq6amtrndq3s+2c2U/L4Jkkvf/++07NoGtdfevcEtQAAAAAAHi7a665RqtWrRroMHotLy/PeLxlyxZ9+eWX3W7T+v7/3LGA1mND8fHxnY7XtTZ58mSXJwpJzdV9XnnlFUnN44jXXXddu7GOTz75xHg8ZcqUdsvHt6irq9Orr76qLVu2tBvn6YyrxmxcpfW53r17t1OJTK3H1c4913PmzFFAQIAaGhpUVFSkH/3oR0pNTdWcOXMUFxfX4fJ1AAC4C391AADwAikpKUai0Oeff65bbrmlzYyUgoICHTt2rE37jmRlZen3v/+9MZPJWd1VfBkIr732mt54440eb+fs4IUrtE6wioyMdGqb1u2sVqvq6+vbLdXVWlfvtWidoNS6Es9AOTdZKTg4uF2buro6/epXv+rxbDNXnd+qqqo2zzds2NDjfbSUKgcAAAAAAINfQ0NDm3GF1ktwOevcsYDWY0POTlxz1wS3JUuW6NVXX5XD4VB5ebkOHDjQZmm1pqamNstpdTa+WF5ervvvv1+nTp3q0fH7c0zOGa3HfpxJCDtXXV1dm+fBwcFas2aNHn/8cVmtVlVWVuqtt97SW2+9JX9/fyUkJGj69OmaO3cuFagBAG5HohAAAF5g/vz5ev7551VfX6/GxkZ98cUXbUr/tp7tM3nyZI0bN67dPmpqavT//t//M5KERo8erbS0NCUmJmr06NEaPny4/Pz8jJlErRNxnK081F/27t3bJkkoMTFRKSkpio+PV0REhAICAuTn52e8/8ADDygnJ0eS2ixJ5m6tE6yGDRvm1DYdLcvVVTKQO9atd7fS0tI2z8PCwtq1ef75540kIT8/P6WkpGjOnDkaP368wsLC5O/vb8zCKysr0x133CHJdefXFZWXBkNSFgAAAAAAcI47xgJ6MzbkbLueGjVqlGbMmKF9+/ZJah5PbJ0otGvXLiPRyc/Pr9PluB5//HEjSSgwMFAXX3yxZs2apaioKIWEhLQZs9m/f78efPBBSf07JueMvp7vjsZLFyxYoOjoaL3xxhvauXOnrFarpOaq2Tk5OcrJydEbb7yhuLg4ffOb32zz8wcAwJVIFAIAwAsMGzZM8+fPV0ZGhqTmGU0tiUJWq1Xbt2832na2dvjGjRuNG+DY2Fg9+OCDXa6TPVBVhJwZNHj33XeNx8uXL9f3vve9LtsP1Iyl1j/fxsZGp7Y59+fujWuZ5+fnG49NJpPi4+PbvF9ZWWn0abPZrJ///OddDpy4o6+2/rkPHz5cL7zwgsuPAQAAAAAABo9zE3T+9re/OVXJuSu9GRtytl1vLFmyxEgU2rFjh77zne/I399fUtuJiOeff76GDx/ebvuDBw8ay6IFBgbqt7/9bYcTFlsMZBWh7iY+Dhs2zBgrfeSRRzRp0iSXHHfChAn67//+b1ksFuXm5ionJ0e5ubkqLCw0EskOHTqkBx98UD/60Y+0cOFClxwXAIDWul/sFAAAeITW5X6zs7NVWVkpScrMzDTW+Pb19dWFF17Y4fYtgwCSdNVVV3WbgFJeXt7XkCX1fNmr7mbz2O12ozqQyWTS6tWru91nT0shu0pISEiPY2jdztfXV4GBgS6Pa6Dt2LHDeDxp0qR2g2779u0zEsaSk5O7nV3lqr7aWmhoqPH4zJkzbUqFAwAAAAAA79NSbbvF8ePH+7zPvo4NudqCBQuMhKj6+npjya2amhplZWUZ7VJTUzvcPjs723ickpLSZZKQ5LrP4uv7f3URnK3g3N0YY+uxnxMnTvQusC4EBQVpzpw5uuGGG/TrX/9azz//vNasWaPIyEhJzZMln3vuOZ09e9blxwYAgEQhAAC8xPTp09vcSG7btk1S+9k+wcHBHW7fet3tmJiYLo9lt9uN2UF91TrR5dy1uzty5MiRLt+vqakxyvaGhoa2GXDpyNGjR41Eqq64Ywmv1jORCgoKnFrCLTc313gcGxvrkUuLdSUrK0slJSXG847KWLckwUnd91VJOnDggGuCa2XkyJEaNWqU8XzPnj0uPwYAAAAAABhcEhISjMeuGAtoPTZUWFjo1NhQ60rMrhYQEKD58+cbz7ds2SJJ+vTTT40EnNDQUM2aNavD7XsyvijJmOzXV60nPJ45c6bbiuSnTp3qtprR5MmTjcetk6TcJSgoSKmpqbr//vuNhLTa2lrl5eW5/dgAgKGHRCEAALyEyWRqs6zYli1bVFdXp927dxuvta461NH2LborYfzFF1+ourq698G2Mnr0aONxcXFxtzfyn332WZfvt6xxLjWv793d/jZs2OBElGozY6wlEamvpk6dauy3pqamzbnqiMPhaJP4NWPGDJfEMVjU1tbqmWeeMZ4PHz5cl1xySbt2rc9xd321sbGxzc+sKz09x3PmzDEer1u3zqll8QAAAAAAgHu0LJElOV9VpqfbtB4LSE9P73O1lylTphjVcE6fPq29e/d22f7EiRNuTxxpXS1o7969qq6uNhKGJOmiiy5qUyG8tZ6ML1ZWVmrXrl19C/Y/goKCjMmRjY2N3VZ7alnSviutz/Wnn36q06dP9y1IJ40ePVrjx483nrtqDBYAgNZIFAIAwIu0TgQ6cuSI/vnPfxoJDyEhIUpOTu5029YJOy1lhTtSU1OjF1980QXRNhs3bpxRVaiqqqrL2Vi7d+/uNpkmODjY2J/FYulyZlJubq7TiUIjRowwHreuaNMXw4cPb7PO+Msvv9zlbKb169eruLhYUvPAy/Lly10Sx2Bw4sQJ/epXv1JFRYXx2n/91391uLRa6766e/fuLgfyXnrpJacHcnp6jleuXGkkLRUWFur111936jgSgzwAAAAAALha6yrazo7d9HSbtLQ0DR8+XJJUUVGh5557zumJQzU1Ne0qBgUHB+uCCy4wnv/973/vNPnI4XDohRdecPtEpRkzZhhVlG02m1577TUVFhYa73c1EXHMmDHG4507d3bazm636y9/+YuamppcEHGz1tWeMjIyOm1XUVGht99+u9v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",
       "text/plain": [
        "<Figure size 1400x1000 with 4 Axes>"
       ]
@@ -86,7 +89,7 @@
      "metadata": {
       "image/png": {
        "height": 935,
-       "width": 1157
+       "width": 1170
       }
      },
      "output_type": "display_data"
@@ -98,7 +101,7 @@
     "%config InlineBackend.figure_format = 'retina'\n",
     "\n",
     "fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(14,10))\n",
-    "fig.suptitle(\"Barnett Zehnwirth\\nStandardized residuals of the statistical chainladder model\\n(Fig 3.11)\");\n",
+    "fig.suptitle(\"Barnett Zehnwirth\\nStandardized residuals of the statistical chainladder model\\n(Figure 23)\");\n",
     "\n",
     "plot1a.plot(\n",
     "    style='.', color='gray', legend=False, ax=ax00,\n",
@@ -106,14 +109,14 @@
     "plot1b.plot(color='red', legend=False, ax=ax00)\n",
     "\n",
     "plot2a.plot(style='.', color='gray', legend=False, ax=ax01, xlabel='Origin Period')\n",
-    "plot2b.plot(color='red', legend=False, ax=ax01)\n",
+    "plot2b.plot(color='red', legend=False, ax=ax01,xlim=(6.5,17.5))\n",
     "plot3a.plot(\n",
     "    style='.', color='gray', legend=False, ax=ax10,\n",
     "    xlabel='Valuation Date', ylabel='Weighted Standardized Residuals')\n",
     "plot3b.plot(color='red', legend=False, ax=ax10)\n",
     "\n",
     "plot4.plot(kind='scatter', marker='o', color='gray', \n",
-    "           x='Fitted Values', y='Residual', ax=ax11, sharey=True);\n"
+    "           x='Fitted Values', y='Residual', ax=ax11, sharey=True);"
    ]
   },
   {
@@ -140,7 +143,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/images/plot_ptf_resid.png b/docs/images/plot_ptf_resid.png
index 66fa8da8..498fdf17 100644
Binary files a/docs/images/plot_ptf_resid.png and b/docs/images/plot_ptf_resid.png differ