diff --git a/02_quant_analysis.py b/02_quant_analysis.py
index 2c33ce8..edc787e 100644
--- a/02_quant_analysis.py
+++ b/02_quant_analysis.py
@@ -1,7 +1,7 @@
 import marimo
 
 __generated_with = "0.19.2"
-app = marimo.App(width="medium")
+app = marimo.App(width="full")
 
 
 @app.cell
@@ -167,17 +167,19 @@ def _(S, mo):
     ''')
 
 
-    return (filter_form,)
+    return
 
 
 @app.cell
-def _(S, data_validated, filter_form, mo):
-    mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
-    _d = S.filter_data(data_validated, age=filter_form.value['age'], gender=filter_form.value['gender'], income=filter_form.value['income'], ethnicity=filter_form.value['ethnicity'], consumer=filter_form.value['consumer'])
+def _(data_validated):
+    # mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
+    # _d = S.filter_data(data_validated, age=filter_form.value['age'], gender=filter_form.value['gender'], income=filter_form.value['income'], ethnicity=filter_form.value['ethnicity'], consumer=filter_form.value['consumer'])
 
-    # Stop execution and prevent other cells from running if no data is selected
-    mo.stop(len(_d.collect()) == 0, mo.md("**No Data available for current filter combination**"))
-    data = _d
+    # # Stop execution and prevent other cells from running if no data is selected
+    # mo.stop(len(_d.collect()) == 0, mo.md("**No Data available for current filter combination**"))
+    # data = _d
+
+    data = data_validated
 
     data.collect()
     return (data,)
@@ -391,28 +393,25 @@ def _(S, mo, vscales):
     mo.md(f"""
     ### How does each voice score on a scale from 1-10?
 
-    {mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales, x_label='Voice', width=1000))}
+    {mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales, x_label='Voice', width=1000, domain=[1,10]))}
     """)
     return
 
 
 @app.cell
-def _(vscales):
-    target_cols=[c for c in vscales.columns if c not in ['_recordId']]
-    target_cols
-    return (target_cols,)
+def _(utils, vscales):
+    _target_cols=[c for c in vscales.collect().columns if c not in ['_recordId']]
+    vscales_row_norm = utils.normalize_row_values(vscales.collect(), target_cols=_target_cols)
+    vscales_row_norm
+    return (vscales_row_norm,)
 
 
 @app.cell
-def _(target_cols, utils, vscales):
-    vscales_row_norm = utils.normalize_row_values(vscales.collect(), target_cols=target_cols)
-    return
+def _(S, mo, vscales_row_norm):
+    mo.md(f"""
+    ### Voice scale 1-10 normalized per respondent?
 
-
-@app.cell
-def _(mo):
-    mo.md(r"""
- 
+    {mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales_row_norm, x_label='Voice', width=1000))}
     """)
     return
 
diff --git a/plots.py b/plots.py
index 8c68bd6..897b07d 100644
--- a/plots.py
+++ b/plots.py
@@ -13,6 +13,12 @@ import hashlib
 class JPMCPlotsMixin:
     """Mixin class for plotting functions in JPMCSurvey."""
 
+    def _process_title(self, title: str) -> str | list[str]:
+        """Process title to handle <br> tags for Altair."""
+        if isinstance(title, str) and '<br>' in title:
+            return title.split('<br>')
+        return title
+
     def _sanitize_filename(self, title: str) -> str:
         """Convert plot title to a safe filename."""
         # Remove HTML tags
@@ -156,8 +162,8 @@ class JPMCPlotsMixin:
         chart_spec = chart.to_dict()
         existing_title = chart_spec.get('title', '')
         
-        # Handle different title formats (string vs dict)
-        if isinstance(existing_title, str):
+        # Handle different title formats (string vs dict vs list)
+        if isinstance(existing_title, (str, list)):
             title_config = {
                 'text': existing_title,
                 'subtitle': lines,
@@ -260,6 +266,7 @@ class JPMCPlotsMixin:
         color: str = ColorPalette.PRIMARY,
         height: int | None = None,
         width: int | str | None = None,
+        domain: list[float] | None = None,
     ) -> alt.Chart:
         """Create a bar plot showing average scores and count of non-null values for each column."""
         df = self._ensure_dataframe(data)
@@ -278,11 +285,14 @@ class JPMCPlotsMixin:
 
         # Convert to pandas for Altair (sort by average descending)
         stats_df = pl.DataFrame(stats).sort('average', descending=True).to_pandas()
+        
+        if domain is None:
+            domain = [stats_df['average'].min(), stats_df['average'].max()]
 
         # Base bar chart
         bars = alt.Chart(stats_df).mark_bar(color=color).encode(
             x=alt.X('voice:N', title=x_label, sort='-y'),
-            y=alt.Y('average:Q', title=y_label, scale=alt.Scale(domain=[0, 10])),
+            y=alt.Y('average:Q', title=y_label, scale=alt.Scale(domain=domain)),
             tooltip=[
                 alt.Tooltip('voice:N', title='Voice'),
                 alt.Tooltip('average:Q', title='Average', format='.2f'),
@@ -303,7 +313,7 @@ class JPMCPlotsMixin:
 
         # Combine layers
         chart = (bars + text).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -360,7 +370,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='Count')
             ]
         ).add_params(selection).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -420,7 +430,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='Count')
             ]
         ).add_params(selection).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -473,7 +483,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='1st Place Votes')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -514,7 +524,7 @@ class JPMCPlotsMixin:
         )
 
         chart = (bars + text).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -571,7 +581,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='Selections')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -627,7 +637,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='In Top 3')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -713,7 +723,7 @@ class JPMCPlotsMixin:
         # Combine layers
         chart = (bars + text).properties(
             title={
-                "text": title,
+                "text": self._process_title(title),
                 "subtitle": [trait_description, "(Numbers on bars indicate respondent count)"]
             },
             width=width or 800,
@@ -776,7 +786,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('correlation:Q', format='.2f')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or 350
         )
@@ -832,7 +842,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('correlation:Q', format='.2f')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or 350
         )
diff --git a/utils.py b/utils.py
index 28d3a2c..3b6fc0a 100644
--- a/utils.py
+++ b/utils.py
@@ -351,18 +351,22 @@ def calculate_weighted_ranking_scores(df: pl.LazyFrame) -> pl.DataFrame:
 
 def normalize_row_values(df: pl.DataFrame, target_cols: list[str]) -> pl.DataFrame:
     """
-    Normalizes values in the specified columns row-wise (Standardization: (x - mean) / std).
-    Ignores null values (NaNs). Only applied if there are at least 2 non-null values in the row.
+    Normalizes values in the specified columns row-wise to 0-10 scale (Min-Max normalization).
+    Formula: ((x - min) / (max - min)) * 10
+    Ignores null values (NaNs).
     """
     
     # Using list evaluation for row-wise stats
     # We create a temporary list column containing values from all target columns
+    # Ensure columns are cast to Float64 to avoid type errors with mixed/string data
     df_norm = df.with_columns(
-        pl.concat_list(target_cols)
+        pl.concat_list([pl.col(c).cast(pl.Float64) for c in target_cols])
         .list.eval(
-            # Apply standardization: (x - mean) / std
-            # std(ddof=1) is the sample standard deviation
-            (pl.element() - pl.element().mean()) / pl.element().std(ddof=1)
+            # Apply Min-Max scaling to 0-10
+            (
+                (pl.element() - pl.element().min()) / 
+                (pl.element().max() - pl.element().min())
+            ) * 10
         )
         .alias("_normalized_values")
     )
@@ -377,8 +381,8 @@ def normalize_row_values(df: pl.DataFrame, target_cols: list[str]) -> pl.DataFra
 
 def normalize_global_values(df: pl.DataFrame, target_cols: list[str]) -> pl.DataFrame:
     """
-    Normalizes values in the specified columns globally (Standardization: (x - global_mean) / global_std).
-    Computes a single mean and standard deviation across ALL values in the target_cols and applies it.
+    Normalizes values in the specified columns globally to 0-10 scale.
+    Formula: ((x - global_min) / (global_max - global_min)) * 10
     Ignores null values (NaNs).
     """
     # Ensure eager for scalar extraction
@@ -390,19 +394,23 @@ def normalize_global_values(df: pl.DataFrame, target_cols: list[str]) -> pl.Data
         return df.lazy() if was_lazy else df
 
     # Calculate global stats efficiently by stacking all columns
-    stats = df.select(target_cols).melt().select([
-        pl.col("value").mean().alias("mean"),
-        pl.col("value").std().alias("std")
+    # Cast to Float64 to ensure numeric calculations
+    stats = df.select([pl.col(c).cast(pl.Float64) for c in target_cols]).melt().select([
+        pl.col("value").min().alias("min"),
+        pl.col("value").max().alias("max")
     ])
     
-    global_mean = stats["mean"][0]
-    global_std = stats["std"][0]
+    global_min = stats["min"][0]
+    global_max = stats["max"][0]
     
-    if global_std is None or global_std == 0:
+    # Handle edge case where all values are same or none exist
+    if global_min is None or global_max is None or global_max == global_min:
         return df.lazy() if was_lazy else df
 
+    global_range = global_max - global_min
+
     res = df.with_columns([
-        ((pl.col(col) - global_mean) / global_std).alias(col)
+        (((pl.col(col).cast(pl.Float64) - global_min) / global_range) * 10).alias(col)
         for col in target_cols
     ])
     
@@ -649,10 +657,12 @@ class JPMCSurvey(JPMCPlotsMixin):
         return subset, None
     
     
-    def get_voice_scale_1_10(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
+    def get_voice_scale_1_10(self, q: pl.LazyFrame, drop_cols=['Voice_Scale_1_10__V46']) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the Voice Scale 1-10 ratings for the Chase virtual assistant. 
         
         Returns subquery that can be chained with other polars queries.
+        
+        Drops scores for V46 as it was improperly configured in the survey and thus did not show up for respondents.
         """
         
         QIDs_map = {}
@@ -662,6 +672,12 @@ class JPMCSurvey(JPMCPlotsMixin):
                 # Convert "Voice 16 Scale 1-10_1" to "Scale_1_10__Voice_16"
                 QIDs_map[qid] = f"Voice_Scale_1_10__V{val['QName'].split()[1]}"
         
+        for col in drop_cols:
+            if col in QIDs_map.values():
+                # remove from QIDs_map
+                qid_to_remove = [k for k,v in QIDs_map.items() if v == col][0]
+                del QIDs_map[qid_to_remove]
+        
         return self._get_subset(q, list(QIDs_map.keys()), rename_cols=False).rename(QIDs_map), None