diff --git a/03_quant_report.py b/03_quant_report.py
new file mode 100644
index 0000000..42d3368
--- /dev/null
+++ b/03_quant_report.py
@@ -0,0 +1,154 @@
+import marimo
+
+__generated_with = "0.19.2"
+app = marimo.App(width="medium")
+
+with app.setup:
+    import marimo as mo
+    import polars as pl
+    from pathlib import Path
+
+    from validation import check_progress, duration_validation, check_straight_liners
+    from utils import JPMCSurvey, combine_exclusive_columns, calculate_weighted_ranking_scores
+    import utils
+
+    from speaking_styles import SPEAKING_STYLES
+
+
+@app.cell
+def _():
+
+    file_browser = mo.ui.file_browser(
+        initial_path="./data/exports", multiple=False, restrict_navigation=True, filetypes=[".csv"], label="Select 'Labels' File"
+    )
+    file_browser
+
+    return (file_browser,)
+
+
+@app.cell
+def _(file_browser):
+    mo.stop(file_browser.path(index=0) is None, mo.md("**⚠️ Please select a `_Labels.csv` file above to proceed**"))
+    RESULTS_FILE = Path(file_browser.path(index=0))
+    QSF_FILE = 'data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf'
+    return QSF_FILE, RESULTS_FILE
+
+
+@app.cell
+def _(QSF_FILE, RESULTS_FILE):
+    S = JPMCSurvey(RESULTS_FILE, QSF_FILE)
+    try:
+        data_all = S.load_data()
+    except NotImplementedError as e:
+        mo.stop(True, mo.md(f"**⚠️ {str(e)}**"))
+    return S, data_all
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ---
+    # Load Data
+
+    **Dataset:** `{Path(RESULTS_FILE).name}`
+
+    **Responses**: `{data_all.collect().shape[0]}`
+    """)
+    return
+
+
+@app.cell
+def _(S, data_all):
+    sl_ss_max_score = 5
+    sl_v1_10_max_score = 10
+
+    _ss_all = S.get_ss_green_blue(data_all)[0].join(S.get_ss_orange_red(data_all)[0], on='_recordId')
+    _sl_ss_c, sl_ss_df = check_straight_liners(_ss_all, max_score=sl_ss_max_score)
+
+    _sl_v1_10_c, sl_v1_10_df = check_straight_liners(
+        S.get_voice_scale_1_10(data_all)[0], 
+        max_score=sl_v1_10_max_score
+    )
+
+
+    mo.md(f"""
+    # Data Validation
+
+    {check_progress(data_all)}
+
+
+
+    {duration_validation(data_all)}
+
+
+    ## Speaking Style - Straight Liners
+    {_sl_ss_c}
+
+
+    ## Voice Score Scale 1-10 - Straight Liners
+    {_sl_v1_10_c}
+    """)
+    return
+
+
+@app.cell
+def _(data_all):
+    # # Drop any Voice Scale 1-10 responses with straight-lining, using sl_v1_10_df _responseId values
+    # records_to_drop = sl_v1_10_df.select('Record ID').to_series().to_list()
+
+    # data_validated = data_all.filter(~pl.col('_recordId').is_in(records_to_drop))
+
+    # mo.md(f"""
+    # Dropped `{len(records_to_drop)}` responses with straight-lining in Voice Scale 1-10 evaluation.
+    # """)
+    data_validated = data_all
+    return (data_validated,)
+
+
+@app.cell(hide_code=True)
+def _():
+
+
+    return
+
+
+@app.cell
+def _(data_validated):
+    data = data_validated
+
+    data.collect()
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ---
+
+    # Introduction (Respondent Demographics)
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ---
+
+    # Brand Character Results
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ---
+
+    # Spoken Voice Results
+    """)
+    return
+
+
+if __name__ == "__main__":
+    app.run()