Let me carefully parse the data and compute the numbers.

Row list:
1001, 2025-01-06, Texas, 1240.00 → Q1, Texas
1002, 1/9/2025, CA→California, 980 → Q1
1003, 2025-01-14, TX→Texas, 312.50 → Q1
1004, Jan 21 2025, california→California, 1764.00 → Q1
1005, 2025-01-27, Ohio, 455 → Q1
1006, 2/3/2025, texas, N/A → Q1, exclude
1007, 2025-02-10, OH→Ohio, 1102.25 → Q1
1008, Feb 14 2025, California, 2310.00 → Q1
1009, 2025-02-18, TX→Texas, -450.00 → Q1 (refund)
1010, 2025-02-24, Texas, 870.10 → Q1
1011, 3/3/2025, Ohio, 1999.99 → Q1
1012, 2025-03-08, CA→California, 640.00 → Q1
1008 duplicate → delete
1013, Mar 12 2025, tx→Texas, 1580.00 → Q1
1014, 2025-03-19, ohio→Ohio, 225.75 → Q1
1015, 2025-03-27, California, 3050.00 → Q1
1016, 4/2/2025, TX→Texas, 990.00 → Q2
1017, 2025-04-07, Ohio, unknown → exclude, Q2
1018, Apr 11 2025, CA→California, 1420.50 → Q2
1019, 2025-04-16, Texas, 2075.00 → Q2
1020, 2025-04-22, OH→Ohio, 760.25 → Q2
1021, 5/1/2025, California, 1111.11 → Q2
1022, 2025-05-06, TX→Texas, 845.00 → Q2
1023, May 13 2025, Ohio, -220.00 → Q2
1024, 2025-05-19, texas→Texas, 1300.00 → Q2
1025, 2025-05-28, CA→California, 2940.40 → Q2
1026, 6/4/2025, Ohio, 515.00 → Q2
1027, 2025-06-10, California, 1875.25 → Q2
1028, Jun 17 2025, TX→Texas, N/A → exclude, Q2
1029, 2025-06-23, OH→Ohio, 1660.00 → Q2
1030, 2025-06-30, Texas, 730.90 → Q2

Data quality: rows received = 31 (data rows). Duplicate removal = 1 (second 1008). Row exclusion = 3 (1006 N/A, 1017 unknown, 1028 N/A). Row count = 31 - 1 - 3 = 27.

Aggregating Texas Q1: 1240.00 + 312.50 + (-450.00) + 870.10 + 1580.00 = 1240+312.50=1552.50; 1552.50-450=1102.50; +870.10=1972.60; +1580=3552.60.
Texas Q2: 990 + 2075 + 845 + 1300 + 730.90 = 990+2075=3065; +845=3910; +1300=5210; +730.90=5940.90.
Texas total = 3552.60 + 5940.90 = 9493.50.

California Q1: 980 + 1764 + 2310 + 640 + 3050 = 980+1764=2744; +2310=5054; +640=5694; +3050=8744.
California Q2: 1420.50 + 1111.11 + 2940.40 + 1875.25 = 1420.50+1111.11=2531.61; +2940.40=5472.01; +1875.25=7347.26.
California total = 8744 + 7347.26 = 16091.26.

Ohio Q1: 455 + 1102.25 + 1999.99 + 225.75 = 455+1102.25=1557.25; +1999.99=3557.24; +225.75=3782.99.
Ohio Q2: 760.25 + (-220) + 515 + 1660 = 760.25-220=540.25; +515=1055.25; +1660=2715.25.
Ohio total = 3782.99 + 2715.25 = 6498.24.

Q1 total: 3552.60 + 8744 + 3782.99 = 16079.59.
Q2 total: 5940.90 + 7347.26 + 2715.25 = 16003.41.
Grand total: 16079.59 + 16003.41 = 32083.00. Verification: 9493.50+16091.26+6498.24 = 32083.00. Good.

Top region: California, $16,091.26.

Okay, let me build the HTML. Requirements: compute from the data with inline JS (not hardcoded numbers — better to embed the raw CSV and have JS perform the cleaning). That's more robust and matches "compute correctly from the data." Embed the CSV as a string, write the parser (handle quoted fields), amount parsing (strip $, commas, quotes), region normalization mapping, date parsing for the 3 formats (ISO, M/D/YYYY, "Mon D, YYYY"), quarter determination, duplicate removal, exclusions, pivot, bar chart via inline SVG generated by JS.

Writing the code. CSV parsing: simple state machine that handles quoted fields.

Region mapping: trim region, lowercase it. Map: tx/texas → Texas; ca/california → California; oh/ohio → Ohio.

Amount parsing: strip $, commas, whitespace. If the parse result is not a finite number → exclude.

Date parsing:
- ISO: match /^(\d{4})-(\d{2})-(\d{2})$/
- Slash: /^(\d{1,2})\/(\d{1,2})\/(\d{4})$/ → month is the first one
- Month name: match the month name.

Bar chart: SVG with 3 bars, scaled to the max value, labeled with region names and dollar amounts.

Formatting: toLocaleString with 2-decimal-place display.

Layout: header, single-line heading, data quality panel, pivot table, bar chart. Clean styling with plain CSS.

Edge cases: rows received = total data rows parsed = 31. Duplicates removed = 1. Exclusions = 3. Count = 27.

Write the complete HTML file. Keep it clean and self-contained.