tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: sally_beck__34377__Settlements Overview
Sheet: Transition Steps

EES Settlements
Transitions Steps
This week:
 |  |  |  |  |  |  |  | OWNERS
 | Establish Settlements SWAT Team to focus on transition:
 |  | EWS |  | EES
 |  | [NAME] |  | [NAME] |  |  |  |  | Done
 |  | [NAME] |  | [NAME]
 |  | [NAME]
 | Finalize Transition Steps |  |  |  |  |  |  |  | SWAT Team
 | Finalize Team Members and Project Scope/Specific Timing for Project
 | [NAME] (Settlements/Bill Payment Data Repository)
 | Establish SWAT Team Involvement in Union Fenosa Reviews (CSC
 | billing system consolidation)
End of June:
 | Resource Assessment (people) for In-House Invoicing |  |  |  |  |  |  |  | [NAME], [NAME]
 | Review Transition of Gas Logistics Process |  |  |  |  |  |  |  | [NAME], [NAME]
 | Finalize Data Requirements for Project Team Ranger
 | -  Design reporting needs for Customer Relationship Group |  |  |  |  |  |  |  | [NAME]
 | -  Design reporting needs for EWS Controls process |  |  |  |  |  |  |  | [NAME]
July:
 | Deal Capture for Settlements/Bill Payment transitioned to EWS |  |  |  |  |  |  |  | Scott
 | Cash Management function (cash forecasting/monitoring) transitioned to EWS |  |  |  |  |  |  |  | [NAME]
 | Develop Settlements/Bill Payment Repository |  |  |  |  |  |  |  | Ranger Project Team
August:
 | Test/Integrate Settlements/Bill Payment Repository |  |  |  |  |  |  |  | Ranger Project Team/IT
September: |  | Note:  Delivery from this point is fully dependent upon
 |  | Data Repository System
 | DPR to Actual Reconciliation in place |  |  |  |  |  |  |  | Scott, [NAME]
 | Accrual Estimates booked using best available information |  |  |  |  |  |  |  | [NAME]
 | In-House settlements for Bundled deals transititioned |  |  |  |  |  |  |  | [NAME], [NAME]
Fourth Quarter:    (to be further defined)
 | Transition remaining Settlements/Bill Payment functions to EWS |  |  |  |  |  |  |  | SWAT Team
 | Establish Service Level Agreements between EES and EWS
Spreadsheet: vkaminski__40727__Storage Model - Intrinsic
Sheet: Discounted Proposal

Used for Basis Analysis: |  | Without Transport |  | Transport Costs | Value
Bayside to Florida Z2 |  | 0.32 |  | 0.12
Bayside to Florida Z3 |  | 0.29
Bayside to Holmesville |  | 0.36
LRC to Holmesville |  | 0.23
Bayside to LRC |  | 0.34
LRC to Barron |  | 0.23
LRC to Florida Z2 |  | 0.19
LRC to Florida Z3 |  | 0.17
 |  |  | avg |  |  | avg / bid
Transportation to Storage
 |  | [NAME]
IT from Bayside |  | 0.08
IT from Carthage |  | 0.07
IT from LRC |  | 0.02
HH |  | 0.01
Transportation from Storage
 |  | [NAME]
FT to Holmesville |  | 0.07 | subject to capacity, submit a service request
FT to [NAME] |  | 0.13
FT to LRC |  | 0.06
FT to Florida Z2 |  | 0.04
FT to Florida Z3 |  | 0.06
FT to [NAME] |  | 0.06
FT to NGPL Goodrich |  | 0.03
IT to Holmesville |  | 0.07
IT to [NAME] |  | 0.13
IT to Trunkline/centervil |  | 0.04 | can get secondary firm if there is enough to displace
IT to LRC |  | 0.06
IT to Florida Z2 |  | 0.04
IT to Florida Z3 |  | 0.06
IT to NGPL - Goodrich |  | 0.03 | depends on pressure & displacement gas
IT to ANR - Centerville |  | 0.04 | by displacment
HH |  | 0.04
Rates open until March 15th
.
FT to Trunkline |  |  | can get secondary firm if there is enough to displace
FT to ANR - Centerville |  |  | deplacment only
Spreadsheet: 794873d1662375613-highlight-when-value-is-higher-than-lowest-cf-higher-value-aligw
Sheet: Sheet1

 |  |  |  | *need to hightight when*
 | Order number | Supplier | Cost | More than 10% higher than the lowest quote recieved?
 | 1 | Greens | 5200
 | 1 | Petes | 4800
 | 1 | Franks | 6200
 | 2 | Mint | 1100
 | 2 | Janets | 1000
 | 2 | Franks | 900
 | 3 | Greens | 600
 | 3 | Petes | 400
 | 3 | Normans | 1000
 | 4 | Petes | 9800
 | 4 | Janets | 8700
 | 4 | Mint | 9500
Spreadsheet: 121431a4-d026-47cb-b27d-5bfa13744f14
Sheet: Sheet1

Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11
 | Time Series Workbook
 | 6202.0 Labour Force, Australia
 | Table 11. Labour force status by Sex - Australian Capital Territory - Trend and Original
Related Information: | Summary Publication | Explanatory Notes | Inquiries
Data Item Description |  |  | Series Type | Series ID | Series Start | Series End | No. Obs. | Unit | Data Type | [NAME]. | Collection Month
Employed - full-time ;  [NAME] ; |  |  | Trend | A184182L | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Employed - full-time ;  Females ; |  |  | Trend | A184199J | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Employed - full-time ;  Persons ; |  |  | Trend | A184216X | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Employed - total ;  [NAME] ; |  |  | Trend | A184184T | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Employed - total ;  Females ; |  |  | Trend | A184201J | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Employed - total ;  Persons ; |  |  | Trend | A184218C | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Unemployed - total ;  [NAME] ; |  |  | Trend | A184187X | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Unemployed - total ;  Females ; |  |  | Trend | A184204R | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Unemployed - total ;  Persons ; |  |  | Trend | A184221T | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Unemployment rate ;  [NAME] ; |  |  | Trend | A184194W | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | Percent | PERCENT | Month | 1
Unemployment rate ;  Females ; |  |  | Trend | A184211L | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | Percent | PERCENT | Month | 1
Unemployment rate ;  Persons ; |  |  | Trend | A184228J | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | Percent | PERCENT | Month | 1
Participation rate ;  [NAME] ; |  |  | Trend | A184193V | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | Percent | PERCENT | Month | 1
Participation rate ;  Females ; |  |  | Trend | A184210K | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | Percent | PERCENT | Month | 1
Participation rate ;  Persons ; |  |  | Trend | A184227F | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | Percent | PERCENT | Month | 1
Civilian population ;  [NAME] ; |  |  | Original | A163537W | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Civilian population ;  Females ; |  |  | Original | A163554X | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
Civilian population ;  Persons ; |  |  | Original | A163571A | 1978-02-01 00:00:00 | 2013-06-01 00:00:00 | 425 | 000 | STOCK | Month | 1
© Commonwealth of Australia  2013
Spreadsheet: 47566d1246611854-fixing-cells-steve-macro-test
Sheet: Monthly_Raw_Data

CPECA Metrics for parameters:
Year to July 2009
 | August | September | October | November | December | January | February | March | April | May | June | July | 12 Months
No. Open | 65 | 59 | 59 | 65 | 68 | 69 | 76 | 76 | 77 | 80 | 82 | 80 | 80
No. Raised | 2 | 5 | 5 | 8 | 9 | 3 | 11 | 7 | 7 | 7 | 5 | 1 | 70
No. Closed | 4 | 11 | 5 | 2 | 6 | 2 | 4 | 7 | 6 | 4 | 3 | 3 | 57
 | 12 | 15 | 12 | 18 | 19 | 13 | 20 | 23 | 24 | 21 | 16 | 14 | 14
 | 16 | 19 | 17 | 22 | 24 | 18 | 24 | 26 | 27 | 27 | 20 | 14 | 14
Generated by [NAME] (STE133) on 03-JUL-2009 09:10:20
via FRACA@YR - V4.18/WEB
Spreadsheet: 715231d1611615837-formula-to-find-rating-as-percentage-of-an-event-test1
Sheet: Sheet1

Track | Race | Horse | Name | Rating | %
Beaudesert | 1 | 1 | Boom Spender | 27.11032223989784
Beaudesert | 1 | 4 | Mishani Machine | 11.77650818528767
Beaudesert | 1 | 2 | Treasure Of Gold | 15.54856853673059
Beaudesert | 1 | 5 | Angels Share | 6.468126347390993
Beaudesert | 1 | 3 | Atlantic Royal | 7.437343662898629
Beaudesert | 1 | 7 | The Red Dancer | 3.362037280464563
Beaudesert | 1 | 6 | Comfortably Numb | 3.280144055041311
Beaudesert | 2 | 1 | Krakatoa Eruption | 19.2828705878425
Beaudesert | 2 | 2 | Underwood | 21.16556219717715
Beaudesert | 2 | 7 | Dances With Time | 6.350569920168149
Beaudesert | 2 | 3 | [NAME] | 4.055406676788428
Beaudesert | 2 | 6 | See For Yourself | 8.824768949473285
Beaudesert | 2 | 4 | [NAME] | 5.659309808827339
Beaudesert | 2 | 5 | Cossie | 5.186021712608563
Beaudesert | 2 | 8 | [NAME] | 2.295733606629541
Beaudesert | 3 | 2 | Call Me Legend | 10.67400172673173
Beaudesert | 3 | 8 | Our Rocky | 10.42019316272496
Beaudesert | 3 | 3 | [NAME] | 5.138584443120744
Beaudesert | 3 | 9 | Special Case | 5.019598215996597
Beaudesert | 3 | 7 | Zeeluck | 1.972080891467511
Beaudesert | 3 | 5 | Zoomfield | 4.304255538252543
Beaudesert | 3 | 6 | Aonach Mor | 6.046698037397281
Beaudesert | 3 | 1 | Rocky Nugget | 3.473854124854674
Beaudesert | 3 | 4 | Solid Asset | 4.111531676578005
Beaudesert | 5 | 7 | Lope For Joya | 6.932244768536658
Beaudesert | 5 | 3 | Biggie | 7.214090432618449
Beaudesert | 5 | 1 | Cleo's Poet | 8.715202531616871
Beaudesert | 5 | 11 | Qualify | 5.40225502320712
Beaudesert | 5 | 6 | Supervisor | 7.248604886409905
Beaudesert | 5 | 2 | Prince Of Arran | 5.434975060532873
Beaudesert | 5 | 8 | Just Wishing | 2.378149295569891
Beaudesert | 5 | 5 | Pole Volter | 2.123100651495353
Beaudesert | 5 | 4 | [NAME] | 2.054806379601059
Spreadsheet: tracy_geaccone__40256__C0366-112458
Sheet: Dept_100182

ENRON PROPERTY & SERVICES CORP
1400 Smith Street
Houston, Texas   77251 - 1188
MONTHLY INVOICE DETAIL
BUILDING SERVICES - Dept. 100182
May, 2001
0366  EOC - Executive
Date | Ticket Number | Work Order | Service Name | Service Description | # [NAME] | Unit Cost | UOM | Service | Ticket
 |  |  |  |  |  |  |  | Total | Total
 |  |  | R/C 112458
2001-05-29 00:00:00 | BSV100182-2001-05-0366-112458 |  | Building Services | Sq. Ft. Occupied | 90 | 0.03 | /EACH | 2.7
 |  |  |  |  |  |  | Ticket Total |  | 2.7
 |  |  |  |  |  |  | Total for R/C: 112458 |  | 2.7
 |  |  |  |  |  |  | Total for Dept_100182 |  | 2.7
Spreadsheet: 682412d1592072227-find-date-of-which-employee-satisfies-two-conditions-sample-2
Sheet: BONOUS

CODE | NET LIVES | NET FP | DATE(YYYYMMDD) |  |  | CHANGE DATE
 |  |  |  |  |  | AS REQUIRED
192 |  |  | NOT ELIGIBLE |  |  | 20190401 | 20200331
727 |  |  | 20200128
888 |  |  | 20190604
 |  |  |  |  |  | PROJECT : IF AN EMPLOYEE (SHOWN IN SHEET NBD COLUMN "EM-CODE) COMPLETES SIX UNIQUE ORDERS(UNIQUE ORDER MEANS: ' ORDER-NO' WITH UNIQUE' DOB')
 |  |  |  |  |  | AND SUM OF ALL ORDER PREM IS >=50000 IN GIVEN DATE RANGE .EMPLOYEE IS ELIGIBLW FOR BONUS FROM DATE ON WHICH HE SATISFIES TWO CONDITIONS.
 |  |  |  |  |  | SOLUTION REQUIRED : I WANT TO DISPLAY DATE IN COL. [NAME] YYYYMMDD FORMAT(AS SHOWN IN COL. 'C' OF 'NBD' SHEET) ON WHICH EMPLOYEE SATISFY ABOVE MENTIONED
 |  |  |  |  |  | TWO CONDITIONS. SO THAT I CAN CALCULATE BONOUS FROM THAT DATE.
 |  |  |  |  |  | EXPECTED RESULT IS GIVEN IN COL. D
Spreadsheet: 41191d1239713074-working-with-lookups-lookup-macro
Sheet: Bank Statement

Day 1
Date | Ref |  | Amount  Received
2009-04-14 00:00:00 | TW000927 |  | 371
2009-04-14 00:00:00 | N0000031 |  | 82.48
2009-04-14 00:00:00 | JS000014 |  | 71.74
2009-04-14 00:00:00 | PW00005 |  | 46.64
2009-04-14 00:00:00 | Y1111119 |  | 31.05
2009-04-14 00:00:00 | Y1111118 |  | 29.02
Day 2
Date | Ref |  | Amount  Received
2009-04-15 00:00:00 | TW000927 |  | 96.8
2009-04-15 00:00:00 | N0000031 |  | 82.48
2009-04-15 00:00:00 | JS000014 |  | 71.74
2009-04-15 00:00:00 | PW00005 |  | 46.64
2009-04-15 00:00:00 | Y1111119 |  | 31.05
2009-04-15 00:00:00 | Y1111118 |  | 29.02
Spreadsheet: 23591575-02b6-405d-be1e-5139a73abeeb
Sheet: Sheet1

Table 10--U.S. oilseed meal prices | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6
Marketing | Soybean | Cottonseed | Sunflowerseed | Peanut | Canola | Linseed
year | meal 2 | meal 3 | meal 4 | meal 5 | meal 6 | meal 7
 | $/Short ton
2001/02 | 167.72 | 136.16 | 87.27 | 112.32 | 143.33 | 121.29
2002/03 | 181.58 | 146.12 | 105 | 128.35 | 144.06 | 122.91
2003/04 | 256.05 | 183.47 | 111.14 | 177.56 | 188.45 | 159.25
2004/05 | 182.9 | 124.04 | 85.5 | 118.34 | 139.75 | 115.55
2005/06 | 174.1658333333333 | 144.2716666666667 | 77.46400000000001 | 106.9781818181818 | 140.5183333333333 | 115.53
2006/07 | 205.44 | 150.36 | 104.88 | 100 | 173.5 | 133.01
2007/08 | 335.94 | 253.81 | 172.81 |  | 251.32 | 228.81
2008/09 | 331.17 | 255.23 | 152.46 |  | 248.82 | 220.89
2009/10 | 311.27 | 220.9 | 151.04 |  | 224.92 | 209.23
2010/11 | 345.52 | 273.84 | 219.72 |  | 263.63 | 240.65
2011/12 | 393.53 | 275.13 | 246.75 |  | 307.59 | 265.68
2012/131 | 440-470 | 345-375 | 250-280 |  | 330-360 | 285-315
2011/12
October | 301.45 | 255.63 | 232.5 |  | 238.7 | 243.75
November | 290.37 | 240.5 | 224 |  | 235.2 | 239
December | 281.65 | 220.63 | 225.63 |  |  | 221.25
January | 310.65 | 213 | 223.5 |  | 253.98 | 209
February | 330.37 | 190 | 191.88 |  | 257.63 | 193.75
March | 365.95 | 225 | 191.88 |  | 277.83 | 216.25
April | 394.29 | 240.63 | 211.25 |  | 313.38 | 256.25
May | 415.17 | 270 | 230.5 |  | 333.69 | 279
June | 422.59 | 294.38 | 226.88 |  | 335.26 | 287.5
July | 515.82 | 350.5 | 300.5 |  | 378.86 | 343
August | 564.69 | 407.5 | 348.13 |  | 388.13 | 358.75
September | 529.37 | 393.75 | 354.38 |  | 370.79 | 340.63
2012/13
October | 488.46 | 343 | 287 |  | 354.49 | 334
November1 | 466.16 | 376.88 | 269.38 |  | 334.46 | 297.5
1 Preliminary.  2 High-protein Decatur, IL.  3 41-percent Memphis.  4 34-percent North Dakota-Minnesota.
5 50-percent Southeast mills.  6 36-percent Pacific Northwest. 7 34-percent Minneapolis.
NA= Not available.
Source: USDA, Agricultural Marketing Service, Monthly Feedstuff Prices.
Last update: | 2012-12-17 14:09:32.390000
Spreadsheet: 001491cf-5c91-421b-b844-b42a251358bc
Sheet: Sheet1

Table 3--U.S. monthly average farm prices and marketings | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8 | Unnamed: 9 | Unnamed: 10 | Unnamed: 11 | Unnamed: 12
 |  |  | 2011/12 |  |  |  | 2010/11 |  |  |  | 2009/10
Month |  | $/cwt |  | 1,000 cwt |  | $/cwt |  | 1,000 cwt |  | $/cwt |  | 1,000 cwt
August |  | 13.6 |  | 11384 |  | 11.7 |  | 10691 |  | 14.9 |  | 10503
September |  | 14.4 |  | 12507 |  | 11 |  | 15095 |  | 14.8 |  | 11061
October |  | 14.5 |  | 11454 |  | 11.5 |  | 17131 |  | 14.5 |  | 14148
November |  | 14.5 |  | 10928 |  | 12.6 |  | 16929 |  | 14.5 |  | 13854
December |  | 14.2 |  | 14177 |  | 13.9 |  | 19137 |  | 14.9 |  | 15902
January |  | 14.2 |  | 14422 |  | 14 |  | 21806 |  | 15 |  | 19351
February |  | 13.8 |  | 13187 |  | 13.4 |  | 14847 |  | 14.8 |  | 13991
March |  | 13.6 |  | 14165 |  | 13 |  | 15844 |  | 14.3 |  | 16292
April |  | 13.9 |  | 13396 |  | 13.1 |  | 12727 |  | 14.3 |  | 12525
May |  | 13.8 |  | 13212 |  | 12.7 |  | 14227 |  | 13.8 |  | 13145
June |  | 13.8 | 1/ |  |  | 12.1 |  | 11317 |  | 13.2 |  | 11019
July |  |  |  |  |  | 12.9 |  | 13369 |  | 12.6 |  | 11952
Average price to date |  | 14.02727272727273 | 2/
Season average farm price |  | 14.1 | 3/ |  |  | 12.7 |  |  |  | 14.4
Average Marketings |  |  |  | 12883.2 |  |  |  | 15260 |  |  |  | 13645.25
Total volume marketed |  |  |  | 128832 |  |  |  | 183120 |  |  |  | 163743
N/A = Not available.
1/ Mid-month only.  2/ Simple average.  3/ Mid-point of season-average farm price projection.
Source: Monthly cash price and marketings, Agricultural Prices, National Agricultural Statistics Service, USDA.
Last updated July 11, 2012.
Spreadsheet: 799864d1665374466-sum-of-wins-and-sum-of-losses-kenya-masters-sc
Sheet: Sheet2

1 | 1 | 21 | 1512 | Magu, [NAME] | 0 | 1 | 446 | 457 | -11
2 | 1 | 32 | 1219 | [NAME], [NAME] | 1 | 1 | 501 | 363 | 138
3 | 9 | 7 | 1642 | Magwanga, Onani | 2 | 1 | 451 | 259 | 192
4 | 6 | 23 | 1490 | Mbugua, [NAME] | 3 | 1 | 446 | 373 | 73
5 | 3 | 16 | 1558 | [NAME], [NAME] | 4 | 1 | 433 | 419 | 14
6 | 2 | 19 | 1538 | [NAME], [NAME] | 5 | 1 | 472 | 336 | 136
7 | 2 | 4 | 1824 | [NAME], [NAME] | 6 | 1 | 385 | 379 | 6
8 | 1 | 2 | 1852 | Nderitu, Gitonga | 6 | 2 | 439 | 456 | -17
9 | 2 | 14 | 1570 | Ochola, [NAME] | 6 | 3 | 333 | 408 | -75
10 | 3 | 25 | 1465 | [NAME], [NAME] | 6 | 4 | 367 | 427 | -60
11 | 3 | 10 | 1611 | Amuke, Benard | 7 | 4 | 429 | 392 | 37
12 | 2 | 11 | 1589 | Otieno, Manase | 8 | 4 | 515 | 385 | 130
13 | 3 | 10 | 1611 | Amuke, Benard*2 | 8 | 5 | 418 | 420 | -2
14 | 1 | 2 | 1852 | Nderitu, Gitonga*2 | 9 | 5 | 476 | 442 | 34
15 | 3 | 7 | 1642 | Magwanga, Onani*2 | 9 | 6 | 408 | 516 | -108
16 | 1 | 14 | 1570 | Ochola, Tony*2 | 10 | 6 | 464 | 338 | 126
17 | 2 | 25 | 1465 | [NAME], Philip*2 | 11 | 6 | 487 | 447 | 40
18 | 4 | 20 | 1524 | Okechi, [NAME] | 12 | 6 | 569 | 269 | 300
19 | 4 | 19 | 1538 | [NAME], Alvin*2 | 12 | 7 | 351 | 520 | -169
20 | 2 | 12 | 1571 | [NAME], [NAME] | 13 | 7 | 519 | 305 | 214
21 | 4 | 8 | 1634 | Outi, Tobias | 14 | 7 | 474 | 316 | 158
22 | 3 | 6 | 1769 | Mutahi, Martins | 15 | 7 | 496 | 384 | 112
Spreadsheet: 769532d1645579581-determine-smallest-reel-possible-for-x-meters-of-cable-smallest-reel-possible-for-meters-rev2
Sheet: Problem

Product X | Reel sizes in inches L x W | Lengths in meters
 |  | 100 | 150 | 200 | 250 | 300 | 350 | 400 | 450 | 500 | 550 | 600 | 650 | 700 | 750 | 800 | 850 | 900 | 950 | 1000 | 1050 | 1100 | 1150 | 1200 | 1250 | 1300 | 1350 | 1400 | 1450 | 1500 | 1550 | 1600 | 1650 | 1700 | 1750 | 1800 | 1850 | 1900 | 1950 | 2000 | 2100 | 2150 | 2200
 | 36x27.5 | 4 | 5 | 1 | 9 | 38
 | 40x27 |  |  | 1 | 34 | 64
 | 42x30 |  |  |  | 3 | 3
 | 44x27 | 2 | 1 | 2 | 1 | 1 | 1 | 1
 | 45x32 |  | 1 |  |  | 1 |  |  |  | 1
 | 50x38 |  | 1 |  | 2 | 1 | 11 | 7 | 10 | 7 | 3 | 15 | 5 | 4 | 1 | 1
 | 56x38 |  |  |  |  | 1 |  |  |  | 1 | 1 | 1 | 3 | 7 | 3 | 6 | 6 | 6 | 4 | 1 |  | 1
 | 60x38 |  |  |  |  |  |  |  |  |  | 1 |  |  |  | 2 | 2 |  | 1 | 2 | 5 | 6 | 2 | 2 | 1 |  | 1
 | 68x38 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 2 | 1 | 4 | 4 | 3 | 3 | 8 | 6 | 16 | 6 | 2 | 1 |  | 1 |  | 1
 | 72x39 |  |  |  |  |  |  |  |  |  |  | 1 |  |  |  |  |  | 1 | 1 | 2 | 4 | 10 | 4 | 3 | 4 | 1 | 1 | 1 | 2 | 4 | 4 |  | 1 |  | 1
 | 72x42 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 1 | 2 | 1 |  |  |  |  | 1 |  |  | 1
 | 78x39 |  | 1 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 2 |  |  |  | 4 | 9 | 3 | 2 | 3 | 4 | 4 | 2 | 2 | 4 | 4
 | 84x43 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 1 |  |  | 1 |  | 1 | 1 |  |  |  |  |  | 2 | 2 | 3 | 1 | 3
 | 96x47 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 1 |  |  | 1
 | 100x76 |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 1
1 | 300 meters of product X were put on a reel as small as 36x27.5
2 | 300 meters of product X were put on 40, 42, 44, 45, 50, 56, 78 in reels when they could have been put on 36 in reels
3 | All the red numbers could have been put on a smaller reel
4 | On tab Raw data, how do I determine the smallest reel on which the meters on that transaction for that transaction product could have been put on?
 | This is the general problem, it would look like this when solved:
5 | In Raw data tab in cell E2 I need a formula that:
 | Looks up material 24002854 (Raw data, cell B2) in Pivot tab
 | Checks if 300 (Raw data, cell C2) is smaller than
 |  | Pivot, cell C1414
 |  | If not check if 300 is smaller than C1413
 |  | If not check if 300 is smaller than C1412
 |  | If not check if 300 is smaller than C1411
 |  | If not check if 300 is smaller than C1410
 |  | If not check if 300 is smaller than C1409
 | 300 is larger than 150 so the result for this row's formula is 24x11
Spreadsheet: 139263d1328160709-how-do-i-anchor-sums-from-columns-bond-anchor-sum
Sheet: Sheet1

2.5 | 0.5 | 101 | 0.99753 | Helper
3.5 | 1 | 101.01
1.4 | 1.5 | 100
3.5 | 2 | 98
2.3 | 2.5 | 98.2
1.9 | 3 | 97
1 | 3.5 | 96
2.3 | 4 | 96.4
Spreadsheet: 93031d1292528133-concatenate-unique-entries-from-cells-concatenating-unique-entries-v3
Sheet: Sheet1

Santa's Route |  |  |  | NORTH POLE ELVES (1)
[NAME] |  |  |  | [NAME] (3)
fgfg |  |  |  | Mom (3)
Mom |  |  |  | Brother (3)
fgfg |  |  |  | [NAME] (3)
Brother |  |  |  | Sister (3)
fsfwef
gggfg
rtetyey
oipo
[NAME]
oiuo
dgdrt
hnbhn
nvbnv
Sister
[NAME] OF CHRISTMAS CARD COPIES
NORTH POLE ELVES (1) [NAME] (3) Mom (3) Brother (3) [NAME] (3) Sister (3) |  |  |  |  | NORTH POLE ELVES (1) [NAME] (3) Mom (3) Brother (3) Sister (3)
Spreadsheet: 700369d1603166412-need-help-to-have-formula-to-ignore-cells-with-no-dates-multiple-cells-in-a-row-playing-with-timeline
Sheet: Condo FHA (tbd)

Subject | Start Date | All day event | Reminder on/off | Meeting Organizer | Categories | Description | Location
Sign Disclosures |  | True | False | Team Rosa | Sign Disclosures
[NAME] |  | True | False | Team Rosa | [NAME]
[NAME] |  | True | False | Team Rosa | [NAME]
Processing Review |  | True | False | Team Rosa | Processing Review
[NAME] |  | True | False | Team Rosa | [NAME] | _x000D_
Submit Conditions |  | True | False | Team Rosa | Submit Conditions
CD |  | True | False | Team Rosa | CD
Final Approval |  | True | False | Team Rosa | Final Approval
[NAME] CD |  | True | False | Team Rosa | [NAME] CD
Docs Out |  | True | False | Team Rosa | Docs Out
Signing |  | True | False | Team Rosa | Signing
Close Date |  | True | False | Team Rosa | Close Date
KOT Resubmit |  | True | False | Team Rosa | KOT Resubmit
Spreadsheet: 152983d1335661216-excel-2007-conditional-formatting-with-2-variables-rick52-1-
Sheet: Sheet1

nex |  |  |  |  |  | a | a | b | b | c | d
0.191 | 0.1636 |  |  |  | bio | 0.21 | 0.22 | 0.15 | 0.16 | 0.01 | 0.01
a | b |  |  |  | coa | 0.21 | 0.22 | 0.15 | 0.16 | 0.02 | 0.3
0.0532 | 0.0511 |  |  |  | dyn | 0.185 | 0.195 | 0.2 | 0.21 | 0.02 | 0.02
c | d |  |  |  | nex | 0.19 | 0.2 | 0.15 | 0.18 | 0.01 | 0.05
A2 Text populated from drop down based in F3:F6
A3 & B3 manually populated
A5 & B5 manually populated
[NAME] (Green or Red) based on [NAME],                             B4 based on B3  etc.
The problem comes in with overlapping or differing values depending on product (from drop down) being produced.
How can the drop down or something else be used to select the values that trigger the format?
 |  |  |  |  |  | a |  | b |  | c | d
 |  |  |  |  | bio | > 21 < 22 |  | > 15 < 16 |  | < 1 | < 1
 |  |  |  |  | coa | > 21 < 22 |  | > 15 < 16 |  | < 2 | < 30
 |  |  |  |  | dyn | > 18.5 < 19.5 |  | > 20 < 21 |  | < 2 | < 2
 |  |  |  |  | nex | > 19 < 20 |  | > 15 < 18 |  | < 1 | < 5
bio
coa
dyn
nex
Spreadsheet: 102f07ff-c8e8-4c16-ba64-893d1b8e5ac3
Sheet: Sheet1

PROG ID | ATHLETE ID | ATHLETE FIRST | ATHLETE LAST | NATIONALITY | START NUMBER | SWIM | T1 | BIKE | T2 | RUN | POSITION | TOTAL TIME
259405 | 22881 | Francesco | Fiori | ITA | 1421 | 00:38:43 | 00:01:22 | 01:11:48 | 00:01:38 | 00:50:15 | 1 | 02:42:06
Spreadsheet: 870701d1717166549-create-summary-page-using-formula-from-multiple-sheets-data-are-in-different-columns-on-ea-sample-file
Sheet: Sheet2

 |  | Name | Price B | Price C | Price A
 |  | Brand1 | 2 | 4 | 6
 |  | Brand4 | 7 | 8 | 9
 |  | Brand3 | 12 | 12 | 12
 |  | Brand7 | 17 | 16 | 15
 |  | Brand6 | 22 | 20 | 18
 |  | Brand1 | 27 | 24 | 21
 |  | Brand5 | 32 | 28 | 24
 |  | Brand2 | 37 | 32 | 27
 |  | Brand7 | 42 | 36 | 30
Spreadsheet: f0378c6e-606f-4d3a-b854-4d5c4c0bc5fb
Sheet: Sheet1

Battle ID | Date & Time | Side | Victory | Score | Opp. Score | Opp. ID | Opp. Name | Scenario ID | Front | Start Date | End Date | Name EN | Name FR
43918501 | 2014-11-07 17:45:58 | ALLIES | 1 | 5 | 3 | 1661331 | [NAME] | 12499 | western | 1944-06-13 | 1944-06-13 | Carentan | Carentan
43918671 | 2014-11-07 17:59:09 | AXIS | 0 | 3 | 5 | 1661331 | [NAME] | 12499 | western | 1944-06-13 | 1944-06-13 | Carentan | Carentan
43933059 | 2014-11-08 17:52:24 | AXIS | 0 | 6 | 7 | 716096 | widetowest | 7668 | pacific | 1945-02-26 | 1945-03-06 | The Meat Grinder | Le Hachoir à viande
43933313 | 2014-11-08 18:05:58 | ALLIES | 0 | 4 | 7 | 716096 | widetowest | 7668 | pacific | 1945-02-26 | 1945-03-06 | The Meat Grinder | Le Hachoir à viande
44052193 | 2014-11-16 19:11:18 | AXIS | 1 | 8 | 4 | 946197 | Groka | 7661 | eastern | 1944-02-16 | 1944-02-17 | Breakout to Lisyanka | [NAME]