tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 56443d1256639963-not-a-duplicate-post-excel
Sheet: Sheet1

DATE OF READ | M1 Old | M1 New2 | [NAME] | M2 New | Usage | [NAME] of Days | Scenario
2008-06-30 00:00:00 | 030728 | 030923 | 109758 | 109758 | 195 | 5 |  |  |  |  |  |  |  |  |  |  | 195 | 5
2008-09-01 00:00:00 | 030923 | 031172 | 109758 | 110672 | 1163 | 63 |  |  |  |  |  |  |  |  |  |  | 1163 | 63
2008-11-14 00:00:00 | 031172 | 031386 | 110672 | 111632 | 1174 | # from last read
2008-12-11 00:00:00 | 031386 | 0 | 111632 | 111632 | ERROR | 0
2008-12-11 00:00:00 | 0 | 000066 | 111632 | 112188 | 622 |  | Sometimes we have more than 1 reads for each site.  So, if New Read is '0' (cell C5) or New Read is equal to Old Read (cell E5 and D5), then there's something wrong, thus the 'ERROR' alert (F5).  But there's a twist to the plot:  sometimes, the original '0' New Read is revised on the same day (see cell A6 - notice that date of read is also on 2008-12-11).  While this is good, however it messes up the number of days from last read because now, A6-A5 = 0, when really, I'd like it to count from the last actual date, which in this case is A4 (2008-11-14).
Spreadsheet: bf4cac9b-a109-4f5f-b8b7-3293b31a811a
Sheet: Sheet1

Government and Public Administration | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5
Pathway: Planning
Code | Topic | Course | Knowledge and Skill | Performance Element | Measurement Criteria
GVPD01.01 | Planning Implementation | #6: Fundamentals of Planning | Coordinate complex planning to permit coherent integration of plans. | Implement plans to achieve benefits of investment in planning. | Secure approval of integrated plans.
 |  |  |  |  | Monitor plan progress.
 |  |  |  |  | Monitor plan budgets.
 |  |  |  | Articulate progress to various audiences to sustain support for present activities and future plans. | Respond to citizen and official requests for information.
 |  |  |  |  | Impress public with effective, cogent presentation skills at public meetings.
 |  |  |  |  | Maintain professional temperament in volatile or difficult situations.
GVPD02.01 | Strategic Planning | #7: Strategic Planning | Develop comprehensive plans for land use, housing, parks and recreation, transportation, economic development, and public facilities to manage change. | Partner with citizens, interest groups, and public officials to develop a vision and generate quality-of-life standards and plans. | Identify emerging trends and issues.
 |  |  |  |  | Collect, analyze, organize, and interpret data.
 |  |  |  |  | Analyze problems and compare options.
 |  |  |  |  | Model alternatives.
 |  |  |  |  | Identify barriers to implementation.
 |  |  |  |  | Identify strategies for reaching goals.
 |  |  |  |  | Develop tactics for overcoming barriers.
Code | Topic | Course | Knowledge and Skill | Performance Element | Measurement Criteria
GVPD03.01 | Planning | #8: Policy and Plan Development | Formulate plans and policies to meet social, economic, and physical needs. | Apply planning principles to make job, growth, population, and revenue projections. | Manage and modify planning process.
 |  |  |  |  | Relate physical design to functioning of environment.
 |  |  |  |  | Generate data on present and future needs.
 |  |  |  |  | Analyze legal aspects of planning, including regulatory processes and techniques.
 |  |  |  |  | Assist with drafting, analysis, and refinement of regulations and procedures.
 |  |  |  |  | Prepare special planning studies.
 |  |  |  |  | Perform mapping and graphic functions.
 |  |  |  |  | Continually update processes.
 |  |  |  | Analyze environment to forecast demographic and technological change. | Predict the interaction between economy, transportation, health and human services, and land regulation, and make recommendations for the future.
 |  |  |  |  | Interpret and analyze local, state, and federal governments' programs and processes and make recommendations for the future.
 |  | Additional Recommended CTE/Degree Major Course:
 |  | #9: Principles of Management
Spreadsheet: 854248d1704199100-constant-lookup-to-table-based-on-fixed-values-example
Sheet: Sheet1

1 | A |  | Values in order |  |  |  |  | Second example: |  | 1 | B |  | Values in order
3 | A |  | A | 10 |  |  |  |  |  | 3 | B |  | B | 5
4 | A |  | B | 5 |  |  |  |  |  | 4 | B |  | A | 10
5 | A |  | C | 15 |  |  |  |  |  | 5 | B |  | C | 15
9 | A |  | D | 20 |  |  |  |  |  | 9 | A |  | D | 20
12 | A |  |  |  |  |  |  |  |  | 12 | A
1 | B |  |  |  |  |  |  |  |  | 1 | C
3 | B |  |  |  |  |  |  |  |  | 3 | C
5 | B |  |  |  |  |  |  |  |  | 5 | C
1 | C |  |  |  |  |  |  |  |  | 1 | C
6 | C |  |  |  |  |  |  |  |  | 6 | C
10 | C |  |  |  |  |  |  |  |  | 10 | C
16 | C |  |  |  |  |  |  |  |  | 16 | C
1 | D |  |  |  |  |  |  |  |  | 1 | D
8 | D |  |  |  |  |  |  |  |  | 8 | D
14 | D |  |  |  |  |  |  |  |  | 14 | D
20 | D |  |  |  |  |  |  |  |  | 20 | D
Spreadsheet: d9ef8da6-736e-41a5-85ca-e04de874ca45
Sheet: Sheet1

PROG ID | ATHLETE ID | ATHLETE FIRST | ATHLETE LAST | NATIONALITY | START NUMBER | SWIM | T1 | BIKE | T2 | RUN | POSITION | TOTAL TIME
7134 | 32629 | [NAME] | Reinertsen | USA | 43 | 00:22:38 | 00:02:37 | 00:47:42 | 00:01:31 | 00:32:23 | 1 | 01:46:49
7134 | 25135 | Kimberly | Fawcett | CAN | 42 | 00:21:36 | 00:05:00 | 00:53:43 | 00:03:44 | 00:53:43 | DNF | 00:00:00
Spreadsheet: 265f7280-394d-4995-9645-80c07b8aacc0
Sheet: Sheet1

Table 11--Annual U.S. cotton textile imports, by origin | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5
Region/Country | 2007 | 2008 | 2009 | 2010 | 2011
 | 1,000 pounds 1/
North America | 2312683.354 | 2169532.063 | 1752268.691 | 1897981.252 | 1839260.388
Canada | 89285.328 | 48649.279 | 38383.267 | 40102.826 | 39566.079
Dominican Republic | 107750.187 | 89928.068 | 58253.755 | 70737.456 | 70749.982
El Salvador | 263194.584 | 279668.016 | 214039.581 | 284027.799 | 258273.283
Guatemala | 176196.646 | 155535.291 | 120311.952 | 117611.836 | 106356.451
Haiti | 134576.011 | 149593.833 | 152776.815 | 134560.382 | 153954.947
Honduras | 514356.077 | 518807.982 | 379460.356 | 429475.609 | 412000.914
Mexico | 761314.736 | 702448.85 | 606024.628 | 628593.794 | 581538.46
Nicaragua | 182792.077 | 179717.897 | 162116.45 | 178477.271 | 206972.871
South America | 182289.592 | 141806.511 | 101403.136 | 101270.632 | 59297.928
Brazil | 84912.982 | 62392.823 | 43049.712 | 33025.66 | 3740
Europe | 278516.579 | 202577.22 | 141718.022 | 162047.399 | 138968.116
Turkey | 129292.754 | 86904.991 | 61121.95 | 69723.489 | 55340.355
Asia | 7280605.254 | 7015492.94 | 6575825.289 | 7461471.365 | 6319352.65
Bangladesh | 510465.422 | 560124.211 | 560584.468 | 628251.215 | 537171.551
Cambodia | 299526.863 | 296773.941 | 235474.417 | 272887.855 | 251638.773
China | 2641761.873 | 2578543.62 | 2765618.264 | 3310554.881 | 2744997.411
Hong Kong | 194610.257 | 140912.95 | 22510.009 | 16631.232 | 12502
India | 757286.32 | 744687.614 | 701018.401 | 788224.209 | 687902.267
Indonesia | 342794.253 | 342070.012 | 329743.286 | 380900.376 | 352378.798
Jordan | 118388.383 | 89297.266 | 67083.454 | 60885.342 | 47887.584
Malaysia | 72969.4 | 62538.739 | 37918.575 | 36599.776 | 33247.917
Pakistan | 1003788.047 | 931327.126 | 861342.994 | 926190.813 | 766688.088
Philippines | 147229.461 | 109217.282 | 75547.747 | 71523.763 | 66282.618
South Korea | 113879.907 | 103623.61 | 84261.68 | 85824.311 | 68853.349
Sri Lanka | 139786.041 | 123202.548 | 95187.223 | 89766.376 | 79228.125
Taiwan | 96065.442 | 69292.206 | 42468.267 | 40850.395 | 33075.114
Thailand | 194044.59 | 181183.751 | 129150.031 | 133696.412 | 97246.821
Vietnam | 404101.208 | 476834.959 | 475608.302 | 537729.121 | 474707.541
Africa | 329007.994 | 297827.996 | 248588.066 | 237930.183 | 203404.376
Egypt | 132914.098 | 133823.781 | 121209.143 | 130666.467 | 108008.972
[NAME] | 66428.825 | 58093.464 | 44977.808 | 44154.37 | 39588.149
World 2/ | 10385843.593 | 9829113.23 | 8820811.807 | 9861620.816 | 8564312.404
1/ Raw-fiber equivalent.  2/ Totals may not add due to rounding.
Last update: 03/12/12.
Sources: USDA, Economic Research Service; and USDC,
U.S. Census Bureau.
Spreadsheet: 468354d1467245585-a-special-sorting-dataset-in-excel-sample-dataset
Sheet: Sheet1

Original dataset |  |  |  |  |  |  |  |  |  | Desired dataset
ID | sample 1 |  | ID | sample 2 |  | ID | sample 3 |  |  | ID | sample 1 | sample 2 | sample 3
AT5G60360.1 | -7.34966714696372 |  | AT3G19390.1 | 12.25 |  | AT5G60360.1 | 13 |  |  | AT3G19390.1 | 0 | 12.25 | 0
AT5G01600.1 | -6.87629790384096 |  | AT1G09850.1 | -7.5 |  | AT1G09850.1 | 4.7 |  |  | AT5G60360.1 | -7.34966714696372 | 0 | 13
AT3G48340.1 | -6.65757868746952 |  | AT5G01600.1 | 4.32 |  | AT3G48340.1 | 1.2 |  |  | AT1G09850.1 | 0 | -7.5 | 4.7
AT5G60770.1 | -6.56786891200627 |  | AT5G01600.1 | 5.2 |  | AT1G49320.1 | 1.5 |  |  | AT5G01600.1 | -6.87629790384096 | 4.76 | 0
AT1G49320.1 | -7.82321330479893 |  | AT5G60770.1 | 7.3 |  |  |  |  |  | AT3G48340.1 | -6.65757868746952 | 0 | 1.2
AT2G24200.3 | 2.3 |  |  |  |  |  |  |  |  | AT5G60770.1 | -6.56786891200627 | 7.3 | 0
 |  |  |  |  |  |  |  |  |  | AT1G49320.1 | -7.82321330479893 | 0 | 1.5
 |  |  |  |  |  |  |  |  |  | AT2G24200.3 | 2.3 | 0 | 0
Spreadsheet: 680227d1590863695-my-sumifs-isnt-working-travel-card-reconciliation
Sheet: Sheet2

Category | Column1
Com Carrier - 2110
Fuel Cost - 2140
Laundry - 2140
Lodging - 2120
PerDiem - 2135
Mileage - 2113
Misc - 2140
Rental Car - 2111
Fees -2136
Transportation -2140
Voucher Fees -2137
Lodging Tax - 2130
 | Baggage
Spreadsheet: darrell_schoolcraft__7335__imball0203
Sheet: Dominion

 | Dominion Gas Ventures
 | POI 12354 - Dominion Gas/Tule Lake
Day | Measured | Scheduled
1 | 260 | 78
2 | 65 | 145
3 | 178 | 145
4 | 158 | 150
5 | 173 | 150
6 | 294 | 150
7 | 186 | 150
8 | 183 | 150
9 | 35 | 78
10 | 180 | 78
11 | 148 | 78
12 | 6 | 150
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
2002-02-28 00:00:00 |  |  | 172008.39
2002-03-12 00:00:00
CBS Volume
 |  |  | 75165
Spreadsheet: 652895d1575622529-equation-for-averaging-cells-that-include-signs-new-excel-help-1-
Sheet: Sheet1

 |  |  |  |  | Final D.O. must be >1mg/L | Depletion must be >2mg/L
Sample ID | Bottle # | ml's Sample Used | Seed Volume (mL) | Initial D.O. | Final D.O. | Depletion | Seed Correction Factor | Net Depletion | Sign | Result (mg/L)
North | 56 | 5 |  | 8.24 | 6.8
 | 57 | 10 |  | 8 | 6.8
 | 58 | 15 |  | 8 | 6.8
 |  |  |  |  |  |  | North Average:
South | 57 | 5 |  | 8.27 | 6.28
 | 59 | 10 |  | 8.15 | 6.28
 | 58 | 15 |  | 8.26 | 6.28
 |  |  |  |  |  |  | South Average:
South Duplicate | 57 | 5 |  | 8.27 | 6.3
 | 59 | 10 |  | 8.15 | 6.3
 | 60 | 15 |  | 8.29 | 6.3
 |  |  |  |  |  |  | South Dup. Average:
 |  |  |  |  | Combined Average of South & DUP:
 |  |  |  |  |  | Duplicate % Recovery (<20%):
 |  |  |  |  | Combined Average of North & South:
 |  |  |  |  | Final D.O. must be >1mg/L | Depletion must be >2mg/L
Sample ID | Bottle # | ml's Sample Used | Seed Volume (mL) | Initial D.O. | Final D.O. | Depletion | Seed Correction Factor | Net Depletion |  | Result (mg/L)
North | 56 | 5 |  | 8.24 | 2
 | 57 | 10 |  | 8 | 0.5
 | 58 | 15 |  | 8 | 1
 |  |  |  |  |  |  | North Average:
South | 57 | 5 |  | 8.27 | 0.8
 | 59 | 10 |  | 8.15 | 0.8
 | 58 | 15 |  | 8.26 | 0.8
 |  |  |  |  |  |  | South Average:
South Dup. | 57 | 5 |  | 8.27 | 0.65
 | 59 | 10 |  | 8.15 | 0.66
 | 60 | 15 |  | 8.29 | 0.63
 |  |  |  |  |  |  | South Dup. Average:
 |  |  |  |  | Combined Average of South & DUP:
 |  |  |  |  |  | Duplicate % Recovery (<20%):
 |  |  |  |  | Combined Average of North & South Raw:
 |  |  |  |  | Final D.O. must be >1mg/L | Depletion must be >2mg/L
Sample ID | Bottle # | ml's Sample Used | Seed Volume (mL) | Initial D.O. | Final D.O. | Depletion | Seed Correction Factor | Net Depletion |  | Result (mg/L)
North | 56 | 5 |  | 8.24 | 6
 | 57 | 10 |  | 8 | 4
 | 58 | 15 |  | 8 | 1
 |  |  |  |  |  |  | North Raw Average:
South | 57 | 5 |  | 8.27 | 6
 | 59 | 10 |  | 8.15 | 4.1
 | 58 | 15 |  | 8.26 | 0.8
 |  |  |  |  |  |  | South Raw Average:
South Dup. | 57 | 5 |  | 8.27 | 6.15
 | 59 | 10 |  | 8.15 | 4.33
 | 60 | 15 |  | 8.29 | 0.6
 |  |  |  |  |  |  | South Raw Dup. Average:
 |  |  |  |  | Combined Average of South Raw & DUP:
 |  |  |  |  |  | Duplicate % Recovery (<20%):
 |  |  |  |  | Combined Average of North & South Raw:
Spreadsheet: 474359d1470743826-pay-verify-using-vba-reg-attendance
Sheet: CMRE-H-JUNE-2016

[NAME] FOR THE MONTH OF  June 2016
Attendence Report for the Period : |  |  | 1st JUNE 2016 to 30th JUne HOSPITAL ATTENDANCE
S. No | EMP CODE | COL - 3 | Employee Name | Date of Joining | Designation | Department | Dates : LWP / Long Leave/ Date of Resignation & Last Working Day/ Date of Joining | LOPs | No of days Payable | No. of Days in the month | Remarks | Arrears Days
1 | 681 | CMRE | J | 2014-11-24 00:00:00 | OCCUPATIONAL THERAPIST | OCCUPATIONAL THERAPY | LOP - 4 DAYS | 4 |  | 31
10 | 689 | CMRE | K | 2015-12-01 00:00:00 | SPEECH THERAPIST | SPEECH THERAPY |  | 8 |  | 31
13 | 649 | CMRE | L | 2011-06-27 00:00:00 | SPECIAL EDUCATOR | SPECIAL SCHOOL |  | 1 |  | 31
15 | 661 | CMRE | M | 2012-11-15 00:00:00 | SPECIAL EDUCATOR | SPECIAL SCHOOL | LOP -21/06/2016 TO 20/07/2016 | 31 |  | 31
16 | 665 | CMRE | N | 2013-07-01 00:00:00 | SPECIAL EDUCATOR | SPECIAL SCHOOL | DENGUE | 14 |  | 31
22 | 678 | CMRE | O | 2014-05-11 00:00:00 | SPECIAL EDUCATOR | SPECIAL SCHOOL |  | 19 |  | 31
28 | 693 | CMRE | P | 2016-07-01 00:00:00 | SPECIAL EDUCATOR | SPECIAL SCHOOL | NEW JOINEE | 11 |  | 31
29 | 694 | CMRE | Q | 2016-07-18 00:00:00 | ASSISTANT | SPECIAL SCHOOL | NEW JOINEE | 28 | 3 | 31
 |  |  | Prepared By : -  DEEPTHI K |  |  | Submited to : - Geetha N
 |  |  | Exe. HR,VIMS & RC |  |  | Exe. HR,VIMS & RC
 |  |  | Signature : - |  |  | Signature : -
Spreadsheet: 43954d1242640162-use-of-sumproduct-countif-sumproduct-arraysum
Sheet: Sheet1

Type | Outcome
A | N/A
B | Y |  |  |  |  |  | Count of Outcome | Outcome
A | N/A |  |  |  |  |  | Type | Y
C | Y |  |  |  |  |  | A
D | N |  |  |  |  |  | B | 2
A | N/A |  |  |  |  |  | C | 1
E | Y |  |  |  |  |  | D
F | Y |  |  |  |  |  | E | 1
B | N |  |  |  |  |  | F | 1
C | N |  |  |  |  |  | Grand Total | 5
B | Y
Expected Results | [NAME] | Sum (Array)
A | 0 | 0 | always press Control+Shift+Enter to activate array formula
B | 2 | 2
C | 1 | 1
D | 0 | 0
E | 1 | 1
F | 1 | 1
 |  |  |  | http://www.excelforum.com/excel-miscellaneous/684024-use-of-sumproduct-countif.html
Spreadsheet: 83604d1283559732-excel-2007-formula-using-next-above-information-help
Sheet: Sheet1

paid | 10
- | 0
- | 0
- | 0
- | 0
paid | 500 |  |  | 3
- | 0
- | 0
paid | 1000 |  |  | 6
- | 0
paid | 100
- | 0
- | 0
- | 0
 | in column "b" is my list of numbers, i have used a filter in column a so we dont see all the 0's..
 | in c9 is an example of the formula, it takes information from the next line above it which does not have a zero (d4)
 | what can i put in c12, so the same formula applies and uses the info from d9 automatically
 | i have used the "if" formula so that i can use the equation for the entire c column and it will only apply when there is a number
Spreadsheet: 720878d1614344227-how-to-find-number-numbering
Sheet: Sheet1

 |  |  |  |  |  | CouponFm | CouponTo | BookNo
 |  |  |  |  |  | 1303361 | 1303380 | 65169 |  |  |  | 1303366
 |  |  |  |  |  | 1303381 | 1303400 | 65170
 |  |  |  |  |  | 1303401 | 1303420 | 65171
 |  |  |  |  |  | 1303421 | 1303440 | 65172
 |  |  |  |  |  | 1303441 | 1303460 | 65173
 |  |  |  |  |  | 1303461 | 1303480 | 65174
Spreadsheet: 86759d1286737334-sumproduct-formula-with-a-minimum-value-needed-weapons-20calc-20-20copy-1-
Sheet: Weapons Calc

 |  |  |  |  |  | group size | 3000
 |  |  | items
 | defend | attack | purchased |  |  | Defense | 58429 | 58429 | 42311
stick | 0 | 1 |  |  |  | Attack | 46076 | 46076 | 27020
bat | 2 | 1
knife | 1 | 2
 | 2 | 3
 | 2 | 3
 | 6 | 5
 | 3 | 7
 | 9 | 8
 | 4 | 1
 | 3 | 5
 | 8 | 10
 | 12 | 15 | 228 | 0
chain | 16 | 18 | 755 | 0
[NAME] | 14 | 22 | 108 | 15
chainsaw | 15 | 2 | 2037 | 2037
bomb | 12 | 24 | 914 | 914
shield spike | 17 | 20 | 34 | 34
 | Total Items purchased |  | 4076
Spreadsheet: 866030d1712956616-subtract-one-value-from-multiple-others-subtraction-work-hours-vacation-misia_klu-
Sheet: Sheet1

Start | Result
Work | Work | Vacation
8 |  | 25
4
2
10
8
Column A - hours of action that an employee is performing in a week, per project
Column B - amount of hours of vacation in a week
I would like to gradually reduce the hours of work by the amount of vacation hours
 |  | Vacation
0 |  | 25
0
0
0
7
Spreadsheet: dan_hyvl__4698__City of Anaheim Master Physical 03282001
Sheet: AMASTPSA 

MASTER FIRM PURCHASE/SALE AGREEMENT INFORMATION SHEET
ENA Entity: | ENA | ATTORNEY:
DATE: | 2001-03-28 00:00:00 | ENA CONTACT: | [NAME]
COUNTERPARTY: | City of Anaheim | CONTACT: | Mr. [NAME]
ADDRESS: | [NAME]., Suite 802 | TITLE:
 | Anaheim, CA 92805 | PHONE:
 |  | FAX:
INCORPORATION STATE: |  | S&P RATING:
STRUCTURE | COUNTERPARTY | ENA ENTITY
COLLATERAL
THRESHOLD  in $ | 5000000 | 25000000
COMMITTED VOLUMES
THRESHOLD in $[NAME] | N/A | N/A
COLLATERAL REQUIRED FOR:
ALL TRADES
OTHER | N/A | N/A
COLLATERAL TYPE
Letter of Credit | OK | OK
Cash | OK | OK
COLLATERAL  MULTIPLE | 250000 | 250000
GUARANTOR (RATING) | N/A | Enron Corp.
LIMIT in $[NAME]
Incorporation State: |  | Oregon
MATERIAL ADVERSE CHANGE TRIGGERS  APPLY TO:
-Counterparty | X
-Guarantor |  | X
Debt Ratings Change | X | X
-Below = | A1 (see Note 1 below) | BBB- (See Note 1 below)
Financial Covenants
-Funded Debt/Net Worth
-Minimum Net worth
-EBITDA/Interest
-Current Ratio
Non-Specific
-Unilateral
Ownership Change
-Less than 51% owned by:
Non-Standard Definitions
-Funded Debt
-Net Worth
-Cash Flow
EVENTS OF DEFAULT
3rd party debt | X | X
-Counterparty | 10000000
-Guarantor ($[NAME]) |  | 100000000
FIN'L STATEMENTS
-Standard | X | X | Upon request
CREDIT DECISION: | APPROVED:  X | DECLINED:
OUTSTANDING PHYSICAL TRADES: |  | YES: | NO:  X
ALL TRADES TO ROLL UP UNDER NEW MASTER: |  | YES:    X | NO:
OTHER COMMENTS | 1.  Debt rating for City of Anaheim is Moody's general obligation rating.
 | Debt rating for Enron is Standard & Poor's underlying credit rating.
CREDIT REVIEW  PREPARED BY: |  | [NAME] | Ext. 53923
Spreadsheet: 887535d1737755664-generating-1-column-of-data-into-a-calendar-reviews-calendar-test_3
Sheet: Dec

 |  |  | 2026-11-30 00:00:00 | 2026-12-01 00:00:00 | 2026-12-02 00:00:00 | 2026-12-03 00:00:00 | 2026-12-04 00:00:00 | 2026-12-05 00:00:00
 |  |  | 2026-12-07 00:00:00 | 2026-12-08 00:00:00 | 2026-12-09 00:00:00 | 2026-12-10 00:00:00 | 2026-12-11 00:00:00 | 2026-12-12 00:00:00
 |  |  | 2026-12-14 00:00:00 | 2026-12-15 00:00:00 | 2026-12-16 00:00:00 | 2026-12-17 00:00:00 | 2026-12-18 00:00:00 | 2026-12-19 00:00:00
 |  |  | 2026-12-21 00:00:00 | 2026-12-22 00:00:00 | 2026-12-23 00:00:00 | 2026-12-24 00:00:00 | 2026-12-25 00:00:00 | 2026-12-26 00:00:00
 |  |  | 2026-12-28 00:00:00 | 2026-12-29 00:00:00 | 2026-12-30 00:00:00 | 2026-12-31 00:00:00 | 2027-01-01 00:00:00 | 2027-01-02 00:00:00
 |  |  | 2027-01-04 00:00:00 | Notes
Spreadsheet: 766906d1644159914-drag-a-formula-across-365-columns-skipping-1-row-every-3-columns-dragged-test10-ans-3
Sheet: TAB2

Day | Night | Day | Night | Day | Night
2 | 3 | 2 | 3 | 2 | 3
2 | 3 | 2 | 3 | 2 | 3
2 | 3 | 2 | 3 | 2 | 3
2 | 3 | 2 | 3 | 2 | 3
2 | 3 | 2 | 3 | 2 | 3
2 | 3 | 2 | 3 | 2 | 3
2 | 3 | 2 | 3 | 2 | 3
Spreadsheet: benjamin_rogers__928__PJM Operational Summaries
Sheet: 02-19-02

2002-02-19 10:47:18
 | Operations Summary for Today's Forecast Peak
A | Internal Scheduled Capacity | 43067
B | Scheduled Tie Flows | 2580
C | Capacity Adjustments | 0
D | Total Scheduled Capability | 45650
E | PJM Load Forecast | 35300
F | Operating Reserve | 10350
G | Unscheduled Capacity | 5929
 | Scheduled Tie Flow Breakdown
H | APS | 1628
I | FE | 622
J | NYPP | 94
K | VP | 236
L | Total | 2580
M | Forecast Eastern Transfer Limit | 6100
N | Forecast Central Transfer Limit | 4100
O | Forecast Western Transfer Limit | 6200
 | Previous Periods Operation Summary
 |  | Day | Evening | Midnight
P | Hour | 1000 | 2000 | 300
Q | PJM  [NAME] | 33700 | 34600 | 26000
R | Actual  Load | 33977 | 34967 | 26158
S | West Dispatch Rate ($/MWhr) | 27.22 | 23.03 | 16.09
T | East Dispatch Rate ($/MWhr) | 27.22 | 23.03 | 16.09
Spreadsheet: 75372d1275946306-matching-multiple-values-against-multiple-values-worldcup_sort
Sheet: groups

GroupA | GroupB | GroupC | GroupD | GroupE | GroupF | GroupG | GroupH
France | Argentina | Algeria | Australia | Cameroon | Italy | Brazil | Chile
Mexico | Greece | England | Germany | Denmark | New Zealand | Ivory Coast | Honduras
South Africa | Nigeria | Slovenia | Ghana | Holland | Paraguay | North Korea | Spain
Uruguay | South Korea | USA | Serbia | Japan | Slovakia | Portugal | Switzerland
Spreadsheet: 84351d1284398438-360-review-results-formatting-copy-of-work
Sheet: Sheet 1

Reference # | Status | Are you... | The person I am reviewing is: | Item # 30 | Item # 32 | Item # 34 | Item # 35 | Item # 36 | Item # 37 | Item # 38 | Item # 39 | Item # 40 | Item # 43 | Item # 44 | Item # 45 | Item # 46 | Item # 48 | Item # 49 | Item # 50 | Item # 51 | Item # 53 | Item # 54 | Item # 55 | Item # 56 | Item # 58 | Item # 59 | Item # 60 | Item # 61 | Item # 62 | Item # 64 | Item # 65 | Item # 66 | Item # 67 | Item # 69 | Item # 70 | Item # 71 | Item # 72 | This person is very effective in the following areas: | This person could be more effective if: | Date | Start Time | Finish Time | Duration (s) | User | Browser | OS | Referrer
5132316 | Complete | The employee | [NAME] | 1. Never | 2. Rarely | 1. Never | 1. Never | 1. Never |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | fg | fgf | 2010-07-01 20:32:44 | Upgrade to Pro | Upgrade to Pro | Upgrade to Pro | 108.25.129.250 | Upgrade to Pro | Upgrade to Pro | Upgrade to Pro
5132283 | Complete | The employee | [NAME] | 4. Normally | 2. Rarely | 5. Almost Always | 6. [NAME] | 4. Normally | 3. Adequately | 3. Adequately | 4. Normally | 4. Normally | 4. Normally | 4. Normally | 5. Almost Always | 4. Normally | 1. Never | 3. Adequately | 4. Normally | 4. Normally | 4. Normally | 1. Never | 3. Adequately | 5. Almost Always | 4. Normally | 2. Rarely | 5. Almost Always | 4. Normally | 3. Adequately | 3. Adequately | 4. Normally | 4. Normally | 6. [NAME] | 5. Almost Always | 3. Adequately | 3. Adequately | 2. Rarely | ,[NAME] | nhk | 2010-07-01 20:06:14 | Upgrade to Pro | Upgrade to Pro | Upgrade to Pro | 108.25.129.250 | Upgrade to Pro | Upgrade to Pro | Upgrade to Pro
 |  | The employee | [NAME] | 1. Never | 2. Rarely | 1. Never | 1. Never | 1. Never
 |  | The employer | [NAME] | 4. Normally | 2. Rarely | 5. Almost Always | 6. [NAME] | 4. Normally
 |  | The employee | [NAME] | 1. Never | 2. Rarely | 1. Never | 1. Never | 1. Never
 |  | The employer | [NAME] | 4. Normally | 2. Rarely | 5. Almost Always | 6. [NAME] | 4. Normally
 |  | The employee | [NAME] | 1. Never | 2. Rarely | 1. Never | 1. Never | 1. Never
 |  | The employee | [NAME] | 4. Normally | 2. Rarely | 5. Almost Always | 6. [NAME] | 4. Normally
 |  | Roughly how I would like results to be
 |  |  | Averages
 |  | [NAME] | Question 1 | Question 2
 |  | Employee | 4 | 5
 |  | Direct Report | 3.4 | 4.3
 |  | Managers | 3.7 | 2.4