tianzl66/Sheetpedia huggingface dataset:
Spreadsheet: 743880d1628792038-sum-between-blank-cells-in-variable-range-sample
Sheet: Sheet1

Line Type | [NAME] | BOM Prod Cd | 8/2021 list | 8/2021 Rep Cost | Parent List Price | Parent Cost
B | 500A | 500A
N | 500B | 500A | 7.353 | 2.59
N | 511B | 500A | 14.1801 | 5
N | 511B | 500A | 17.0763 | 6.02
B | 500A | 500A
N | 500B | 500A | 6.3878 | 2.25
N | 511B | 500A | 21.1304 | 7.45
N | 511B | 500A | 40.5547 | 14.3
B | 500A | 500A
N | 500B | 500A | 7.353 | 2.59
N | 511B | 500A | 28.5808 | 10.08
N | 511B | 500A | 17.6968 | 6.24
B | 500A | 500A
N | 500B | 500A | 5.6438 | 1.99
N | 511B | 500A | 17.4797 | 6.16
N | 511B | 500A | 9.5317 | 3.36
N | 520B | 500A | 2.8571 | 0.84
B | 500A | 500A
N | 500B | 500A | 5.6438 | 1.99
N | 511B | 500A | 17.4797 | 6.16
N | 511B | 500A | 9.5317 | 3.36
N | 520B | 500A | 2.8571 | 0.84
B | 500A | 500A
N | 500B | 500A | 6.3878 | 2.25
N | 511B | 500A | 21.1304 | 7.45
N | 511B | 500A | 40.5547 | 14.3
B | 500A | 500A
N | 500B | 500A | 5.6438 | 1.99
N | 511B | 500A | 17.4797 | 6.16
Spreadsheet: 722617d1615329754-scheduling-days-off-for-staff-rdo-auto-populate
Sheet: Sheet1

Post Number & Description - PRIMARY |  |  |  | RDO | PPPA | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday
PERNR | Employee Name
 | 5480-002 | S SCC 1 AM SUPERVISOR |  | F/S |  | 0400-1200 | 0400-1200 | 0400-1200 | 0400-1200 | RDO | RDO | 0400-1200
 | 5480-003 | S SCC 2 PM SUPPERVISOR |  | S/M |  | RDO | 1100-1900 | 1100-1900 | 1100-1900 | 1100-1900 | 1100-1900 | RDO
Spreadsheet: 26772d1219254415-what-function-to-use-to-populate-data-from-other-worksheet-multipagelookup
Sheet: Loc 3

Part No. | Description | Amount | Price | City
CC-22 | Apple | 6 | 2 | New York
CC-33 | [NAME] | 7 | 4 | New York
CC-44 | Peach | 8 | 6 | New York
Spreadsheet: 124303d1318321230-sumproduct-criteria-problem-technical-services-period-final-stock-2011-12-v1
Sheet: Stock Movement

 | Date | Order No./Rec No. | Tech Intl. / Stock In | Technician Name | Product No. | Item Description | Stock In / Out | Opex Stock |  |  |  | Capex Stock
0 |  |  |  |  |  |  |  | Opening Balance | Stock In | Stock Out | Closing Balance | Opening Balance | Stock In | Stock Out | Closing Balance | Total Closing
1 |  | 1234 | km | [NAME] | 001 | 24 | 1 | 24 | 0 | 1 | 23 | 0 | 0 | 0 | 0 | 23
2 |  | 1234 | km | [NAME] | 001 | 24 | 1 |  | 0 | 1 |  |  | 0 | 0
3 |  |  |  |  |  |  |  |  | 0 | 0 |  |  | 0 | 0
4 |  |  |  |  |  |  |  |  | 0 | 0 |  |  | 0 | 0
5 |  |  |  |  |  |  |  |  | 0 | 0 |  |  | 0 | 0 |  |  |  |  | `
6 |  |  |  |  |  |  |  |  | 0 | 0 |  |  | 0 | #N/A
Spreadsheet: 833301d1686892330-find-two-columns-at-the-same-time-and-get-the-result-help-5-
Sheet: Sheet1

Code1 | Code2 | Value | Country |  |  | Code1 | Code2
 | V044987 | A | USA |  |  |  | V044987 | A | USA |  |  | USA
 | V057144 | B | EURO |  |  |  | V057144 | B | EURO |  | B | EURO
 | V033696 | C | UK |  |  | A041135 | V005849 | A | Taiwan |  | A | Taiwan
 | V044792 | C | JAPAN |  |  | [NAME] |  | B | KOREA |  | B | KOREA
A041135 | V005848 | A | Taiwan |  |  |  | V062635 | B | Thailand |  | A | Thailand
[NAME] |  | B | KOREA
A083053 |  | Z | Finland
A105781 |  | B | Australia
A106458 |  | J | RUSIA
A106857 |  | H | India
A151831 | V062635 | A | Thailand
A110511 |  | B | Singapore
A118131 |  | K | CHINA
Spreadsheet: 199146d1355110454-help-with-conditional-formatting-based-on-percentages-jazzed-up-gradebook-experiment
Sheet: Sheet1

 | Student | Assignment | Assignment | Assignment | Assignment | Total Points | Percent Grade
[NAME] |  | 25 | 25 | 50 | 100
 | George | 23 | 25 | 42 | 85
 | [NAME] | 22 | 18 | 46 | 93
 | [NAME] | 20 | 24 | 0 | 99
 | [NAME] | 23 | 21 | 55 | 74
 | Franklin | 0 | 3 | 22 | 22
Spreadsheet: 75185d1275701641-validation-with-drop-down-book1
Sheet: KOL

 |  |  |  |  |  |  |  |  |  |  |  |  | local | imported
 |  |  |  |  | ACTUAL Delivery Timelines (no. of days) |  |  |  |  |  |  |  | andrez | huwaei
 |  |  |  |  |  |  |  |  |  |  |  |  | space
 |  |  |  |  |  |  |  | Type of vendor | local |  |  |  | fibcomm
 |  |  |  |  |  |  |  |  |  |  |  |  | ceragon
 |  |  |  | imported |  |  |  |  |  |  |  |  | [NAME]
 |  |  |  | Vendor | ITEM | Average Transit Time (from [NAME]'s factory to Indian Port of Entry) | Average TAT for custom clearance (for Imported Items only) | Average Transit Time for Receipt at TWH (either from CHA or domestic supplier location to TWH) | Total TAT (from Supplier despatch to Material receipt)
 |  |  |  | Huwaei | PDH Antenna | 22 | 5 | 1 | 28 days
 |  |  |  | Huwaei | Microwave Hop | 22 | 5 | 1 | 28 days
 |  |  |  | Huwaei | Indoor BTS | 22 | 5 | 1 | 28 days
 |  |  |  | Huwaei | Outdoor BTS | 22 | 5 | 1 | 28 days
 |  |  |  | local
 |  |  |  | Vendor | ITEM | Average Transit Time (for sending road permit) | Average Transit Time for Receipt at TWH | Total TAT (from Supplier despatch to Material receipt)
 |  |  |  | andrez | Power cable  (red) | 3 | 9 | 28 days
 |  |  |  | space | Grounding cable | 3 | 9 | 28 days
 |  |  |  | fibcomm | Jumper Cable 1/2" Din M-Din M of 3.0 M Length | 3 | 9 | 28 days
 |  |  |  | ceragon | Feeder Connector - DIN Female for 7/8" | 3 | 9 | 28 days
 |  |  |  | [NAME] | [NAME] | 3 | 9 | 28 days
Spreadsheet: 825498d1681367332-using-sumproduct-to-get-sum-of-numbers-where-some-cells-contains-a-dash-book1-2-
Sheet: DaY-Wise in WeekWise

 |  |  | Week 1 | Week 2 | Week 3 | Week 4
 |  | From | 1 | 8 | 15 | 22
 |  | To | 7 | 14 | 21 | 31
W
 |  |  | DaY-Wise in WeekWise
 |  |  | I need to get the Summation based on the DAY, so how can I use SUMPRODuCT to get the Sum of first Week 1 i.e - Day 1 - 7 then 8 - 14, 15 -21 then 22 till 31…
 |  |  | In the above Solution I have explicitly selected the range from the Col D till Col J which is the Data for the Days from 1 to 7, I was expecting some solution which also had a dimension of having the Days Between >=1 and <= 7 , so on so forth for >=8 and <= 15..
Spreadsheet: 867050d1713957570-formulae-to-copy-data-between-sheets-with-only-a-certan-value-rams-sample
Sheet: DATA

 |  |  |  | Action | Hierarchy | Response |  | Consequence | Probability
 |  |  |  | Treat | Eliminate | Contractor |  | Rare | Insignificant
 |  |  |  | Terminate | Substitute | Client |  | Unlikely | Minor
 |  |  |  | Tolerate | Engineering | PC |  | Possible | Moderate
 |  |  |  | Transfer | Administrative |  |  | Likely | Major
 |  |  |  |  | PPE |  |  | Almost Certain | Catastrophic
 | Insignificant | Minor | Moderate | Major | Catastrophic | Risk Value
Almost Certain | 11 (M) | 16 (H) | 20 (H) | 23 (Ex) | 25 (Ex)
Likely | 7 (M) | 12 (M) | 17 (H) | 21 (Ex) | 24 (Ex)
Possible | 4 (L) | 8 (M) | 13 (H) | 18 (H) | 22 (Ex)
Unlikely | 2 (L) | 5 (L) | 9 (M) | 14 (H) | 19 (H)
Rare | 1 (L) | 3 (L) | 6 (M) | 10 (M) | 15 (H)
Spreadsheet: 698967d1602278535-logic-using-if-statements-with-percentages-example-increase
Sheet: Sheet1

Sold | Increased | Manual Increase % | Projected | Manual Increase Qty |  | What [NAME] should return
20 | 0.1 |  |  |  |  | 21
5 | 0.18 |  |  |  |  | 5
200 | 2.01 |  |  |  |  | 270
425 | 1.47 |  |  |  |  | 531
612 | 0.68 |  |  |  |  | 704
998 | 0.49 |  |  |  |  | 1098
7 | 0.05 |  |  |  |  | 7
14 | 0.2 |  |  |  |  | 15
874 | 2.5 |  |  |  |  | 1180
95 | 0.22 |  |  |  |  | 100
Range |  | Increase
0 | 0.25 | 5
0.250001 | 0.5 | 10
0.500001 | 1 | 15
1.000001 | 2 | 25
2.000001 | 100 | 35
I want to write logic into the formula for Column "D". I first want to be able to Put in a manual Increase if desired into Column "E". IF I don't wan to do that then I want to put in an Increase by Percentage in Column "C".
After that I want to automatically increase Column "D" based on the logic written into "IF" statements. So it will look in Column "B" and increase Column "D" based off the ranges in the table
I don't want to use a table, just "IF" Statements. I just can't figure out how Excel sees the percentages. Any help would be appreciated. Thanks,
Spreadsheet: 782538d1654103441-oldest-and-latest-dates-formula-oldest-and-latest-date
Sheet: Sheet1

 |  |  | 2022-05-20 00:00:00 | 2022-05-21 00:00:00 | 2022-05-22 00:00:00 | 2022-05-23 00:00:00 | 2022-05-24 00:00:00 | 2022-05-25 00:00:00 | 2022-05-26 00:00:00 | 2022-05-27 00:00:00 | 2022-05-28 00:00:00 | 2022-05-29 00:00:00 | 2022-05-30 00:00:00 | 2022-05-31 00:00:00
WBS | Oldest Date | Latest Date | 20th | 21st | 22nd | 23rd | 24th | 25th | 26th | 27th | 28th | 29th | 30th | 31st
H5640A199 |  |  |  |  | 2 | 1 | 2 | 2 | 1 | 2
H5806A199 |  |  | 5 | 3 | 1 | 10 | 6 |  | 8
Need formula to get Oldest (Start) date and Latest (Close) date from the table above
Spreadsheet: 725277d1616603413-display-times-next-to-the-field-callschedule
Sheet: Code

Sunday |  | Monday |  | Tuesday |  | Wednesday |  | Thursday |  | Friday |  | Saturday
 |  |  |  |  |  |  |  | 1900-01-01 00:00:00
 |  |  |  |  |  |  |  | AMPGY3 |  | AMPGY3 |  | AMPGY3
 |  |  |  |  |  |  |  | AMPGY2A |  | AMPGY2A |  | AMPGY2A
 |  |  |  |  |  |  |  | AMPGY2B |  | AMPGY2B
 |  |  |  |  |  |  |  | AMPGY2C |  | AMPGY2C
 |  |  |  |  |  |  |  | PGY2NF |  |  |  | PGY2NF
 |  | AM PGY3 (6a-1230p) |  | AM PGY3 (6a-6p) |  | AM PGY3 (6a-6p) |  | AM PGY3 (6a-6p) |  | AM PGY3 (6a-5p) |  | AM PGY3 (6a-6p)
AM PGY2B (6a-6p) |  | AM PGY2A (6a-730p) |  | AM PGY2A (6a-1230p) |  | AM PGY2A (6a-6p) |  | AM PGY2A (6a-6p) |  | AM PGY2A (6a-5p) |  | AM PGY2A (6a-6p)
AM PGY2C (6a-6p) |  | AM PGY2B (6a-6p) |  | AM PGY2B (6a-6p) |  | AM PGY2B (6a-1230p) |  | AM PGY2B (6a-6p) |  | AM PGY2B (6a-5p)
 |  | AM PGY2C (6a-6p) |  | AM PGY2C (6a-6p) |  | AM PGY2C (6a-6p) |  | AM PGY2C (6a-1230p) |  | AM PGY2C (6a-5p)
PGY2 NF (6p-6a) |  | PGY2 NF (730p-630a) |  | PGY2 NF (6p-6a) |  | PGY2 NF (6p-6a) |  | PGY2 NF (6p-6a) |  |  |  | PGY2 NF (6p-6a)
Spreadsheet: 772587d1647376927-excel-formula-consolidate-data-in-columns-and-create-new-rows-two-criteria-test-workbook
Sheet: Sheet1

Value | Amount | Type | Val_Type |  | Distinct Values | Sum Of |  | "Distinct" Values | Sum Of |  | Val_Type | Sum of Amount
1 | 12 | A |  |  |  |  |  | 1 | 123.5 |  | 1 | 123.5
1 | 44.5 | A |  |  |  |  |  | 2 | 85 |  | 2 | 85
1 | 45 | A |  |  |  |  |  | 3 | 82.25 |  | 3 | 82.25
1 | 17 | A |  |  |  |  |  | 4 | 134.75 |  | 4 | 134.75
2 | 21 | A |  |  |  |  |  | 1 | 72.5 |  | Grand Total | 425.5
2 | 1 | A |  |  |  |  |  | 3 | 32.5
1 | 13 | C |  |  |  |  |  | 4 | 3
1 | 15.5 | C
1 | 17 | C
3 | 82.25 | A
3 | 0.5 | C |  |  | Currently ^ |  |  | What I want ^
1 | 27 | C
3 | 13 | C
3 | 19 | C
4 | 3 | C
1 | 5 | A
4 | 55 | A
2 | 63 | A
4 | 79.75 | A
Spreadsheet: 1a05707b-6a4e-4745-aab7-632bf7793654
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
 | 6345.0 Wage Price Index, Australia
 | Table 1. Total Hourly Rates of Pay Excluding Bonuses: Sector, Original, Seasonally Adjusted and Trend
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
Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Private ;  All industries ; |  |  | Original | A2603039T | 1997-09-01 00:00:00 | 2012-09-01 00:00:00 | 61 | Index Numbers | INDEX | Quarter | 3
Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Public ;  All industries ; |  |  | Original | A2603989W | 1997-09-01 00:00:00 | 2012-09-01 00:00:00 | 61 | Index Numbers | INDEX | Quarter | 3
Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Private and Public ;  All industries ; |  |  | Original | A2603609J | 1997-09-01 00:00:00 | 2012-09-01 00:00:00 | 61 | Index Numbers | INDEX | Quarter | 3
Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Private ;  All industries ; |  |  | Seasonally Adjusted | [NAME] | 1997-09-01 00:00:00 | 2012-09-01 00:00:00 | 61 | Index Numbers | INDEX | Quarter | 3
Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Public ;  All industries ; |  |  | Seasonally Adjusted | A2713852T | 1997-09-01 00:00:00 | 2012-09-01 00:00:00 | 61 | Index Numbers | INDEX | Quarter | 3
Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Private and Public ;  All industries ; |  |  | Seasonally Adjusted | A2713849C | 1997-09-01 00:00:00 | 2012-09-01 00:00:00 | 61 | Index Numbers | INDEX | Quarter | 3
Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Private ;  All industries ; |  |  | Trend | A2713848A | 1997-09-01 00:00:00 | 2012-09-01 00:00:00 | 61 | Index Numbers | INDEX | Quarter | 3
Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Public ;  All industries ; |  |  | Trend | A2713854W | 1997-09-01 00:00:00 | 2012-09-01 00:00:00 | 61 | Index Numbers | INDEX | Quarter | 3
Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Private and Public ;  All industries ; |  |  | Trend | A2713851R | 1997-09-01 00:00:00 | 2012-09-01 00:00:00 | 61 | Index Numbers | INDEX | Quarter | 3
© Commonwealth of Australia  2012
Spreadsheet: 782217d1653913884-count-rows-until-a-certain-sum-value-is-reached-based-off-addtl-criteria-sales-analysis
Sheet: Sheet4

Dish Code | Dish #
MOV | 11
MOC | 12
LUC | 13
BOL | 14
[NAME] | 15
CPP | 16
COC | 17
POZ | 18
YMM | 19
YMV | 20
PIB | 21
 | 22
EMR | 23
TOR | 24
TNC | 25
BUF | 26
MJL | 27
MJV | 28
SMG | 29
PZG | 30
ENB | 31
MAP | 32
CLM | 33
 | 34
FLO | 35
MAP-V | 36
CTS | 37
BBS | 38
TPP | 39
CSS | 40
BLC | 41
BPC | 42
SIC | 43
KUG | 44
PBK | 45
MBS | 46
MPL | 47
LRG | 48
PAP | 49
SQV | 50
CAL | 51
SKP | 52
BNG | 53
AGN | 54
SHL | 55
ARI | 56
BRC | 57
SHC | 58
MBB | 59
MAR | 60
EMP-RHU | 61
SPS | 62
C-MIS | 63
TRT | 64
DPS | 65
SQF | 66
[NAME] | 67
JAS | 68
SBR | 69
FOR | 70
SAN-WFM | 71
SAN-ITL | 72
SAN-FOR | 73
BGS-CA | 74
PEL | 75
BGS-D | 76
BGS-C | 77
BGS-P | 78
STK-C | 79
C-PEP | 80
C-RYE | 81
C-HON | 82
BRP-STN | 83
APC | 84
BRP-RHU | 85
ICX | 86
PAN | 86
PST | 87
TSC | 88
TSO | 89
KRC | 90
PNR | 91
LTK | 92
TMV | 93
TMM | 94
SHR | 95
SND | 96
RTP | 97
SMA | 98
RGF | 99
MBL | 100
PLM | 101
RAD | 102
RIG | 103
XTRA-C | 104
PIE | 105
C-SAF | 106
C-SES | 107
STS-BC | 108
STS-SC | 109
STS-PB | 110
BUR | 111
LAR | 113
LAF | 114
SAN-TRK | 115
SAN-HAM | 116
LAV | 117
CPD | 118
BBS-W | 119
TOR-W | 120
MOC-W | 121
BOL-W | 122
BUF-W | 123
COC-W | 124
MJV-W | 125
BUTTER 50lb | N/A
END | 126
PIB-W | 127
MPL-W | 128
ENB-W | 129
SMG-W | 130
LUC-W | 131
EMR-W | 132
STS-S SPINACH | N/A
BANANA | N/A
STRAW | N/A
CHOPPED SP | N/A
BLUEBERRY | N/A
NJ BLUE | N/A
AMG | 133
AMG-W | 134
CEB | 135
CVB | 136
VCH | 137
MCH | 138
CVT | 139
JAR | 140
TCS | 141
CCS | 142
CIN | 143
CND | 144
TIK | 145
[NAME] | 146
ARC | 147
LAF-W | 148
LAP | 149
VBB | 150
[NAME] | 151
RPZ | 152
WPZ | 153
SPZ | 154
PZG-W | 155
BGB-B | 156
BGB-L | 157
GFP | 158
Spreadsheet: 95008213-0b51-4935-b0f0-6c73fbb07ef8
Sheet: Inversiones de capital

UNIVERSIDAD POLITECNICA SALESIANA
INVERSIONES DE CAPITAL
 |  |  |  |  |  |  | EJECUCION 2011 | PRESUPUESTO 2012
Inversiones de Capital |  |  |  |  |  |  | 13330103.77
[NAME] | 89266 | 133214.82 | 147085.04 |  | 270333.9655484621 |  | 476186.15 | 583000
CONSTRUCCIONES EN PROCESO | 3278015 | 2412615 | 3481018.98 |  | 8279657.12 |  | 5326767.59 | 13480107.28
EDIFICIOS | 815 | 0 | 2280 | 1 | 0 |  | 50450.66 | 0
EQUIPO DE OFICINA | 123958 | 31302.69 | 28023.76 |  | 67692.20735748047 |  | 25956.95 | 65631.39626751485
EQUIPO Y MAQUINARIA | 75088 | 196444.88 | 10001.34 |  | 362000 |  | 717025.96 | 352000
EQUIPOS AUDIOVISUALES | 138632 | 54500 | 155742.03 |  | 362522.7169772122 |  | 228174.72 | 284066.56
EQUIPOS DE COMPUTACION | 537257 | 435997.4 | 918834.6800000002 |  | 645511.2750206348 |  | 339740.03 | 750577.9
EQUIPOS DE LABORATORIO | 441271 | 480000 | 1486451.47 |  | 2802278.233205319 |  | 5041166.700000002 | 878500
EQUIPOS DEPORTIVOS | 43279 | 30577.94 | 29749 |  | 52660 |  | 16034.37 | 60540
EQUIPOS FOTOGRAFICOS | 2959 | 2679.96 | 12103.69 |  | 22016.7 |  | 6505.09 | 26864
[NAME] MANTENIMIENTO Y SEGURIDAD EDIFICIOS | 6000 | 12533.81 | 21663.68 |  | 20276.34 |  | 27688.76 | 41842
[NAME] | 10319 | 32990.14 | 23760.5 |  | 52596.27 |  | 7311.52 | 45915.76557750699
INSTALACIONES | 114388 | 60000 | 0 |  | 0 | 0 | 1215.47 | 0
LICENCIAS, MARCAS, PATENTES, VACUNAS ANTIVIRUS | 166590 | 147000 | 44756.19 |  | 97302.838770378 |  | 31277.27 | 251953.3720000014
PROGRAMAS DE COMPUTACION | 750 | 0 | 0 | 0 | 10000 | 1 | 0 | 0
MOBILIARIO EDUCATIVO | 170894 | 50200 | 125383.94 |  | 82971.63999999998 |  | 139851.05 | 129530
MUEBLES Y ENSERES | 200091 | 159525.42 | 392783.59 |  | 321501.11123589 |  | 369552.29 | 538948.62
OBRAS DE ARTE | 7965 | 40296.62 | 6460 |  | 4279.89073527601 |  | 11165.1 | 14880
OTROS ACTIVOS | 325 | 6000 | 464.58 |  | 3000 |  | 0 | 0
VEHICULOS | 24874 | 0 | 13871.57 | 1 | 9991.07 |  | 4000 | 0
HERRAMIENTAS | 6523 | 0 | 1735.22 | 1 | 9057.68 |  | 8131.8 | 6949.430000000047
TERRENOS | 475000 | 0 | 308000 | 1 | 0 |  | 500000 | 0
[NAME] |  |  |  |  |  |  | 1902.29
[NAME] |  |  |  |  | [NAME]
RECTOR |  |  |  | SECRETARIO TECNICO DE PRESUPUESTO FINANZAS Y SEGUROS
Spreadsheet: 01ed928d-5fe4-486b-ad2f-e95a46266d9b
Sheet: serials v mono

Serials vs. [NAME] by [NAME] FY10 and FY11
 | FY10 | FY11 | % change | % of total FY11 expend.
Kingston
monographs | 343565.02 | 333441.26
monographs endowments | 3810.68 | 4377.87
MONOGRAPHS TOTAL
serials | 2726388.62 | 2891248.79
serials endowments | 36582.03 | 39437.93
SERIALS TOTAL
KINGSTON SUBTOTAL
[NAME]
monographs | 76.5 | 462.78
serials | 10536.48 | 9719.39
CCE SUBTOTAL
CCE
monographs | 13780.93 | 10237.42
serials | 4460.97 | 3411.96
[NAME]
All branches
monographs
serials
TOTAL
Spreadsheet: mary_fischer__25442__roi_version_2
Sheet: Capital Investment Value

 | Capital Asset Investment Value
 | Value Points | Value Opportunity   (Low, [NAME], High) | Current | Expected | [NAME] | Customer Success
 | Increase Warranty Claims Remuneration |  | n/a |  |  | [NAME], Progress Telecom, AT&T, Sprint
 | Reduce Redundant Purchases |  | n/a
 | Increase Asset Life/Decrease Asset Purchases |  | n/a
 | Decrease Asset Write-offs
 | Decrease Audit Labor Expense
 | Total Capital Asset Investment Value
 | Capital Asset Investment Benchmarks
 |  |  | Savings % vs. Opportunity
 | Value Points | Related Questions | High | Moderate | Low
 | Increase Warranty Claims Remuneration | Net Asset Base, A, F | 0.001 | 0.0005 | 0
 | Reduce Redundant Purchases | CAPEX, B, G, H | 0.01 | 0.005 | 0
 | Increase Asset Life/Decrease Asset Purchases | CAPEX, C, G, H | 0.1 | 0.05 | 0
 | Decrease Asset Write-offs | Net Asset Base, B, F | 0.1 | 0.05 | 0
 | Decrease Audit Labor Expense | Labor Expense, B, I, J, K | 0.75 | 0.25 | 0
Spreadsheet: 1be386a7-c183-4208-92ac-664425071f5a
Sheet: Top Items

Top 10 Vertices, Ranked by Degree | Degree
Iran | 31
Scotland | 1
Sweden | 1
Chili | 1
Italy | 1
wales | 1
Austria | 1
Belgium | 1
Greece | 1
Taiwan | 1
Top 10 Vertices, Ranked by Betweenness Centrality | Betweenness Centrality
Iran | 465
Scotland | 0
Sweden | 0
Chili | 0
Italy | 0
wales | 0
Austria | 0
Belgium | 0
Greece | 0
Taiwan | 0
Top 10 Vertices, Ranked by Closeness Centrality | Closeness Centrality
Iran | 0.032258
Scotland | 0.016393
Sweden | 0.016393
Chili | 0.016393
Italy | 0.016393
wales | 0.016393
Austria | 0.016393
Belgium | 0.016393
Greece | 0.016393
Taiwan | 0.016393
Top 10 Vertices, Ranked by Eigenvector Centrality | Eigenvector Centrality
Scotland | 0.03125
Italy | 0.03125
Chili | 0.03125
Sweden | 0.03125
wales | 0.03125
Denmark | 0.03125
Belgium | 0.03125
Austria | 0.03125
Taiwan | 0.03125
Spain | 0.03125
Top 10 Vertices, Ranked by Clustering Coefficient | Clustering Coefficient
Scotland | 0
Italy | 0
Chili | 0
Sweden | 0
wales | 0
Denmark | 0
Belgium | 0
Austria | 0
Taiwan | 0
Spain | 0
Top 10 Vertices, Ranked by PageRank | PageRank
Iran | 14.78355
Scotland | 0.555353
Sweden | 0.555353
Chili | 0.555353
Italy | 0.555353
wales | 0.555353
Austria | 0.555353
Belgium | 0.555353
Greece | 0.555353
Taiwan | 0.555353
Spreadsheet: 136486d1326681475-excel-2007-how-can-i-get-the-average-wage-of-pitchers-if-they-are-mixed-up-with-other-values-average
Sheet: Sheet1

Team |  |  | Player | Salary | Position
New | York | Yankees | Contreras, | [NAME] | 5500000 | Pitcher
New | York | Yankees | Flaherty, | [NAME] | 750000 | Catcher
New | York | Yankees | Giambi, | [NAME] | 11428571 | First Baseman
New | York | Yankees | Hammond, | [NAME] | 2200000 | Pitcher
New | York | Yankees | Hitchcock, | Sterling | 6000000 | Pitcher
New | York | Yankees | Zeile, | Todd | 1500000 | [NAME]
Anaheim | Angels |  | [NAME], | Garret | 5350000 | Outfielder
Anaheim | Angels |  | Appier, | [NAME] | 11500000 | Pitcher
Anaheim | Angels |  | Callaway, | [NAME] | 302500 | Pitcher
Anaheim | Angels |  | Donnelly, | [NAME] | 325000 | Pitcher
Anaheim | Angels |  | Eckstein, | [NAME] | 425000 | Shortstop
Anaheim | Angels |  | Erstad, | Darin | 7250000 | Outfielder
Position | Average Salary
Pitcher
Catcher
First Baseman
Outfielder
Shortstop
Spreadsheet: 3b1582d8-d1f5-4f20-b14b-8133113d73e1
Sheet: Sheet1

Table 9--U.S. vegetable oil and fats prices | Unnamed: 1 | Unnamed: 2 | Unnamed: 3 | Unnamed: 4 | Unnamed: 5 | Unnamed: 6 | Unnamed: 7 | Unnamed: 8
Marketing | Soybean | Cottonseed | Sunflower | Canola | Peanut | Corn | Lard 6 | Edible
year | oil 2 | oil 3 | oil 4 | oil 4 | oil 5 | oil 6 |  | tallow 6
 |  |  |  | Cents/pound
2000/01 | 14.15 | 15.98 | 15.88 | 17.56 | 34.97 | 13.54 | 14.61 | 13.43
2001/02 | 16.46 | 17.98 | 23.25 | 23.45 | 32.23 | 19.14 | 13.55 | 13.87
2002/03 | 22.04 | 37.75 | 33.13 | 29.75 | 46.7 | 28.17 | 18.13 | 17.8
2003/04 | 29.97 | 31.21 | 33.42 | 33.76 | 60.84 | 28.43 | 26.13 | 22.37
2004/05 | 23.01 | 28.01 | 43.71 | 30.78 | 53.63 | 27.86 | 21.8 | 18.48
2005/06 | 23.41 | 29.47 | 40.64 | 31 | 44.48 | 25.18 | 21.74 | 18.16
2006/07 | 31.02 | 35.7 | 58.03 | 40.57 | 52.99 | 31.8 | 28.43 | 27.32
2007/08 | 52.03 | 73.56 | 91.15 | 65.64 | 94.53 | 69.4 | 40.85 | 41.68
2008/09 | 32.16 | 37.1 | 50.24 | 39.54 | 78.49 | 32.75 | 26.72 | 25.47
2009/10 | 35.95 | 40.27 | 52.8 | 42.88 | 59.62 | 39.29 | 31.99 | 32.26
2010/11 | 53.2 | 54.5 | 86.12 | 58.68 | 77.24 | 60.76 | 51.52 | 51.34
2011/121 | 50.5-54.5 | 52.0-56.0 | 89.0-93.0 | 55.0-59.0 | 95.5-99.5 | 53.5-57.5 | 51.0-55.0 | 51.0-55.0
2010/11
October | 44.02 | 47.2 | 56 | 51.45 | 71.4 | 47.5 | 46.64 | 37
November | 47.62 | 50.75 | 63 | 53.63 | 75.13 | 51.96 | 37.32 | 41.75
December | 51.51 | 54 | 62.9 | 58.25 | 77.9 | 54.71 | 38.3 | 45
January | 53.84 | 55.92 | 74.125 | 59.5 | 80.06 | 57.91 | 48.5 | 50.1
February | 54.21 | 56.75 | 85.625 | 60.13 | 79.63 | 63.39 | 49.6 | 49.9
March | 54.07 | 55.5 | 96.75 | 60.25 | 77.5 | 67.72 | 52 | 51.75
April | 56.65 | 57.7 | 101.2 | 62.05 | 78.7 | 68.89 | 51.5 | 52.83
May | 56.09 | 56.06 | 103.75 | 60.19 | 82.81 | 68.33 | 54.31 | 53.87
June | 55.68 | 55.25 | 103.25 | 59.56 | 78.5 | 66.7 | 56.75 | 57.41
July | 55.16 | 54.75 | 97 | 60.7 | 88.05 | 62 | 63 | 60.89
August | 54.39 | 54.75 | 95 | 60 | 95.56 | 62 | 58.96 | 56.35
September | 55.13 | 55.35 | 94.8 | 58.45 | 97.5 | 57.95 | 61.33 | 59.28
2011/12
October | 51.73 | 51.56 | 92.5 | 56.81 | 97 | 54.24 | 61.1 | 52.09
November | 51.44 | 50.5 | 91 | 56.13 | 98.75 | 53.98 | 48.86 | 45.51
December | 50.17 | 51.1 | 91 | 55.4 | 96.1 | 53.36 | 48.71 | 50.78
January | 50.99 | 52.19 | 88.75 | 55.06 | 95.81 | 54 |  | 51.1
February1 | 52.36 | 54.56 | 86 | 56.94 | 95 | 56.24 | 52.55 | 53.17
1 Preliminary.  2 Decatur, IL.  3 PBSY Greenwood, MS.  4 Midwest. 5 Southeast mills.  6 Chicago.
Sources: USDA, Agricultural Marketing Service,  Monthly Feedstuff Prices and Milling and Baking News.
Last update: | 2012-03-13 08:08:34.220000