Transcribe the content from the pdf/image into the Excel file (including the chart) and complete any missing formulas so the workbook is fully populated and the calculations are in place.

Data Entry / Import, Reporting / Visualization, Calculation, Structuring / Formatting

Investment: Trading And Position Management


For account type H and the MCC description: Eating Places and Restaurants, what would be the average fee that the card scheme GlobalCard would charge for a transaction value of 10 EUR? Provide the answer in EUR and 6 decimals.
Answer must be just a number expressed in EUR rounded to 6 decimals. If a question does not have a relevant or applicable answer for the task, please respond with 'Not Applicable'


Rmove the Physical Location column from the Detail by Turbine sheet, and keep the Physical Location values in the Summary by Status sheet exactly as they are. Ensure that deleting columns does not affect the content of the current sheet or other sheets.

rick_buy__32884__Turbine Position Report 111601.xlsx

Structuring / Formatting, Validation / Review, Cross-sheet/file Retrieval


On the Summary by Status sheet, add a cumulative curve chart ($MM) for “Scheduled vs Cancellation Payments” using monthly data from Dec-98 through Dec-02. Plot the cumulative series for Scheduled and Cancellation across Committed, Tentative, and Available, and include Total Scheduled and Total Cancellation (Total Cancellation represents total exposure). Add a blue vertical line at Nov-2001 and annotate that month’s cumulative cancellation cost and cumulative paid amounts in blue, with the difference shown in red as “Incremental Cancellation Cost” (e.g., $35MM).

rick_buy__32877__Turbine Position Report 111501a_02.xlsx


Reformat the table by bolding the titles and inserting row borders between the different departments to better delineate each section.


Calculate the headcount for each of the three groups in the worksheet and their respective percentages of the total, and confirm in the table that the three percentages sum to 100%.


Review the summary tab against each of the individual sheets and reconcile any inconsistencies. Where the summary differs from the sub-sheets, update the summary to reflect the sub-sheet values so the workbook is internally consistent.

tracy_geaccone__40470__Functional Income 2001 (version 1).xlsx

Cross-sheet Retrieval, Validation / Review


Consolidate data from each business unit worksheet into a new ETS worksheet, and add three columns: Total, Consol, and Enron:

Total: Sum of all business units
Consol: Consolidation adjustments to eliminate intercompany transactions from Citrus and NBP
Enron: Final consolidated company figures (Total + Consol)

tracy_geaccone__40470__Functional Income 2001 (version 1)_02.xlsx

Cross-sheet Retrieval, Structuring / Formatting, Calculation


Restore the missing formulas so the totals and Net Income rollups calculate correctly, converting any hard-coded cells back to formula-driven values. This should bring the sheet back to a consistent, automated state for the 2001 functional income schedule.

tracy_geaccone__40470__Functional Income 2001 (version 1)_03.xlsx


Key the table into Excel on an "ETS" spreadsheet and reinstate the missing formulas on the ETS sheet. Also update the I1 header to “Other” and ensure the totals and Net Income roll-ups (columns J-L around row 225 and the Net Income row at 228) calculate correctly.

tracy_geaccone__40470__Functional Income 2001 (version 1)_03.jpeg

tracy_geaccone__40470__Functional Income 2001 (version 1)_03.pdf

Data Entry / Import, Structuring / Formatting, Validation / Review, Calculation


On the 'simplecorr' sheet, create a table whose column headers mirror the paper types in the left-side table, with a leftmost header labeled 'Correlation.' Using the NBSK pulp price from the left table and each paper’s monthly price series, compute and populate the correlation of NBSK to each paper’s price.


On the simplecorr sheet, create a table that mirrors the header from the table to the left to list the various paper grades. Set the left-side header to 1–6 months Lag and Lead, and then, using the NBSK pulp price from the left table together with the monthly price tables for each paper grade, calculate and populate the 1–6‑month Lag and 1-7 month Lead relationships between NBSK and each paper price.


On the Regression sheet, run a linear regression to examine the relationship between Std. No. 4, 83–85 Brt Xerog. and NBSK chg, with NBSK as the dependent variable. Summarize the results in a single table with the top header listing Std. No. 4, 83–85 Brt Xerog., and NBSK, and the left header listing mn, se, r2, f, and ssreg.

Calculation, Financial Modeling, Structuring / Formatting


Replicate the linear regression analysis we previously ran on the No. 4 Xero vs NBSK chg relationship, extending the same approach to the other paper price series and summarizing their impact on NBSK in a table. Use the same column headers as the No. 4 Xero–NBSK chg regression table.

Structuring / Formatting, Calculation, Financial Modeling


Complete the asset allocation schedule using the provided asset detail data by filling in any blank items. First calculate Total Equity and Total (total assets). Then compute Cash %, Equity %, and Fixed Income %, round each to two decimals, populate the respective percentage fields, and confirm the three sum to approximately 100%.

Data Entry / Import, Calculation, Validation / Review


Could I obtain monthly data from January 2025 to May 2025 (including January 2025 and May 2025) for NASDAQ, NYSE, Shanghai Stock Exchange, Shenzhen Stock Exchange, and Hong Kong Exchanges and Clearing?\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the following column names in order: Exchange Name, Statistical Month, Total Trading Value (USD millions), Total Number of Listed Companies, Domestic Market Capitalization (USD millions), Index Levels, Average Daily Trading Value (USD millions)\nFor the statistical month, keep the format as January 2025.\nFor total trading value and domestic market capitalization, both of the terms refer to equity.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily.\n\nBased on the RawData sheet, complete the following 4 subtasks:\n\nSubtask 1 - Sheet2_KPIs: Calculate 4 KPI indicators for each exchange-month.\nColumns: Exchange Name, Statistical Month, Trading Value per Company (USD millions), Market Cap per Company (USD millions), Daily Trading Intensity (USD millions), Market Efficiency Ratio\nTrading Value per Company = Total Trading Value / Total Number of Listed Companies (2 decimals). Market Cap per Company = Domestic Market Capitalization / Total Number of Listed Companies (2 decimals). Daily Trading Intensity = Average Daily Trading Value (2 decimals). Market Efficiency Ratio = Total Trading Value / Domestic Market Capitalization (4 decimals).\n\nSubtask 2 - Sheet3_MonthlyRanking: Rank exchanges by trading value within each month independently.\nColumns: Exchange Name, Statistical Month, Total Trading Value (USD millions), Monthly Rank, Is Top 3\nEach month should have ranks 1-5, not global ranking. Is Top 3: \"Yes\" or \"No\". Sort by month chronologically (January to May), then by Monthly Rank ascending within each month.\n\nSubtask 3 - Sheet4_GrowthAnalysis: Calculate month-over-month growth rates for February-May only.\nColumns: Exchange Name, Current Month, Previous Month, Trading Value Growth (%), Market Cap Growth (%)\nGrowth = (Current - Previous) / Previous × 100 (2 decimals). Sort by Exchange Name alphabetically, then by month chronologically (February to May) within each exchange.\n\nSubtask 4 - Sheet5_Summary: Statistical summary for each exchange across 5 months.\nColumns: Exchange Name, Total Trading Value (USD millions), Avg Monthly Trading Value (USD millions), Max Monthly Trading Value (USD millions), Min Monthly Trading Value (USD millions), Std Dev of Trading Value (USD millions), Coefficient of Variation (%), Avg Market Capitalization (USD millions), Avg Number of Companies\nCoefficient of Variation = Std Dev / Avg × 100. Sort by Total Trading Value descending.

Web Search, Data Entry / Import, Calculation, Structuring / Formatting


Need to analyze the trend of the U.S. federal government spending and deficit before and after the pandemic. Please provide me with the following data through fiscal years 2015-2024: the federal Budget (trillion), the federal spending (trillion), the federal deficit (trillion), the national debt (trillion), and the net interest cost on the gross federal debt (trillion).\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the following column names in order:\nFiscal Year, Federal Budget, Federal Spending, Federal Deficit, National Debt, Net Interest Cost \n\nUnder the Fiscal Year, state the statistics like FY2015, FY2016.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily.\n\nBased on the RawData sheet, complete the following 4 subtasks:\n\nSubtask 1 - Sheet2_Pandemic_Comparison: Compare pre-pandemic (FY2015-2019) vs post-pandemic (FY2020-2024) statistics.\nColumns: Period, Avg Budget, Avg Spending, Avg Deficit, Avg National Debt, Avg Interest Cost, Total Years\nAll averages with 2 decimals.\n\nSubtask 2 - Sheet3_Fiscal_Indicators: Calculate 5 fiscal health metrics for each year.\nColumns: Fiscal Year, Budget Deficit Rate, Debt-to-Budget Ratio, Interest-to-Spending Ratio, Spending Efficiency, Debt Service Burden\nBudget Deficit Rate = Deficit / Budget × 100 (2 decimals). Debt-to-Budget Ratio = Debt / Budget (2 decimals). Interest-to-Spending Ratio = Interest / Spending × 100 (2 decimals). Spending Efficiency = Budget / Spending × 100 (2 decimals). Debt Service Burden = Interest / Debt × 100 (4 decimals).\n\nSubtask 3 - Sheet4_Growth_Analysis: Calculate year-over-year growth rates for FY2016-FY2024.\nColumns: Fiscal Year, Budget Growth, Spending Growth, Deficit Growth, Debt Growth, Interest Cost Growth\nAll growth rates = (Current - Previous) / Previous × 100 (2 decimals).\n\nSubtask 4 - Sheet5_Summary: Overall 10-year statistics.\nColumns: Total Budget, Total Spending, Total Deficit, Avg Annual Debt, Total Interest Paid, Max Single-Year Deficit, Max Deficit Year, Overall Deficit Rate, Avg Interest Rate, Debt Growth Rate\nOverall Deficit Rate = Total Deficit / Total Budget × 100 (2 decimals). Avg Interest Rate = Total Interest / Avg Debt × 100 (4 decimals). Debt Growth Rate = (FY2024 Debt - FY2015 Debt) / FY2015 Debt × 100 (2 decimals).

Web Search, Data Entry / Import, Structuring / Formatting, Calculation, Reporting / Visualization


I am working on a tracking report regarding the biotechnology and pharmaceutical companies that went public on NASDAQ in 2024, namely: CG Oncology, Zenas BioPharma, Upstream Bio, MBX Biosciences, and Metagenomi. I want to start by organizing some data. Please help me find out the listing date (as in yyyy/mm/dd), listing board(full name), initial offering price, total funds raised in 2024. Additionally, I need the revenue, net income attributable to shareholders and R&D expenses disclosed in their 2024 annual reports for these companies. All amounts should be in United States dollars, numerical only, and retained to two decimals. If no relevant data can be found, please fill in with N/A.\n\n[Data Time Baseline] Please retrieve data based on each company's 2024 10-K annual report (versions published before March 31, 2025). For 'Total Funds Raised in 2024', only count IPO initial offering proceeds (including over-allotment), excluding subsequent follow-on offerings.\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the following column names in sequence:\nCompany Name, Listing board, Bloomberg ticker, Listing Date, Initial Offering Price(per share), Total Funds Raised in 2024(million), Revenue in 2024(million), Net Income Attributable to Shareholders(million), R&D Expenses(million)\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily.\n\nBased on the RawData sheet, complete the following 4 subtasks:\n\nSubtask 1 - Sheet2_Financial_Metrics: Calculate financial performance indicators.\nColumns: Company Name, Revenue in 2024(million), Net Income Attributable to Shareholders(million), R&D Expenses(million), R&D Intensity(%), Net Profit Margin(%), Capital Efficiency Ratio, Burn Rate Category\nR&D Intensity = R&D Expenses / Revenue x 100 (2 decimals). Net Profit Margin = Net Income / Revenue x 100 (2 decimals). Capital Efficiency Ratio = Revenue / Total Funds Raised in 2024 (4 decimals). Burn Rate Category: \"Profitable\" if Net Income >= 0, \"Moderate Burn\" if -100 < Net Income < 0, \"High Burn\" if Net Income <= -100, \"Unknown\" if data missing.\n\nSubtask 2 - Sheet3_Data_Quality: Validate data completeness.\nColumns: Company Name, Has Total Funds Raised in 2024, Has Revenue in 2024, Has Net Income Attributable to Shareholders, Has R&D Expenses, Data Completeness(%), Quality Rating, Missing Fields Count\nFor each field: \"Yes\" if data exists, \"No\" if N/A or empty. Data Completeness(%) = (Number of fields with data / 4) x 100 (2 decimals). Quality Rating: \"Excellent\" if 100%, \"Good\" if >=75%, \"Fair\" if >=50%, \"Poor\" if <50%.\n\nSubtask 3 - Sheet4_IPO_Timeline: Reorganize by chronological order.\nColumns: Company Name, Bloomberg Ticker, Listing Date, Initial Offering Price(per share), Total Funds Raised in 2024(million), IPO Quarter, Days Since First IPO, IPO Sequence\nSort by Listing Date (earliest first). IPO Quarter: Q1/Q2/Q3/Q4 based on month. Days Since First IPO: days elapsed since CG Oncology's IPO on 2024/01/25 (integer). IPO Sequence: chronological order number 1, 2, 3... (integer).\n\nSubtask 4 - Sheet5_Comparative: Conduct comparative analysis with ranking.\nColumns: Company Name, Total Funds Raised in 2024(million), R&D Expenses(million), Funds Raised Rank, R&D Expense Rank, Market Position, Investment Scale, Composite Score\nRanks: 1=highest among valid values, \"N/A\" if data missing. Market Position: \"Premium Tier\" if IPO price >= $19, \"Mid-High Tier\" if >= $17, \"Standard Tier\" otherwise. Investment Scale: \"Large Scale\" if funds >= $400M, \"Mid Scale\" if >= $250M, \"Unknown\" otherwise. Composite Score: (Funds/437)x50 + (R&D/139.14)x50 (2 decimals), \"N/A\" if both values missing.

Web Search, Data Entry / Import, Structuring / Formatting, Calculation, Validation / Review


Add a top border to all values in the Summary tab that are calculated as the sum of other rows.

Investment: Trading And Position Management / Investment: Credit / Investment: Pricing And Valuation


On December 11, 2001, a trading firm enters into a long Cinergy–PJM‑W basis spread at the respective mid prices: it goes long 100 MW Jul–Aug 2002 baseload at Cinergy and short 100 MW Jul–Aug 2002 baseload at PJM‑W. Two weeks later, market prices move to: Cinergy Jul–Aug 2002 quoted at 49.00/49.25, and PJM‑W Jul–Aug 2002 quoted at 54.00/54.25. You are required first to use the original quotes to calculate the mid prices at both hubs and the initial spread, then use the new quotes to calculate the new mid prices and the new spread. Next, compute the mark‑to‑market P&L over the two‑week period for this 100 MW spread position (showing the P&L on the Cinergy leg, the PJM‑W leg, and the combined position). Finally, based on the change in the spread, determine whether Cinergy has strengthened relative to PJM‑W or vice versa, and discuss, assuming this spread was intended to hedge the risk of “selling power in PJM‑W and buying power in Cinergy,” whether the hedge performed effectively and why.

Investment: Pricing And Valuation / Investment: Trading And Position Management


At the Cinergy hub, a retail power company wants to hedge a 50 MW baseload position for the year 2002. It has two alternatives: Strategy A is to buy 50 MW of the Jan–Dec 2002 annual strip at the mid price; Strategy B is to buy, also at mid prices, 50 MW of each of the seven seasonal strips listed above, so that together they cover the entire year. You are asked first to compute the mid price of each seasonal strip and of the annual strip. Then, for Strategy B, calculate for each strip the total energy volume (in MWh) and the total cost, and use these to derive the volume‑weighted average hedge price for the whole year under Strategy B. Finally, compare this average price with the annual‑strip mid price under Strategy A, determine which strategy has the lower nominal hedging cost and by how many $/MWh, and, taking into account liquidity, execution complexity, and the shape of the seasonal load profile, explain which strategy you would recommend and why.


Complete both the Flat and Peak tables by using the provided Direct Sales contract data and monthly Curve Prices. For each deal, calculate the corresponding monthly MWh. Populate the monthly MWh values under the appropriate counterparty rows, compute the Total MWh for each month, and then calculate the monthly Value using Total MWh × Curve Price. The final deliverable is a complete, accurate, and audit-ready monthly summary of MWh and Value for both Flat and Peak products


For September–December 2001 and September–December 2002, compute the monthly ratios of ERCOT peak power prices to NYMEX natural gas prices. Then calculate, for each period, the average ratio and the standard deviation (as a measure of volatility). Based on these results, determine in which period power prices were more “stable” relative to gas prices (using lower ratio volatility as the criterion).

10_Daily Forward Price Curves 10262001.xlsx


From the PJM dataset for January 1997 through April 2000, identify all months that simultaneously satisfy the following three conditions:

Peak Demand > 45,000

Megawatt Daily Pricing Average > 30

Henry Hub gas price > 2.5


Run the holding-period return analysis for ChinaBond Export‑Import Bank debt assuming a 0.5‑year hold, comparing the 1‑year vs. 4‑year maturities. Use the scenario where, over the next six months, the 1Y yield shifts up by 1bp and the longer tenor shifts up by 6bp, with current coupons set at 1.25% and 1.75%, respectively.


Finalize the stock-selection model by completing the cross-sectional ranking formulas in columns K/L/M on Sheet1, with M aligned to the one-year Sharpe ranking and K/L aligned to their respective factor columns per the headers. Once the formulas are in place, use the results in column O to populate Sheet2 with the final selections—two columns (stock code and weight)—showing only those with weights greater than zero.


Use publicly available market/financial data to populate Sheet1 columns F, G, and H—namely the 12‑month dividend yield, last quarter YoY profit growth, and current quarter YoY profit growth—for each constituent security. No other changes are required to the workbook.


Based on the Canada – Non-Commercial roster, prepare a headcount summary by functional area, showing how many employees fall into Group 1, Group 2, and Group 3 in each department.


Update the Canada Non-Commercial functional distribution table, ensuring the table remains aligned with the latest staffing data in Sheet2. Additionally, add two columns — Total and %Total — to display the total headcount and percentage for each group.

Structuring / Formatting, Calculation, Validation / Review


Standardize all date entries in the spreadsheet to the 01-FEB-2002 format (dd-MMM-yyyy). Month headers should read consistently as 01-JAN-2002, 01-FEB-2002, etc. Pay attention to case sensitivity.

errol_mclaughlin_jr__10054__0621 Projected Effect on US Books_00.xlsx


Create a new Transfer worksheet that summarizes LONGS, SHORT, and NET by delivery month, with Total Longs, Total Shorts, and Total Net at the bottom; the month list should follow the Z, F1… sequence covering all months present, and show 0 for months with no data. In Access Trades, add a side display for TOTAL LONG and TOTAL SHORT with two Notes columns to confirm these tie to the Transfer totals (“ok” if consistent, otherwise note the variance), and return the updated workbook including the Transfer summary and the Access Trades validation block.


On Sheet4, create two monthly, time-aligned summaries: 'Peoples Deal Value' should sum the Mid Value by month from the 'Peoples Baseload Sale' and 'Transport' sheets (same-month totals), with months without data shown as 0 and a grand total at the end; 'PERC Deal Value' should sum the Mid Value by month from the 'PERC' sheet, with months without data shown as 0. Ensure the month sets for both tables are aligned and sorted chronologically.


Insert a 'Total Available' row and a 'Total Allocated' row, then complete the reconciliation line to verify that 'Total Available' and 'Total Allocated' are consistent.

Structuring / Formatting, Validation / Review, Calculation


Add the 2/11/2000 column on the Feb 00 tab by mirroring the 2/10 data, with the following adjustments: Houston Pipe Line should be 6,000 (instead of 9,000), and for Huntsville and Woodlands use 1,000 and 5,000 respectively. All other items remain unchanged from 2/10.

Data Entry / Import, Structuring / Formatting


On Sheet3, append the month-over-month percentage changes for BSCTMP and SBSK, then compute the Pearson correlation between their monthly price change rates.


On Sheet3, insert on the left side of the sheet a results table with a top-header showing the series names “BSCTMP” and “SBSK,” and two subsections with left-hand labels “1–6 month lag” and “1–6 month lead. In the 1–6 month lag section, quantify the timing relationship by calculating Pearson correlations between SBSK and BSCTMP shifted 1–6 months later and entering the resulting coefficients under the headings “1 month lag” through “6 month lag.” In the 1–6 month lead section, calculate correlations between BSCTMP and SBSK shifted 1–6 months later and entering the coefficients under the headings “1 month lead” through “6 month lead.


Update the data in 'tracy_geaccone__40572__EA Allocations to Other BUs-9.20.01.xlsx' based on 'tracy_geaccone__40573__EA Alloc to Other BUs - Support_04.xlsx'.

Cross-sheet Retrieval, Data Entry / Import


Calculate the total FTE percentage by region and by business line, and roll these up into a consolidated summary. This should include totals across EWS, EES, EGM, M&A, and EGA for each region (US Energy, Canada, Federal), with an overall summary of the combined FTE percentages.

james_steffes__12910__Enron Govt Affairs - EES & ENA_0.xlsx


Using the daily PGE/SCG spot midpoints, the TW base price, the fuel‑loss factor, the fixed commodity cost (0.0246), and the day’s delivered volumes to PGE and SCG, please calculate Adj TW Per, the net unit spreads at PGE and SCG, the theoretical profit (spread × volume), the daily total profit, and the Astra/TW 70%/30% profit split, and roll these up to a full‑month summary. Among these values, keep four decimal places for Adj TW Per, the net unit spreads at PGE and SCG, and keep two decimal places for all other figures.


Recompute and update the entire workbook under the new operating assumptions, without changing the original price and volume inputs (PGE/SCG Midpoint, TW benchmark price, and commodity cost at 0.0246). Reduce the TW→PGE fuel loss factor to 4.0% and change the profit split to Astra 60% / TW 40%, which better reflects joint sharing of market and scheduling risk.


For each record, use the Frequency to place the Rent amount into the corresponding Total Weekly, Total Biweekly, or Total Monthly column, and leave the non-applicable total columns blank. Then roll up the Sub-Total for each frequency and estimate the Annual Total.

Cross-sheet Retrieval, Data Entry / Import, Calculation


Update the Active Deals vs Headcount sheet with the monthly Active Deals and headcount (HC), and capture the growth amount and the percentage increase from Sep 99 to Mar 01.

sally_beck__35543__Energy Ops 98-01 headcount_0.xlsx

Data Entry / Import, Calculation, Cross-sheet/file Retrieval


Using the trades as the source, identify and fill in the applicable asset and liability items for ENA Corp and ECT Inv, then roll them up into a summary. Please ensure the entries reflect only the portions relevant to each entity.

2_ECTI and ENA Credit trades 31 Dec 01_0.xlsx

Data Entry / Import, Cross-sheet/file Retrieval, Calculation


Please track and compile the average monthly house prices for England, Scotland, Wales and Northern Ireland, for the period from January 2022 to April 2025 (including January 2022 and April 2025). Please cite all the statistics from government websites.\n\n[Data Source Note] Please use the UK House Price Index (HPI) data published on GOV.UK / HM Land Registry or ONS official sources. Note: HPI data is periodically revised; data from different publication dates is acceptable as long as sourced from official government websites. For data from February 2024 onwards, rounded values (to nearest thousand) from ONS bulletins are acceptable.\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the following column names in order: Month, House price in England(Pounds), House price in Scotland(Pounds), House price in Wales(Pounds), House price in Northern Ireland(Pounds)\nFor the column 'Month', list the month in the format 2022/01, 2022/02.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily.\n\nBased on the RawData sheet, complete the following 4 subtasks:\n\nSubtask 1 - Sheet2_Quarterly_Aggregation: Convert monthly data to quarterly averages by region.\nColumns: Quarter, England Avg Price, Scotland Avg Price, Wales Avg Price, Northern Ireland Avg Price\nQuarter format: 2022-Q1, 2022-Q2, etc. Each price value with 2 decimals. Sort chronologically from 2022-Q1 to 2025-Q2.\n\nSubtask 2 - Sheet3_Price_Changes: Calculate month-over-month price changes.\nColumns: Month, England MoM Change (%), Scotland MoM Change (%), Wales MoM Change (%), Northern Ireland MoM Change (%)\nAll percentages with 2 decimals. Start from 2022/02 (comparing to 2022/01).\n\nSubtask 3 - Sheet4_Price_Classification: Classify each region's price level for each month.\nColumns: Month, England Category, Scotland Category, Wales Category, Northern Ireland Category\nClassification rules: \"Very High\" if >= 310,000 (England), >= 200,000 (Scotland), >= 225,000 (Wales), >= 190,000 (Northern Ireland); \"High\" if >= 300,000 / >= 190,000 / >= 215,000 / >= 180,000; \"Medium\" if >= 290,000 / >= 180,000 / >= 205,000 / >= 170,000; \"Low\" otherwise.\n\nSubtask 4 - Sheet5_Summary_Statistics: Calculate summary statistics for each region.\nColumns: Region, Max Price, Max Price Month, Min Price, Min Price Month, Overall Change (%), Peak-to-Current Change (%), Volatility Score\nOverall Change = (Price in 2025/04 - Price in 2022/01) / Price in 2022/01 x 100 (2 decimals). Peak-to-Current Change = (Price in 2025/04 - Max Price) / Max Price x 100 (2 decimals). Volatility Score = STDEV.P (population standard deviation) of all monthly prices / AVERAGE x 100 (2 decimals). Max/Min Price with 2 decimals.


Compare the month-to-month movement direction of ERCOT peak power prices and NYMEX natural gas prices in 2002 (i.e., whether each month is higher or lower than the previous month). Based on the 12 monthly changes in 2002, determine how many months the two prices moved in the same direction and how many months they moved in opposite directions.


Standardize the font across all data entries so they ues a single, consistent typeface throughout. No content changes are required; this is a formatting cleanup only.


What is the TW EOL charge for 2002? Pls just provide the amount.

tracy_geaccone__40511__ETS - Transwestern IC 0914.xlsx


How many plants are recorded in the spreadsheet?


Audit the workbook and correct the formula errors in place so numbers calculate properly.


Audit the Deal Sheet in the workbook and correct the formula errors in place so numbers calculate properly.

Planning And Budgeting/Investment: Trading And Position Management


At the end of the table, add two summary rows labeled 'Total Disbursements' and 'Net Receipts (Disbursements)'.


Assume that in January 2002 you enter a “portfolio” by going long ERCOT peak power and short an equal-scale amount of NYMEX natural gas. Over the full year 2002, does this portfolio make or lose money, and what is its nominal return?


Complete the content in the summary sheet based on other spreadsheets. Leave blank if no information found.

Cross-sheet Retrieval, Calculation, Data Entry / Import


You are given an Excel table (Figure 1.19) showing, for IDA-eligible countries, disbursements, amortization, and interest on public and publicly guaranteed debt in 2021–2023 by creditor type (Multilateral, IMF, Bilateral, Commercial bank and other, Bondholders), as well as 2021–2023 totals. At the bottom of the table, the total PPG long-term debt net transfers for 2022 and 2023 are reported.

Using only the data in the table, please:

Produce at least one chart that displays disbursements, amortization, and interest for each creditor type in 2021–2023.

Write a structured economic analysis of no more than 300 words, summarizing the main trends, key turning points, and possible financing behavior patterns. In your analysis, calculate and cite the increase in total long-term debt net transfers in 2023.


Convert the 'EOTT TX-NM Facilities' worksheet into a standard table and filter it to display Andrew’s county only.

larry_campbell__20958__EOTT tanks tx NM.xlsx


Complete the Existing Deals with Huber and Proposed Deals with Huber summaries by cross-referencing the Exposure sheet to capture each contract’s start and end dates, delivery point, and contract price.
Calculate the average of 55-day payables at each designated time point, and aggregate these amounts into the summary totals corresponding to their respective time periods.

mark_whitt__25254__Huber - Existing & Proposed Deals 111201_3.xlsx


Prepare a stacked area chart titled "Existing and Proposed Deals with Huber", showing monthly time on the X-axis (from December 2001 to December 2006) and daily gas supply volumes (MMBtu/d) on the Y-axis in the Summary sheet. Each colored band should represent a distinct pricing benchmark or contract type (deal), illustrating how volumes vary over time

mark_whitt__25254__Huber - Existing & Proposed Deals 111201_4.xlsx

Reporting / Visualization, Cross-sheet/file Retrieval


Create a stacked area chart titled “Rolling 55 Day Payables (as of the 25th of the Month)” to visualize rolling payable amounts across multiple natural gas price indices from December 2001 to December 2006 in the Summary sheet. The Y-axis should be dollars and the X-axis monthly time, with each data point representing the rolling 55-day payables total as of the 25th of that month.


Update the weekly sheet’s rent table to include four new columns — “Total Weekly,” “Total Biweekly,” “Total Monthly,” and “Grand Total” — and populate each tenant’s amounts by week, every two weeks, and monthly, leaving missing items as blank cells. At the bottom, add a “Sub-total” row that sums each column, a “Number of Periods” row reflecting the annual payment count for each tenant based on their rent frequency, and an “Annual Totals” row that calculates the yearly totals for each column; the “Grand Total” column should only be populated on the “Annual Totals” row and remain blank elsewhere.


Update the Beaumont Worksheet to reflect a Base Volume assumption of 17,000 (from 18,000) and roll the change through all affected calculations and summaries. Ensure the threshold/labels (e.g., “>17,000”) and the excess/deficit, buyback, swing, baseload, and invoice totals are consistent with the new base.


Update the 'overview' worksheet with the new data located to the right of the 'to be moved to line:' label on the 'support for reclass of ENA' sheet. Also insert a 'TOTAL EWS' line under the top header.

sally_beck__34355__11-30 DPR first cut_01.xlsx

Data Entry / Import, Structuring / Formatting, Cross-sheet/file Retrieval


Audit the consolidated 2002 plan workbook and correct the formula errors and omissions so the subtotals and roll-ups calculate properly.

john_lavorato__16560__Revised 2002 Consol Plan Review Template.xlsx


Shift the merged title so it begins in column B rather than A to align with the table, and delete all rows that are not marked "violation."


Create four grey area charts for the AGA Storage Report – Working Gas year-over-year comparison, showing the weekly difference versus the same week last year (in Bcf). Place them on Prod_Yr-Yr (Producing), East_Yr-Yr (East Consuming), West_Yr-Yr (West), and Total_Yr-Yr (Total), with the x-axis running through the week ending 9/23.

Reporting / Visualization, Structuring / Formatting


Build four multi-year line charts for “AGA Storage Report – Working Gas,” covering Producing, Consuming East, Consuming West, and Total, that compare weekly Storage Level (Bcf) across 1994/95, 1995/96, 1996/97, 1997/98, 1998/99, and 1999/00. Use Bcf on the Y-axis and Week Ending (from 5-Nov to 21-Oct) on the X-axis, include a vertical dashed line separating WINTER and SUMMER, and place each chart on sheets named Producing, East, West, and Total.


Update the NC_PL worksheet so its formatting matches the style used on NC_BS.


Add a new worksheet titled “P&C” and build a Property & Casualty Insurance Highlights page modeled after the other summary tabs. Set the header at the top (American Financial Group; Property & Casualty Insurance Highlights; (In millions)) and lay out Fourth Quarter 2003 vs. 2002 and Twelve Months Ended 2003 vs. 2002 with an “Inc (Dec)” column, consistent with the existing format.

Structuring / Formatting, Reporting / Visualization


Using the Cleburne Plant Damage Sensitivities, evaluate the financial impact at plant capacities of 240MW, 245MW, 258MW, and 263MW. For Q1–Q4, compute and compare the implied equity value at the contract effective date and subsequent dates, determine damages per Section 11.02(i)(a) under current conditions, assess the impact of modifying the interest rate adjustment on the capacity payment, and the result if the interest rate adjustment is fully removed, then quantify the deltas versus the 263MW baseline to show sensitivity of equity value and damages.

richard_sanders__31745__Tick Analysis @ 14-Nov-00_3.xlsx

Financial Modeling, Calculation, Reporting / Visualization


Compute the Monthly Volume and, under different Prime Rate and Corporate Rate assumptions, calculate the monthly Carrying Cost and resulting P/L, then provide the annual roll-up summary.


Identify and populate, for each Trader, the corresponding Book Organization for both Power and Natural Gas, using the short-form names only. The update should reflect the abbreviated book codes aligned to each trader (e.g., NETCO power books and PWR-GAS gas books).

kam_keiser__18064__UBS Master Book List_0.xlsx

Data Entry / Import, Cross-sheet/file Retrieval


Under the assumptions of Scenario 1, calculate and populate—for each gas point—the total position (contracts), the 30‑day average standard deviation, and the P&L (US$), then aggregate to a grand total for P&L.


Apply Scenario 2 to calculate the current positions, the 30-day standard deviation and the P&L (US$) for each gas point and then provide the aggregated total for P&L. This should reflect the Scenario 2 convention.


Using the daily Crude Oil and Natural Gas prices recorded in the NYMEX Settlement Price Averages table, calculate the required averages and the settlement reference prices, and populate the blank fields at the bottom of the table. Format CRUDE OIL values to three decimal places and Natural Gas values to five decimal places. If that particular statistic doesn’t apply to a given column, just fill in “N/A.”


I want to compare the labor market between four southern US states, namely Alabama, Georgia, Louisiana, and Mississippi. I need to compile monthly employment data not seasonally adjusted for these states from January 2024 to June 2024 (including January 2024 and June 2024). Please provide the following:\nUnadjusted Unemployment Rate\nLabor Force Participation Rate\nAll Employees – Total Nonfarm (Thousands of Persons)\nAll Employees – Manufacturing (Thousands of Persons)\nAverage Hourly Earnings of All Employees (Total Private)\nAverage Hourly Earnings of All Employees (Manufacturing)\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the following column names in order:\nStates, Statistical Month, Unadjusted Unemployment Rate(%), Labor Force Participation Rate(%), All Employees – Total Nonfarm(in thousands), All Employees – Manufacturing(in thousands), Average Hourly Earnings of All Employees (Total Private), Average Hourly Earnings of All Employees (Manufacturing)\nFor Statistical Month, use the format yyyy-mm, like 2024-06.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily.\n\nBased on the RawData sheet, complete the following 4 subtasks:\n\nSubtask 1 - Sheet2_State_Ranking: Rank states by overall labor market health (6-month average).\nColumns: Rank, State, Avg Unemployment Rate (%), Avg Participation Rate (%), Avg Total Employment (thousands), Avg Manufacturing Employment (thousands), Avg Wage - Private ($), Avg Wage - Manufacturing ($), Labor Market Health Score\nHealth Score = (100 - Avg Unemployment) + Avg Participation + (Avg Wage Private - 25). All percentages 2 decimals, employment figures 1 decimal, wages 2 decimals, health score 2 decimals. Sort by health score descending.\n\nSubtask 2 - Sheet3_Monthly_Trends: Calculate month-over-month changes for each state (Feb-Jun, 5 months per state = 20 rows).\nColumns: State, Month, Unemployment Change (pp), Participation Change (pp), Total Employment Growth (%), Manufacturing Employment Growth (%), Wage Growth - Private (%)\nUse formula (Current-Previous)/Previous×100 for growth rates. All values 2 decimals. Sort by State then Month.\n\nSubtask 3 - Sheet4_Employment_Structure: Employment composition analysis for each state (June 2024 snapshot).\nColumns: State, Total Nonfarm Employment (thousands), Manufacturing Employment (thousands), Non-Manufacturing Employment (thousands), Manufacturing Share (%), Total Employment 6M Growth (%), Manufacturing 6M Growth (%)\nNon-Manufacturing = Total - Manufacturing. Share = Manufacturing/Total×100. 6M Growth = (Jun-Jan)/Jan×100. Employment 1 decimal, percentages 2 decimals.\n\nSubtask 4 - Sheet5_Wage_Analysis: Wage premium and volatility analysis by state (6-month statistics).\nColumns: State, Avg Private Wage ($), Avg Manufacturing Wage ($), Wage Premium ($), Wage Premium (%), Wage Volatility - Private ($), Wage Range - Private ($), Premium Category\nPremium = Mfg Wage - Private Wage. Premium % = Premium/Private×100. Volatility = STDEV. Range = MAX - MIN. Category: \"High Premium\" if Premium % > 5%, \"Medium Premium\" if 0-5%, \"Low Premium\" if 0 to -5%, \"Negative Premium\" if < -5%. All wages 2 decimals."


Please compare the growth trajectory of the leading global e-commerce platforms by compiling their quarterly performance for 2024—Amazon, eBay, Shopee (Sea), Alibaba. Use figures from official financial reports for publicly traded companies; for privately held firms, cite reputable news sources and mark relevant numbers with an asterisk (*) to denote that they are estimates. For information you can't find online, just fill in with \"–\".\n\n[Data Source Note] Please use official financial reports for FY2024 (versions published before February 28, 2025). For companies that report in non-USD currencies (e.g., Alibaba), do not recalculate FX yourself—always use the USD figures exactly as stated in the company's official quarterly earnings release, which already apply a fixed exchange rate (the Federal Reserve H.10 rate on or near the quarter-end date). For Alibaba, use the \"Net income\" line (not \"Net income attributable to ordinary shareholders\") as the net profit metric.\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the columns (in this order): Company, Quarter, GMV (USD bn), Revenue (USD bn), Net Profit (USD bn)\n\nFor Quarters, label it simply Q1, Q2, etc.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily.\n\nBased on the RawData sheet, complete the following 4 subtasks:\n\nSubtask 1 - Sheet2_Financial_Indicators: Calculate key financial performance metrics for each company-quarter.\nColumns: Company, Quarter, Revenue (USD bn), Net Profit (USD bn), Net Profit Margin (%), Monetization Rate (%), Profit per Revenue Dollar ($), Profitability Status\nNet Profit Margin = (Net Profit / Revenue) x 100 (2 decimals). Monetization Rate = (Revenue / GMV) x 100 (2 decimals, \"N/A\" if no GMV). Profit per Revenue Dollar = Net Profit / Revenue (4 decimals). Profitability Status: \"Highly Profitable\" if Net Profit >= $5bn, \"Profitable\" if >= $0, \"Near Breakeven\" if >= -$0.1bn, \"Loss Making\" if < -$0.1bn, \"Unknown\" if missing.\n\nSubtask 2 - Sheet3_Comparison_Matrix: Restructure data into company comparison matrix (pivot structure).\nColumns: Metric, Quarter, Amazon, eBay, Shopee (Sea), Alibaba\nCreate 3 metric blocks (12 rows total): Revenue (USD bn) for Q1-Q4, Net Profit (USD bn) for Q1-Q4, Net Profit Margin (%) for Q1-Q4. All values: 2 decimals, \"N/A\" if unavailable.\n\nSubtask 3 - Sheet4_Growth_Classification: Annual growth analysis and classification by company.\nColumns: Company, Total Revenue (USD bn), Total Net Profit (USD bn), Avg Quarterly Revenue (USD bn), Avg Quarterly Profit (USD bn), Q1-Q4 Revenue Growth (%), Q1-Q4 Profit Change (%), Revenue Growth Category, Profit Trend, Avg Business Model Efficiency\nQ1-Q4 Revenue Growth = (Q4 Revenue - Q1 Revenue) / Q1 Revenue x 100 (2 decimals). Profit Change: output as a percentage string with two decimals (e.g., \"92.31%\"), or \"Turnaround\" if it went from loss to profit. Revenue Growth Category: \"High Growth\" if >=30%, \"Moderate Growth\" if >=10%, \"Stable\" if >=0%, \"Declining\" if <0%. Profit Trend: \"Consistently Improving\" if all quarters increase, \"Declining\" if all decrease, \"Overall Improving\" if Q4>Q1, else \"Volatile\". Business Model Efficiency = Avg Revenue / Avg GMV (4 decimals, \"N/A\" if no GMV).\n\nSubtask 4 - Sheet5_Data_Validation: Validate data quality and identify anomalies.\nColumns: Company, Quarter, Has GMV, Has All Financial Data, Data Completeness (%), Validation Status, Quality Score, Quality Grade\nHas GMV: \"Yes\"/\"No\". Has All Financial Data: \"Yes\" if Revenue and Net Profit exist. Data Completeness = (sum of available fields / 3) x 100 (2 decimals, counting GMV, Revenue, Net Profit). Quality Score: starts from Completeness, -10 if no GMV, -5 per issue (2 decimals). Quality Grade: \"Excellent\" if >=90, \"Good\" if >=70, \"Fair\" if >=50, \"Poor\" if <50.


Fill in the cells highlighted with a blue background, and then remove the background color.

Data Entry / Import, Structuring / Formatting, Cross-sheet/file Retrieval, Calculation


Switch the timing/rotation model to Method 1 and set the moving average to 120 with a deviation threshold of 35%. After updating the parameters, ensure the model outputs are refreshed to reflect the new settings.

Structuring / Formatting, Calculation, Financial Modeling


Task Background:

Global growth is expected to remain steady for the foreseeable future despite escalating geopolitical tensions and heightened uncertainty surrounding global trade policy. Global inflation is expected to moderate, allowing central banks to ease monetary policy to support economic activity. However, the outlook remains subdued for low- and middle-income countries (LMICs), particularly for those with low creditworthiness, constrained fiscal space, and significant political and social unrest. Risks to the macroeconomic outlook for LMICs are tilted to the downside, including the escalation of armed conflicts, further trade fragmentation, persistent global inflation, a weaker global risk appetite, and slower-than-expected growth in major LMICs, especially China.

In 2023, LMICs accumulated significant additional debt and faced the corresponding heavy debt service burden. Although global interest rates are declining, debt service costs are expected to moderate gradually. However, LMICs' debt outlook still carries downside risks. The growing prominence of nontraditional creditors, particularly the accumulation of debt owed to China, complicates debt resolution. This is especially critical for small states with underdeveloped domestic financial markets. Furthermore, higher borrowing costs and increased debt service burdens may exacerbate fiscal challenges, especially in LMICs with tightening fiscal policies.

Task Objective:

Based on the provided spreadsheet data, write a report. The report should include the following:

Chart Design and Analysis:
For each spreadsheet provided, create at least one chart based on the data.

Report Writing:
Use the data from the provided spreadsheets to draft the report. Each spreadsheet should be analyzed, and for each one, at least one chart should be generated.

Final Output:
Save the final report as a PDF document.


Review and update the Year-End Goal Progress section of the Weekly Commodity Logic Report by tracking the KPIs established at the beginning of the year. Make sure the completion percentages for Potential Revenue-Generating Transactions, $ Financed Through Bank Logic, and Paying Customers Using Production reflect the most current totals.

mark_taylor__25070__Weekly Marketing Report 15_19Oct2001_0.xlsx

Calculation, Data Entry / Import, Cross-sheet/file Retrieval


Add the necessary rows and columns in the file so EBIT is calculated and shown for the 2001 Forecast, 2001 Proforma, and 2002 Plan.

john_lavorato__16560__Revised 2002 Consol Plan Review Template_01.xlsx


Update the December entry: set the nominated date to November 22, the Daily Quantity to 24,516, and the Monthly Quantity to 760,000. Add an Estimated Monthly Quantity column, and at the bottom of the table calculate the total for the months already included and the period average daily quantity.

Data Entry / Import, Structuring / Formatting, Calculation


On the correlation sheet, add derived columns from the BSCTMP, NBSK, and SBSK price series: a “Dollar difference” showing the spread between SBSK and BSCTMP, and the logarithmic growth rate for each of three price series between two consecutive months; retain the original date column at the end.


Complete the missing Interreg co-financing data in the FR finances sheet (Rate, Maximum allocation, Previous payments, Current payment request, and Remaining) for the applicable categories.

Data Entry / Import, Calculation, Reporting / Visualization


Revise the data of 2002 allocation in HR sheet to reflect the figures in HR Detail sheet.

tracy_geaccone__40573__EA Alloc to Other BUs - Support_01.xlsx

Validation / Review, Cross-sheet/file Retrieval


Compile the headcount for each department and update the Master sheet with the department-level totals. If a department has no headcount data, leave the corresponding cell blank.


Based on the latest financials, complete all empty cells in the 'Total Wholesale Cash Flows excluding Prepays' table on Summary Average Term.

louise_kitchen__23041__CFs by Industry and Erating_0.xlsx


Audit the workbook and correct the formula errors in place so numbers calculate properly.


Complete the orange-highlighted cells on the Timing Tracking sheet so they consistently point to the Benchmark Returns sheet. On Benchmark Returns, fill in the previously missing columns using the same logic as the existing columns, validate that the annual breakdown is behaving normally, and add formulas to summarize performance across the full sample period with results flowing back to the tracking view. Assume 250 trading days a year for the full sample period.

Structuring / Formatting, Calculation, Financial Modeling, Validation / Review


Transcribe the content from the pdfinto the Excel file.

Data Entry / Import, Structuring / Formatting

Accounting For Receipts And Payments/Statements


Transcribe the pivot table from the pdf into the Excel file as a single table and add a column as the total of each row.

Investment: Trading And Position Management / Other Accounting


Based on the provided financial data, generate an asset analysis report (a Word document) for Adidas in 2024, with a focus on the reasons behind the growth of total assets. The report should be divided into two parts: Current Assets and Non-Current Assets, describing their changes and the reasons for their growth.


Transcribe the content from the image into the Excel file.


Translate the screenshot from the English PDF into Chinese and save it into a single PDF file.


In the worksheet “Enron Energy Services – Daily Cash Burn Forecast”, extract all non-empty amount cells for Gas Purchases in the week of 12/10/2001. Write these data into Sheet4 in long format:

Column 1: Description
Column 2: Date
Column 3: Value

Ignore blank cells and cells containing “-”.
Use conditional formatting to highlight values greater than 600,000 with a red fill.

Structuring / Formatting, Cross-sheet/file Retrieval


Finalize the Position Sensitivities for Gas (in US$) by calculating and populating each gas point’s total position contracts, average stdev and the potential profit or loss of the positions under price movements of ±2 and ±1 standard deviations over the past 30 days.


Based on the assumptions in the table, build out a complete pro forma model for the real estate investment and populate the capital stack and return analysis as shown.

Investment: Pricing And Valuation / Model Forecasting


Insert blank rows between adjacent tables in the workbook to create spacing.


Using all the amounts listed under “Due from SCs” and “Due to SCs” in the November market-settlement sheets, compute the ISO’s final net exposure over the entire settlement period. You should make use of the following data from both sides: Total Invoiced, Total Collected, Total Paid, and Total Adjustments, and derive the final net receivable or net payable amount based on the complete receivable–payable relationships. Then calculate what percentage this final net amount represents relative to the total invoiced amount on both sides (i.e., the combined Total Invoiced figures). The final answer should report: (1) whether the ISO is net receivable or net payable; and (2) the percentage of the net amount relative to the total invoiced volume, rounded to three decimal places.


According to the specifications in the Strips sheet, aggregate the daily fundamental data for the PJM Interconnection market and compute the corresponding monthly statistics, suitable for power-trading analysis and review.


Add a sheet Table 1.10, showing Total DEL by departmental group. Total DEL is made up of resource DEL excluding depreciation plus capital DEL. Ignore any discrepancies caused by rounding in the last digit.

Calculation, Data Entry / Import, Structuring / Formatting