Complete the validation and indicator calculations as follows: on the Balance Sheet, add a control to ensure TOTAL ASSETS equals TOTAL LIABILITIES AND EQUITY; on the Income Statement (Revenue & Expenses), add an Equity Roll Forward Test to reconcile equity movement and highlight any differences.

Validation / Review, Calculation, Structuring / Formatting


Transcribe the content from the pdf/image into the Excel file.

Data Entry / Import, Structuring / Formatting


Correct the year header on the Five Year Review tab (set the first year to 2000) and bring that tab into alignment with the other worksheets by updating any figures that are inconsistent.

Validation / Review, Cross-sheet/file Retrieval


Please organize the following data for Apple, Amazon, Google, Microsoft, and Netflix over the five-year period from 2020 to 2024: Total revenue (in billions of US dollars), Operating profit according to International Financial Reporting Standards (in billions of US dollars), Operating profit margin, and Free cash flow (FCF, in billions of US dollars). Please quote the data from the financial reports of the company. Mark the data that cannot be found with \"-\". The value should be specified to two decimal places.\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the following column names in sequence:\nCompany, Year, Total revenue (Billion), Operating profit (Billion), Operating profit margin, FCF (Billion)\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_YoY_Growth: Calculate year-over-year growth rates for each company.\nColumns: Company, Year, Total Revenue(Billion), Revenue YoY Growth(%), Operating Profit(Billion), Operating Profit YoY Growth(%), FCF(Billion), FCF YoY Growth(%)\nFirst year (2020) for each company should show N/A for growth rates. All values with 2 decimals.\n\nSubtask 2 - Sheet3_Ranking_By_Year: Reorganize data by year and rank companies within each year.\nColumns: Year, Company, Total Revenue(Billion), Revenue Rank, Operating Profit(Billion), Operating Profit Rank, FCF(Billion), FCF Rank, Market Leader\nWithin each year, sort companies by revenue (highest to lowest). Ranks are integers 1-5. Market Leader: \"Yes\" for the company with highest revenue in each year, \"No\" for others.\n\nSubtask 3 - Sheet4_Five_Year_Summary: Aggregate 5-year statistics for each company.\nColumns: Company, Total Revenue 5-Year Sum(Billion), Average Annual Revenue(Billion), Revenue CAGR 2020-2024(%), Total FCF 5-Year(Billion), Average Operating Margin(%), Operating Profit CAGR 2020-2024(%), Growth Category\nCAGR formula: ((Ending Value / Beginning Value)^(1/4) - 1) × 100 (4 years from 2020 to 2024). Growth Category: \"High Growth\" if Revenue CAGR ≥ 15%, \"Moderate Growth\" if ≥ 5%, \"Stable Growth\" otherwise. All percentages with 2 decimals.\n\nSubtask 4 - Sheet5_Financial_Health: Assess financial health based on 2024 data.\nColumns: Company, Revenue 2024(Billion), Operating Margin 2024(%), FCF 2024(Billion), Profitability Score, Cash Generation Score, Scale Score, Total Health Score, Health Rating, Risk Level\nProfitability Score: 40 if Operating Margin ≥ 30%, 30 if 20-30%, 20 if 10-20%, 10 if < 10%. Cash Generation Score: 30 if FCF ≥ 70B, 20 if 30-70B, 10 if < 30B. Scale Score: 30 if Revenue ≥ 500B, 25 if 350-500B, 20 if 200-350B, 10 if < 200B. Total Health Score = sum of above three scores. Health Rating: \"Excellent\" if Total ≥ 85, \"Good\" if 70-84, \"Fair\" if 55-69, \"Poor\" if < 55. Risk Level: \"Low Risk\" if Total ≥ 85, \"Medium Risk\" if 55-84, \"High Risk\" if < 55.

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


Using the images and the current workbook, please complete the missing entries on the ‘Five Year Review’ sheet. Populate the ‘FIVE YEARS IN REVIEW – FINANCIAL DATA’ lines so the five-year figures and totals are filled in and consistent with the rest of the file. Leave blank if no reference is available for specific parts.

new_finrpt00__EUSES_financial_processed_02_Changes in Fund Balances.jpeg

new_finrpt00__EUSES_financial_processed_02_Changes in Fund Balances.pdf

Cross-sheet Retrieval, Data Entry / Import, Validation / Review, Calculation


Compute the “sum of A” and “sum of B” in the last rows of the Income sheet for each Financial Indicator Component, using data from all sheets in the workbook.


Please write a structured economic analysis report based on the table data I provide.
Requirements:

Base the analysis only on the table content; do not introduce external knowledge.

Identify trends, changes, turning points, and fluctuation patterns during crisis periods in the data.

Analyze relationships between different indicators, such as the connection between time periods and specific indicator changes, or whether certain event years exhibit significant volatility.

Integrate your findings into a formal economic report–style narrative and include clear visual charts, for example:

“During the period of xxx, we observe that...”

“The data indicate that the institution exhibits...”

The report must include:

A summary of trends

Characteristics of special years

Inferred economic behavior patterns

The report should be no more than 300 words.

Figure 1.4 shows that, for low- and middle-income countries over 1971–2023, new World Bank lending (IBRD and IDA, including IDA grants) tends to rise as a share of GNI when GNI growth declines. During major downturns – such as the 2008–09 financial crisis and the COVID-19 pandemic – the ratio of IBRD and IDA lending (and IDA grants) to GNI increases noticeably even as economic growth turns sharply negative. This pattern, which recurs around several episodes of weak growth and external shocks, indicates that World Bank lending has played a countercyclical and stabilizing role, expanding in periods of stress to help cushion dramatic drops in economic activity in eligible low- and middle-income economies.


The report should be translated into English, maintaining a neat layout, and a docx version of the report should be generated.


I am studying how partisan standoffs influence administrative efficiency and the macro-economy, so I need a quantitative overview of every federal government shutdown.\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the columns in this order:\nStart Date, End Date, Duration (days), President, Speaker of the House, Senate Majority Leader, Furloughed Employees, Estimated Loss (USD million) , Main Disputed Provisions\n\nRequirements:\n1. Cover every officially recorded federal government shutdown between October 1976 and December 2024 (including October 1976 and December 2024).\n2. Date should be formatted as YYYY-MM-DD.\n3. Record the President, House and Senate leaders during the shutdown period.\n4. For furloughed employees and estimated loss, give the exact number. If there is no exact number or data is unavailable, fill in with \"–\".\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_Presidential_Era: Aggregate shutdowns by President to analyze patterns across different administrations.\nColumns: President, Number of Shutdowns, Total Days, Average Duration (days), Longest Shutdown (days), Total Furloughed Employees, Total Estimated Loss (USD million), Era Severity Rating\nAverage Duration with 1 decimal. Era Severity Rating: \"High Frequency & High Impact\" if ≥5 shutdowns AND ≥30 total days; \"High Frequency\" if ≥5 shutdowns only; \"High Impact\" if ≥30 total days only; \"Moderate\" if ≥2 shutdowns; \"Low\" otherwise. For totals: \"N/A\" if all source values are \"–\", otherwise sum available numeric values. Sort by presidential chronological order (by order of first appearance in RawData).\n\nSubtask 2 - Sheet3_Timeline_Context: Add chronological context and temporal analysis.\nColumns: Start Date, End Date, Duration (days), Year, Decade, President, Days Since Previous Shutdown, Cumulative Shutdown Count, Duration Category\nYear: extract from Start Date (integer). Decade: \"1970s\", \"1980s\", \"1990s\", \"2000s\", \"2010s\", \"2020s\". Days Since Previous Shutdown: days between previous shutdown's End Date and current Start Date (integer), \"First Event\" for row 1. Cumulative Shutdown Count: sequential number 1, 2, 3... (integer). Duration Category: \"Extended (≥14 days)\" if ≥14, \"Moderate (5-13 days)\" if 5-13, \"Brief (<5 days)\" if <5.\n\nSubtask 3 - Sheet4_Data_Validation: Assess data completeness for each shutdown event.\nColumns: Start Date, President, Has Furloughed Data, Has Loss Data, Has Provisions Data, Data Completeness (%), Quality Rating, Missing Fields\nHas [Field] Data: \"Yes\" if data exists, \"No\" if \"–\". Data Completeness (%): (count of \"Yes\" fields / 3) × 100 (1 decimal). Quality Rating: \"Complete\" if 100%, \"Good\" if ≥66.7%, \"Partial\" if ≥33.3%, \"Poor\" if <33.3%. Missing Fields: comma-separated list like \"Furloughed, Loss\" or \"None\".\n\nSubtask 4 - Sheet5_Impact_Classification: Classify shutdown severity based on multiple impact dimensions.\nColumns: Start Date, Duration (days), President, Furloughed Employees, Estimated Loss (USD million), Duration Impact, Economic Impact, Workforce Impact, Overall Severity Score, Overall Impact Classification\nDuration Impact: \"Severe\" if ≥14 days, \"Moderate\" if 5-13, \"Minor\" if <5. Economic Impact: \"Unknown\" if N/A or \"–\", \"Major\" if ≥1000M, \"Significant\" if ≥100M, \"Moderate\" if ≥10M, \"Minor\" if <10M. Workforce Impact: \"Unknown\" if N/A or \"–\", \"Massive\" if ≥500K, \"Major\" if ≥100K, \"Moderate\" if ≥10K, \"Limited\" if <10K. Overall Severity Score: Duration component (max 40 points) + Economic component (max 30 points) + Workforce component (max 30 points), result with 1 decimal. Overall Impact Classification: \"Critical\" if ≥70, \"High\" if ≥50, \"Moderate\" if ≥30, \"Low\" if ≥10, \"Minimal\" if <10.

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


Review the Inv & WC Value Adj summary tab and add the missing cross‑sheet data references to the other worksheets so the roll‑up pulls the correct figures. Return the updated file with those links in place.


Per the headers and established formula logic, populate formulas for columns X through AH so the timing model’s performance statistics for 2013–2025 are complete and consistent with the existing approach.


Add a NAV chart above the Strategy Performance table, plotting two series: Rotation NAV and Benchmark. Use a red line with red markers for Rotation NAV and a gray line with gray markers for the Benchmark; start both series at a base value of 1, and set the time axis to show annual ticks.


Per the red parameters and the Method 1/Method 2 guidance noted in H8 and H9, complete the formulas in columns T and U (starting from1) and then complete column I. The method selection in B6 should drive the model so that all cells and charts refresh consistently when switching between methods.

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


Complete the missing formulas in columns E and F on the sheet Holding Period Return Analysis.
This task uses the yield curve on the given date to compute the annualized return over the specified holding period for a pair of virtual bonds.
The calculation steps should follow the logic shown in column D, and the yellow-highlighted cells are input parameters.
The model should update the virtual bonds’ holding-period return dynamically when these parameters change.

Compute the “Initial Full Price” by setting the coupon rate equal to the current yield.

Determine the “Remaining Maturity at the End of the Holding Period” based on the parameters in the table.

Use the cash-flow discounting formula and the corresponding parameters to compute the “Final Full Price.”


Suppose we need to hold a 0.5-year AA(2) municipal investment bond. Using the model, compare the holding-period returns of the 2-year and 4-year tenors. It is known that over the next half year, the 1-year yield will rise by 2 bps, and the 5-year term spread relative to the 1-year will widen by 3 bps.


Translate all Chinese text in this Excel workbook (including sheet names, headers, cell contents, formulas, charts, etc) into English and save the translated version as a new workbook.

Structuring / Formatting, Translation, Reporting / Visualization


Continue writing the report based on the provided spreadsheet data, and generate the subsequent part of the report, saving it as a PDF file that maintains the same format as the original report.

Requirements:

Use the data from each spreadsheet provided to supplement the report.

Update the report section by section based on the spreadsheet data.


For the 10th of the year 2023, what are the Fee IDs applicable to Belles_cookbook_store?

Answer must be a list of values in comma separated list, eg: A, B, C. If the answer is an empty list, reply with an empty string. If a question does not have a relevant or applicable answer for the task, please respond with 'Not Applicable'

741, 709, 454, 813, 381, 536, 473, 572, 477, 286


In January 2023 what delta would Belles_cookbook_store pay if the relative fee of the fee with ID=384 changed to 1?

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'


Complete the missing data in the 'Five Year Review' worksheet of new_finrpt01__EUSES_financial_processed_02.xlsx, ensuring the five-year figures across fund balances, operating revenues, and operating expenditures are fully populated and consistent with the rest of the financial statements. Leave blank if no reference is available for specific parts.

Cross-sheet Retrieval, Data Entry / Import, Calculation, Validation / Review


Create a new spreadsheet named “Exp by Fun Gen Support Chart5” and, on this sheet, build two pie charts based on the two existing data tables.

The top pie chart should show “Expenditures by Function – All Funds”. Use the amounts from the “TOTAL EXPENDITURES” detail lines for each function in the All Funds summary table, calculate each function’s percentage of total expenditures, and make each function one slice. Label each slice with the function name and its percentage, and add a legend with the same function names.

The bottom pie chart should show the “breakdown of General Support Services”. Use the amounts for all rows that belong to the General Support Services function in the detailed table, calculate each item’s percentage of the total General Support Services amount, and make each item one slice. Again, label each slice with the item name and its percentage, and include a matching legend.

Both pie charts should be 3-D and visually well-balanced.

Reporting / Visualization, Cross-sheet/file Retrieval


Add a weekday line directly below the date headers and update the 12/31/2001 (Mon) column. For that day, there are no “Receipts”; record disbursements of $1,980,800 to Calpine (Power Purchases) and $100,000 to an unspecified vendor (Gas Purchases). Under Enron Facility Services, enter $3,101,855 for “$2.5 per day” and -$2,081,386 for “estimate receipt”; in Personnel, EES is $584,500; leave all other items as “-”.


Please review the pivot table on the Replacement Cost sheet against the LiquidationValue sheet to confirm they are consistent. If there are discrepancies, update the Replacement Cost pivot to reflect the LiquidationValue data.

Validation / Review, Structuring / Formatting


Using the pivot table’s monthly Nominal Dollars as the base, compute a fixed 50% Margin Requirement – Fixed NYMEX for each month, then calculate the Cumulative Margin Required as 2x the current month’s margin plus the sum of margins from the next month through the final month. On this balance, apply a 25% annual rate converted to monthly (÷12) to derive the Monthly Cost of Capital – NPW, discount at 3% (APR) annually to obtain monthly present values, and sum those to report the Total Cost of Credit to NPW; if needed, you may validate Nominal Dollars using price × quantity, but for this run use Nominal Dollars.

Investment: Trading And Position Management


Add a new worksheet named "Scenario3" to the same workbook, mirroring the structure, row/column layout, monthly detail table, and chart area of "Scenario1". For Scenario3, update the hedging assumptions to a balanced allocation: 10-Yr 25%, 5-Yr 20%, 1-Yr 15%, May-Sep 20%, Q3 15%. Keep the note "Maximum Monthly Average Short Position to Cover (July Peak) = 30,508 MW" unchanged; only the new sheet should be added, and formulas may be used within it.

Structuring / Formatting, Financial Modeling


Using the daily data for August from the Eron (NC) Service Invoice, verify whether they reconcile with the monthly totals for Net Receipts – Allocated UA4 and Deliveries in the Imbalance Statement. Then compute the final month’s cumulative imbalance.

theresa_staab__39629__Enron Lost Creek Statement Email 0801.xlsx

The monthly Net Receipt of 714,490 matches the sum of the daily Net Receipt figures in the Service Invoice.

The monthly Deliveries of –681,155 also match the “Nominated Delivered Total” in the Service Invoice.

This indicates that the two tables reconcile correctly for August 2001. However, the sign of the Current Month Imbalance is inconsistent.

In addition, the calculated Cumulative Imbalance for August 2001 is –403.

Procurement And Sales/Investment: Trading And Position Management


Using the assumption area plus monthly detail on the Scenario1, Scenario2, and Scenario3 tabs, please add two charts per scenario: a hedge coverage stacked column chart with Month on the X-axis and stacked series (monthly contract MW), with an optional Short reference line; Y-axis in MW; show only the first year. Also add a portfolio cost line chart with Month on the X-axis and series Portfolio Weighted Avg (primary) and Monthly Price (reference), optionally including the 10-Yr/5-Yr/1-Yr Fixed Price lines; Y-axis in $/MWh; include all years.


Input the required data into the EA Alloc to Other BUs – Support workbook and complete any missing formulas on the CABC tab (Analysis of I/C Billings).

tracy_geaccone__40573__EA Alloc to Other BUs - Support_02.pdf

tracy_geaccone__40573__EA Alloc to Other BUs - Support_02.xlsx

Data Entry / Import, Validation / Review, Calculation


Verify the accuracy of the department headcount summary by cross-checking each department’s total number of employees against its corresponding detailed roster sheet. Identify and correct any discrepancies—such as miscounts, missing entries, or remove any departments that no longer exist—and update the summary to reflect the accurate total headcount.


Compile the existing budgets for each department and write each department’s summarized budget into column M of the Master sheet. If a person or department has no budget, treat the budget as 0. If a department does not exist, remove it from the sheet. After all departmental budgets are updated, calculate the total budget across all departments.

Structuring / Formatting, Calculation, Reporting / Visualization


Build comprehensive Q2, Q3, and Q4 calculation tables following the same structure and logic as Q1, applying each task’s specific assumptions to compute the Interest Rate Adjustment, Apache Savings, Adjusted Capacity Rate, Months in the Year, Plant Capacity, Yearly Capacity Payments, and Monthly Capacity Payments for each year from 1997 to 2019.  Finally, provide the Equity Present Value (XNPV5).

richard_sanders__31745__Tick Analysis @ 14-Nov-00_2.xlsx

richard_sanders__31745__Tick Analysis @ 14-Nov-00_4.xlsx


Convert the employee rotation roster into a color-coded rotation plan Gantt chart on Rotation chart. The Rotation Chart tab should display each employee’s assignments by month for 2001 so the schedule is easy to review at a glance. Each color-coded rotation should also be labeled with the corresponding group name. To distinguish between the power and gas groups, add the suffix “-power” or “-gas” to the group names.


Please prepare a summary of all groups and staffing as of March 1, including each group and business line’s supervisor and the number of Analysts (TT) and Associates (TT) on their team. The table should include four columns: Group, Line Supervisor (surname first), No. Analysts (TT), and No. Associates (TT).

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


Gather Enron North America’s Mid Year 2001 performance across all departments into “All Originators by Value” sheet, showing each Originator, Commodity Team, Total, and % Total. Then add addiontal line to include the overall Total and %Total lines in the summary.


Assume the following changes occur in the Jul–Dec 2002 market: Flat curve prices increase uniformly by $2/MWh; Peak 6x16 curve prices increase uniformly by $5/MWh; monthly contract volumes (Flat and Peak Total MWh) remain unchanged. Based on the 2002 table, calculate: (1) the total added value (mark-to-market change) for the combined Flat + Peak portfolio; and (2) what percentage of this added value comes from the Peak 6x16 contracts rather than the Flat contracts.

The total added value of the July–December 2002 portfolio is $1,989,600 (in absolute terms). Of this amount, approximately 27.9% (about 28%) comes from the Peak 6x16 contracts, with the remaining ~72.1% coming from the Flat contracts.


Summarize the volume and dollar imbalances that exist between the various pipeline operators (Operators) and Transwestern.


Prepare and complete the Exposure Table, which is used to track the company’s price exposure across multiple index points. The table should incorporate monthly contracted volumes and corresponding market prices for each pricing index, and calculate 55 days payables for each month using the formula: (prior month’s day count × Volume × Price) + (25 × Volume × Price).

mark_whitt__25254__Huber - Existing & Proposed Deals 111201_0.xlsx

mark_whitt__25254__Huber - Existing & Proposed Deals 111201_1.xlsx

Investment: Trading And Exposure Management


Append two columns to the end of the current explosure table: “Total” and “WA Price” (volumn-weighted average price averaged over the days).

mark_whitt__25254__Huber - Existing & Proposed Deals 111201_1.xlsx

mark_whitt__25254__Huber - Existing & Proposed Deals 111201_2.xlsx


Using the discount rate assumptions in the table and each Shipper’s hurdle rate, term, volume, and annual rates, please complete and calculate the financial metrics for each Shipper—NPV, Actual Rate, Gross Value, cash flows by year and so on.


Please create four charts in the “Graph Data Oct 01” spreadsheet, placing them in the empty area between rows 30 and 80. Please size them evenly so they look balanced and do not overlap.
Chart 1 – Trend of Weekly Errors Rolling 60 Days
Plot the total number of errors for each week from 7/30 to 10/1. Use a line chart, add a linear trendline, and show the data value at each point on the line.
Chart 2 – Summary of Errors by Group for week of 10/1
Create a column chart where the X‑axis is Group (EIM, EGM, EEL, EA‑GAS, etc.). Include two data series: one for “# of errors” and one for “Ratio of Errors to Active Books.”
Chart 3 – Trend of Book Creation Rolling 30 Day period
For each Group, aggregate the Book creation counts by week. Use a stacked column chart with the X‑axis showing each week from 8/27 to 10/1, and the Y‑axis showing the number of books. Each Group should have its own color in the stack.
Chart 4 – Breakout of Errors by Type per Week Rolling 60 Days
Using the rows for each error type, create a stacked column chart by week. The X‑axis should be the weekly periods from 7/23 to 10/5, and the Y‑axis the number of errors. Each error type should be a different color, and the value for each segment should be shown as a data label on the stack.

Reporting / Visualization, Structuring / Formatting


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


Treat “California Power Exchange” and “Pacific Gas and Electric (California Power Exchange) (a)” as a single combined CPX customer group. Reconstruct CPX’s full-cycle cash flows and net position across all sheets from November 2000 through June 2001, and determine whether, as of the certification date, the ISO is net receivable from CPX or net payable to CPX, along with a breakdown of the underlying reasons.

CPX as a whole is a net debtor to the ISO, with approximately $3.028 billion outstanding at period end.


Using the Power Purchase Agreement (PPA) Cost and Savings Table for Cedar Braker, calculate and populate all relevant values in the summary table under the assumption of a 9.13% discount rate. The analysis should compute the Net Present Value (NPV) for each component — including the Old PPA, Market Stranded Investment (SI), New PPA, and PPA Savings — and compare the economic impact of the different scenarios.

richard_sanders__31745__Tick Analysis @ 14-Nov-00_0.xlsx

richard_sanders__31745__Tick Analysis @ 14-Nov-00.xlsx


Complete the Cleburne Plant Damage Sensitivities table task Q1 – At Inception:
First, based on the given assumptions in the table, fill in the Interest Rate Adjustment, Apache Savings, Adjusted Capacity Rate, Months in the Year, Plant Capacity, Yearly Capacity Payments, and Monthly Capacity Payments for each year from 1997 to 2019. Finally, provide the equity present value (XNPV5) as of 12/31/1996 and 12/31/2000.

richard_sanders__31745__Tick Analysis @ 14-Nov-00_1.xlsx

richard_sanders__31745__Tick Analysis @ 14-Nov-00_2.xlsx


On active deals vs headcount sheet, create two separate line charts to illustrate Enron Wholesale Services’ business and HR growth w/o Calgary from 1999 to 2001 :
Chart 1: Active deals by month, showing separate lines for Natural Gas, Power, and Financial deals.
Chart 2: Energy Operations headcount, comparing Adjusted Plan and Actual headcount over time.

sally_beck__35543__Energy Ops 98-01 headcount_2.xlsx

sally_beck__35543__Energy Ops 98-01 headcount_3.xlsx


Compile and populate the Summary sheet with Enron Energy Operations’ departmental annual headcount Plan vs Actual and then add a short analysis of the business drivers behind headcount growth for 1999–2001. Organize the commentary into three sections—Enron Americas, EGM, and EIM—and place it on the right-hand side of the Summary sheet.

sally_beck__35543__Energy Ops 98-01 headcount_4.xlsx

sally_beck__35543__Energy Ops 98-01 headcount_5.xlsx

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


Complete the Financial Ratios section on the Balance Sheet. In addition, infer and populate the revaluation-related asset entries on rows 25 and 26.


Complete the Income Statement (Purchase method) by calculating the Amortization of goodwill and the Amortization of extra depreciation, then update Income before income taxes. Based on that, compute the corresponding income tax, net income, and earnings per share on the Income Statement.


Review the Income Statement for any mis-entered figures and correct them.


Review the workbook and resolve the mismatched figures caused by formulas in the A1:K16 range that were entered without the necessary cell references. Identify the affected cells within that range and correct the formulas so the numbers reconcile.

jeff_dasovich__14032__E220 Take home midterm--Dasovich_01.xlsx

jeff_dasovich__14032__E220 Take home midterm--Dasovich.xlsx


Complete the last few rows for “Inventory turnover” and the related metrics (average time in inventory, receivables turnover, and payables turnover) in line with the calculation relationships described in the sheet notes.

jeff_dasovich__14032__E220 Take home midterm--Dasovich_02.xlsx

jeff_dasovich__14032__E220 Take home midterm--Dasovich_01.xlsx


Identify the data in the chart and save it to a spreadsheet.


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


For each month from December 2000 through April 2012, compute the difference between the Total Monthly Nominal Volume (CNG + TCO) and the Total Monthly Discounted Volume (CNG + TCO) for the long-term natural gas contract. Then compare the Average Daily Nominal Volume for CNG with the Average Daily Nominal Volume for TCO.

Total Monthly Nominal Volume (CNG+TCO): 75,100,712

Total Monthly Discounted Volume (CNG+TCO): 53,458,048

Total Monthly Nominal Volume (CNG+TCO) - Total Monthly Discounted Volume (CNG+TCO) = 21,642,663

Average Daily Nominal Volume (CNG): 11,409

Average Daily Nominal Volume (TCO): 5,619

Average Daily Nominal Volume (CNG) - Average Daily Nominal Volume (TCO)=5,790

Investment: Pricing And Valuation / Risk Management / Procurement And Sales


Take the left-side raw pulp price data for BSCTMP, NBSK, and SBSK and add derived fields to calculate the correlations for BSCTMP vs. NBSK and BSCTMP vs. SBSK, identify the  maximum and minimum values of Dollar difference and record the dates they occur, and report the standard deviation for each of BSCTMP, NBSK, and SBSK price series. Also calculates the annualized standard deviation (volatility) of the monthly log growth rates of  BSCTMP,  NBSK,  SBSK, and present it in percentage terms to show relative volatility.


On the correl_graph sheet, create a time-series line chart comparing BSCTMP, NBSK, and SBSK prices to show how they move relative to each other. Use time on the x-axis to make their correlation visible.


Reference the Summary sheet and restate the SUM-USD sheet on a USD basis. Update all figures and roll-ups in SUM-USD to reflect USD reporting consistent with the Summary.

Structuring / Formatting, Calculation, Cross-sheet/file Retrieval


Review the YTD Recon sheet and compare Canada vs. Houston across Term, Cash, and the overall Total. Summarize the variance month-by-month and for the full year.


On the SUM-USD and Summary tabs, add a right-side data block that consolidates quarterly totals and the full-year total for each row. Please ensure every line is included.


Update the TOTAL PHYSICAL GAS tab to mirror the layout on TOTAL US GAS. Specifically, insert a “% CHANGE FROM LAST 30 DAYS” column on the TOTAL PHYSICAL GAS worksheet.


On the All Natural Gas sheet, create an Excel Ctrl+T table and filter to show only the COUNTERPARTY entries highlighted in red.

kam_keiser__18021__Summary of Book Requests Update_01.xlsx

kam_keiser__18021__Summary of Book Requests Update_02.xlsx


Add two new entries to the peaker plant comps and then refresh the averages. Constellation (Illinois; Electric Power Daily, article 6/20/2000; operation 6/1/2001) with unspecified turbine type, 300 MW, total cost $130MM, and $433/kW; and TVA (Mississippi; Electric Power Daily, article 6/23/2000; operation 6/1/2002) using GE Gas Turbines, 340 MW, total cost $170MM, and $500/kW. After these are entered, update the overall average and the average excluding GE LM 6000 units.

benjamin_rogers__1258__merchant peaker plants version3.xlsx

benjamin_rogers__1259__merchant peaker plants version4.xlsx

Data Entry / Import, Structuring / Formatting, Calculation


For EDF MAN, clear the 'Line of Credit Covering Initial Margin (except EDF Mann…)' and then recalculate any related figures that need to be synchronized. Leave all other content unchanged.


Using RepIS-Qtrly as the base, please create the RepIS-Annual and RepIS-Qtrly YTD schedules, formatted consistent with the other spreadsheets in the workbook.

Cross-sheet Retrieval, Structuring / Formatting


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


For an infrastructure-finance paper, I need to benchmark capital intensity of large offshore wind assets. List every offshore wind farm in European waters that was fully commissioned (i.e., all turbines installed and formally entered commercial operation) from 2010-01-01 to 2024-12-31 and whose nameplate capacity is >= 300 MW. Ignore projects still under construction or phases that are only partially energized / not yet formally fully commissioned as of December 31, 2024.\n\n[Data Source and Methodology Notes]\n1. Data Source: Please use 4C Offshore or WindEurope database records as of October 31, 2024.\n2. Commissioning Boundary (Strict Definition): Projects must have formally announced full commissioning before December 31, 2024. Projects that only completed turbine installation but have not yet formally achieved full commissioning (e.g., Baltic Eagle completed installation in 2024 but full commissioning in July 2025) should NOT be included.\n3. Capacity Convention:\n   - Hollandse Kust Zuid 1-2: 770 MW / 70 turbines\n   - Hollandse Kust Zuid 3-4: 759 MW / 69 turbines (official KEC convention)\n   - Borkum Riffgrund 2: 450 MW (developer convention)\n   - Seagreen: 1075 MW (developer official convention)\n4. Combined Projects: Combined projects (e.g., Gode Wind 1+2, Thornton Bank C-Power phases) are treated as single wind farms.\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the columns in this exact order:\nWind Farm, Sea / Basin, Capacity (MW), Turbines Number, Turbine Model, Commissioning Year, Owner / Operator\nFill missing fields with \"NA\".\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_Turbine_Unit_Economics: Analyze turbine-level economics and efficiency metrics.\nColumns: Wind Farm, Capacity (MW), Turbines Number, Average Turbine Capacity (MW), Capacity Density (turbines/100MW), Scale Category, Turbine Size Category\nAverage Turbine Capacity (MW) = Capacity (MW) / Turbines Number (2 decimals). Capacity Density (turbines/100MW) = (Turbines Number / Capacity (MW)) x 100 (2 decimals). Scale Category: \"Gigawatt-Scale\" if Capacity >= 1000 MW, \"Large-Scale\" if >= 600, \"Mid-Scale\" if >= 400, \"Standard-Scale\" otherwise. Turbine Size Category: \"Ultra-Large (>=10MW)\" if Average >= 10, \"Large (7-10MW)\" if >= 7, \"Medium (5-7MW)\" if >= 5, \"Standard (<5MW)\" otherwise.\n\nSubtask 2 - Sheet3_Geographic_Market: Aggregate projects by sea/basin to analyze regional market structure and concentration.\nColumns: Sea / Basin, Number of Projects, Total Capacity (MW), Average Project Size (MW), Largest Project (MW), Market Share (%), Market Development\nNumber of Projects: COUNT of projects per region (integer). Total Capacity (MW): SUM of all capacities in region (2 decimals). Average Project Size (MW): Total / Number (2 decimals). Largest Project (MW): MAX capacity in region (2 decimals). Market Share (%): (Regional Total / Grand Total) x 100 (2 decimals). Market Development: \"Concentrated\" if <= 2 projects, \"Moderate\" if 3-5, \"Diversified\" if >= 6. Sort by Total Capacity (MW) descending.\n\nSubtask 3 - Sheet4_Technology_Manufacturer: Extract manufacturer from turbine model strings and classify technology generation.\nColumns: Wind Farm, Turbine Model, Commissioning Year, Manufacturer, Manufacturer Type, Unit Capacity (MW), Technology Generation\nManufacturer: Extract from Turbine Model string (e.g., \"Siemens\", \"Vestas\", \"GE\", \"MHI Vestas\", \"AREVA\", \"Senvion\", \"Adwen\", \"REpower\", \"Bard\", \"Other\"). Manufacturer Type: \"Major OEM\" if Siemens/Vestas/GE, \"Established Player\" if MHI Vestas/Senvion/AREVA, \"Specialized/Regional\" otherwise. Unit Capacity (MW): Primarily extract rated capacity from model name (e.g., \"SWT-6.0-154\" → 6.0 MW), even if it differs from farm capacity / turbine count; 2 decimals. Technology Generation: \"Next-Gen (>=10MW)\" if >= 10, \"Gen 4 (8-10MW)\" if >= 8, \"Gen 3 (6-8MW)\" if >= 6, \"Gen 2 (4-6MW)\" if >= 4, \"Gen 1 (<4MW)\" otherwise.\n\nSubtask 4 - Sheet5_Operator_Portfolio: Identify major operators by parsing ownership strings and calculate portfolio metrics. Show top 15 operators only.\nColumns: Operator Name, Number of Projects, Total Capacity (MW), Average Project Size (MW), Portfolio Rank, Market Share (%)\nParse Owner/Operator field (split by \"/\" separator) to identify individual operators. Normalize common operator names (e.g., \"Orsted\"/\"orsted\" -> \"Ørsted\"). Number of Projects: Count of distinct projects in which the operator participates (integer, not split for joint ownership). Total Capacity (MW): SUM of capacities, split equally among co-owners (2 decimals). Average Project Size (MW): Total / Number (2 decimals). Portfolio Rank: 1 = highest total capacity (integer). Market Share (%): (Operator Total / Sum of Top 15) x 100 (2 decimals). Sort by Total Capacity descending, include only top 15 operators.

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


Calculate the Interest Payment fpr enron and fill the correnponding cell.

Calculation, Structuring / Formatting, Financial Modeling


Create a new 'by type_area' worksheet based on the Summary and the other tabs. It should present two separate tables summarized by Imbal Type; within each table, consolidate by area, include Volume, Value and Date, and calculate totals. Finally, confirm that the value and volume totals tie to the totals shown on the Summary.

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


Complete the Summary worksheet by entering the missing data and aligning it to the underlying sheets.

Cross-sheet Retrieval, Data Entry / Import


Please review the workbook and correct the naming errors in the CENT book entries—where a PHY tag should indicate a name with a PB prefix rather than FB. Once corrected in both Short Name and Long Name, set the corresponding row background to blue to flag the changes.

Structuring / Formatting, Validation / Review


Add a separate line item labeled “IT Infrastructure Allocation” under each ENA support department in the 2002 Plan and leave the amounts blank for now. This line will represent the planned IT infrastructure allocations ENA will pass through to the Business Units in 2002.

tracy_geaccone__40184__EA Allocations to Other BUs-10.08.01.xlsx

tracy_geaccone__40205__EA Allocations to Other BUs-10.22.01_01.xlsx


Please list the top 10 individuals from the Forbes Global Billionaires Ranking for each year from 2019 to 2024 (including 2024). For each person, include their name, ranking, net worth, source of wealth, and age of that year end. Present the information in a table format. All your data should come from Forbes, which means the ages and net worth are calculated based on the publication date.\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the column headers in order:\nYear, Rank, Name, Net Worth (in USD billions), Age, Source of Wealth\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_Wealth_Growth: Analyze wealth growth trends and year-over-year changes.\nColumns: Year, Name, Current Net Worth (USD billions), Annual Change (USD billions), Annual Change (%), Cumulative Growth (%), Growth Trend\nAnnual Change calculates difference from previous year (N/A for first appearance, 2 decimals). Cumulative Growth (%) is relative to first year on list (2 decimals). Growth Trend: \"Rapid Growth\" if Annual Change % ≥ 20%, \"Steady Growth\" if ≥ 5%, \"Stable\" if -5% to 5%, \"Moderate Decline\" if -20% to -5%, \"Significant Decline\" if < -20%, \"New Entry\" if no previous year data. Net worth values to 2 decimals.\n\nSubtask 2 - Sheet3_Industry_Concentration: Aggregate by industry sector and analyze market concentration.\nColumns: Year, Industry, Billionaire Count, Total Wealth (USD billions), Average Wealth (USD billions), Market Share (%), Concentration Level\nIndustry standardization: Amazon → E-Commerce, Microsoft → Technology-Software, Tesla/SpaceX → Technology-Automotive/Aerospace, Facebook/Meta → Technology-Social Media, Google/Alphabet → Technology-Internet Services, Oracle → Technology-Enterprise Software, LVMH → Luxury Goods, Berkshire Hathaway → Diversified Holdings, Walmart → Retail, Zara → Fashion Retail, Bloomberg → Financial Services, Telecom → Telecommunications, Reliance → Conglomerate. Market Share (%) = (Industry Total / Year Total) × 100 (2 decimals). Average Wealth = Total / Count (2 decimals). Concentration Level: \"Monopolistic\" if count = 1, \"Highly Concentrated\" if count = 2, \"Concentrated\" if count ≤ 4, \"Diversified\" if count > 4. Sort by Year ascending, then Total Wealth descending within each year. Billionaire Count is integer.\n\nSubtask 3 - Sheet4_Ranking_Stability: Track individual ranking stability and volatility over time.\nColumns: Name, Years on List, First Year, Last Year, Best Rank, Worst Rank, Average Rank, Rank Volatility, Ranking Trend, Stability Rating\nYears on List = count of appearances (integer). Best Rank = minimum rank number (integer). Worst Rank = maximum rank number (integer). Average Rank = mean of all ranks (2 decimals). Rank Volatility = standard deviation of ranks, 0.00 if only one year (2 decimals). Ranking Trend: \"Rising\" if average annual rank change ≤ -1, \"Slightly Rising\" if ≤ -0.2, \"Stable\" if -0.2 to 0.2, \"Slightly Falling\" if ≤ 1, \"Falling\" if > 1 (rank change calculated as (last rank - first rank) / years span). Stability Rating: \"Highly Stable\" if years ≥ 6 and volatility ≤ 1.5, \"Stable\" if years ≥ 5 and volatility ≤ 2.5, \"Moderately Stable\" if years ≥ 4 and volatility ≤ 3.5, \"Volatile\" if years ≥ 3, \"Insufficient Data\" otherwise. Sort by Years on List descending, then by order of first appearance in RawData for ties. First Year, Last Year, Best Rank, Worst Rank are integers.\n\nSubtask 4 - Sheet5_Pivot_Summary: Create pivot summary showing top 3 and aggregate statistics by year.\nColumns: Year, Rank 1 Name, Rank 1 Wealth (USD billions), Rank 2 Name, Rank 2 Wealth (USD billions), Rank 3 Name, Rank 3 Wealth (USD billions), Total Top 10 Wealth (USD billions), Average Top 10 Wealth (USD billions), Median Age, Wealth Gap (Rank 1 vs Rank 10), Top 3 Concentration (%)\nTotal Top 10 Wealth = sum of all 10 billionaires (2 decimals). Average Top 10 Wealth = Total / 10 (2 decimals). Median Age = median of 10 ages (integer). Wealth Gap = Rank 1 wealth - Rank 10 wealth (2 decimals). Top 3 Concentration (%) = (Sum of Top 3 / Total Top 10) × 100 (2 decimals). All wealth values to 2 decimals. Sort by Year ascending.

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