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Noelle Hafsa June 11, 2021 Spreadsheet

About four months after my husband asked for a divorce I made an appointment to meet a divorce attorney recommended by my therapist. She was 60 years old, short, smart and focused. I thought she was great. She charged $350.00 per hour. It was good that she was focused. I was at the time furious at my husband for his recent behaviors and I told her that I wanted to file for divorce immediately. Could she explain the process to me?

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Lester P. Goodbinder had suffered another agonizing week in Pittsburgh. The semi-annual audit he conducted at the Bourgeois Ball Bearing Factory stretched into five 14-hour days examining electronic spreadsheets on an archaic computer system installed in the early ‘80s. The equipment churned so abysmally he cleverly joked to himself it was powered by lazy hamsters on treadmills. Not only that, the accounting software loaded on the system was an early version of ”Abacus,” and only slightly faster than a key-punch adding machine but considerably slower than a hand-held calculator.

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Here‘s a very simple budget set up. Keep a simple income spreadsheet. List all the sources by name in column A. List how much each brings in in column B. And then, any notes you have for the income (like if it is temporary) in column C. You don‘t need to get very detailed with the income, because it only needs to be accounted for so that we can budget for it‘s use. And, the incomes use is in our expenses spreadsheet. This spreadsheet will be much more complex than the income one. You‘ll need a field for income that you carry over from the income sheet. You‘ll also need a field for a total expenses budgeted for. A third field will give us the budget surplus. We get that by subtracting the budgeted amounts from the income amount. A final field will subtract the actual amount spent from the income, and will serve to tell us where we stand in our budget. If you like, you can add another field that subtracts the actual amount spent from the amount budgeted.

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When Microsoft Excel is used to manipulate, store and analyse data it can become extremely difficult to manage, let alone efficiently work to produce any meaningful insights. This is because with data sets large and small, the data must be meaningful, logical, structured, internally consistent and clean. This holds true regardless of whether the data has been imported into excel from another system or manually entered. In this computing age, most people know that for any data set to be useable it must first be relatively structured and clean. A spreadsheet and its table layout naturally encourages data to be somewhat structured, however ensuring data is clean is also difficult.

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Spreadsheets such as Microsoft Excel are well suited to tasks involving the manipulation of small amounts of related data. Working out a budget, producing visual reports, organizing lists and calculations that involve many variables are all tasks well suited to a spreadsheet. There are some data related tasks however that spreadsheets such as Microsoft Excel are not suited for. Tasks involving the processing and combination of large sets of data for example are generally not well suited to spreadsheets. There is another technology with a long history and theoretical background that specializes in these sorts of tasks. That technology is relational databases. The most common way people insert data into and extract data from relational databases is via the language of Structured Query Language.

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Microsoft Excel is a phenomenally powerful calculator. You can create spreadsheets with 10,000 lines of data and calculate subtotals instantly. Indeed, if you change your data, any totals will get automatically updated. Arguably that‘s not too impressive. If we have quarterly revenues of $1m, and we secure another $20k, we can update our subtotal without summing revenues from scratch. So it‘s more impressive that Excel can do the same thing with statistical functions. If you‘ve ever plotted a chart on Excel, you may be aware that you can add a best fit line. These best fit lines are calculated using a method known as regression. Basically, you have to calculate the distance of every single point from the line, and minimise the sum. The maths is a little more sophisticated but the key point is that, every time you change the data, you need to perform the analysis all over again.

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