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Marquise Adèle June 2, 2021 Spreadsheet

As a set of general rules data is most useful when things like text fields hold only names as well as meaningful and validated codes, categories and classifications. Text notes and other free form text should be isolated to a dedicated notes field and thus separated from other numeric data. Numeric fields should hold only numeric values (numbers, dates, %‘s and in the correct quantum or magnitude with no text prefixes, suffixes, spaces, text elements or text notes present. You must also be careful that numeric data is not stored as text and it should be internally consistent in terms of the correct format so that it can be used in calculations or for comparison and queries. Finally, addresses should be separated out into multiple fields such as street address, town /suburb, state / province, postal code and country to allow for geographic analysis and mail outs if required. Fixing up a data set to meet these criteria is called data scrubbing, cleansing or massaging. This data cleansing process can be very time consuming even for an experienced Microsoft Excel user, database engineer, business analyst or computer programmer.

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One of the topics I cover on my Advanced Excel courses is hardly ‘advanced‘ at all, but it is a very useful and popular technique with my students. It makes use of the OLE capability to create invoices by embedding Excel data. First you need to create an Excel spreadsheet and format it in an appropriate manner, keeping in mind that this will form the basic structure of your invoice and will eventually be seen by your clients. You don‘t include any Company contact details or logos in the spreadsheet though as these will be incorporated into the Word document. The next step is to lay out the invoice itself in a Word document, based upon your normal Company letterhead. Leave the main body of the document empty as this is where the Excel spreadsheet will be embedded. All you need in this master Word document is your usual Company branding and contact information.

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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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In a well-designed spreadsheet, any output can be calculated from the raw data. However, that‘s not always enough. Sometimes the output is fixed and the raw data is variable. Let‘s say you run an investment company and want to offer your clients a fixed return. An Excel expert could create a very complex model to calculate the likely return on investments over a fixed period. You could then calculate the internal rate of return being offered to clients. The problem is that you‘re not interested in the return offered to clients; that is, after all, fixed. Instead you‘re concerned with how much money you expect to draw from the investment fund, whilst still offering your investors a satisfactory return. If you have $1 and owe investors a quarter, you can calculate your profits using a simple formula.

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He grossed $2,000 a week for his bosses, and earned slightly less than $500 for himself. Still, the wages kept him in seeds, bowling shoes, stick pins, and a Platinum Buddy Holly Fan Club Membership. Lester‘s favorite word was ”crapola,” and he applied it to the ball bearing factory‘s antiquated data processing system in coats as thick as the olive drab membrane clinging to the smudgy glass before him. ”You piteous piece of crapola!” he‘d hiss at the computer when error messages flashed across its screen or its ancient system locked under the demand of crunching numbers to the tenth decimal point. ”Some day I‘ll throw your sorry ass into one of those melting pots out there!”

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He was an ex-divorce attorney who had seen firsthand what a messy thing divorce was when lawyers were involved. He developed a program where a couple would meet together, with him present, and work through the divorce piece by piece. Property, finances, kids, pensions. It was a great system. And he was cheap! Relatively speaking. It took about 10 months including some stops and starts (”what do you mean you want some of my inheritance? if you want my inheritance then you STAY married to me”) but in the end we were able to come to terms with each other in a reasonable and fair way.

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