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https://www.wiley.com/Excel+Data+Analysis+For+Dummies%2C+5th+Edition-p-9781119844426
Paul McFedries
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Published:
February 15, 2022

Excel Data Analysis For Dummies

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Overview

Harness the power of Excel to discover what your numbers are hiding

Excel Data Analysis For Dummies, 2nd Edition is the ultimate guide to getting the most out of your data. Veteran Dummies author Stephen L. Nelson guides you through the basic and not-so-basic features of Excel to help you discover the gems hidden in your rough data. From input, to analysis, to visualization, the book walks you through the steps that lead to superior data analysis.

Excel is the number-one spreadsheet application, with ever-expanding capabilities. If you're only using it to balance the books, you're missing out on a host of functions that can benefit your business or personal finances by uncovering trends and other important information hidden within the numbers. Excel Data Analysis For Dummies, 2nd Edition eliminates the need for advanced statistics or analysis courses by allowing you to harness the full power of Excel to do the heavy lifting for you. This 2nd Edition is fully updated to include information about Excel's latest features, making it a your go-to Excel guide for data analysis. Topics include:

  • Working with external databases
  • PivotTables and PivotCharts
  • Using Excel for statistical and financial functions
  • Solver, Small Business Finance Manager, and more

The book also includes a guide to chart types and formatting, and advice on effective visual data presentation. You already have the data, so you might as well get something great out of it. Excel Data Analysis For Dummies, 2nd Edition is the key to discovering what your numbers are hiding.

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About The Author

Stephen L. Nelson is an author and CPA who provides accounting, business advisory, tax planning, and tax preparation services to small businesses. He is the author of more than 100 books, including QuickBooks For Dummies and Quicken For Dummies.

excel data analysis for dummies

CHEAT SHEET

Data analysis, by definition, requires some data to analyze. However, after you’ve imported or entered that data and cleaned it up as best you can. what’s your next move? Ah, that’s where the “analysis” part raises its hand in the air and says, “Pick me, pick me!”Excel is bursting at its digital seams with tools for analyzing data, but there are some that you’ll turn to most often.
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All kinds of people use Excel, including scientists, engineers, mathematicians, statisticians, and pollsters. But if you could somehow survey all the world’s Excel users, the typical user would probably have something to do with the financial industry. Whether they’re accountants or adjusters, bankers or borrowers, or money managers or money lenders, financial types rely on Excel every day to analyze budgets, loans, investments, and other monetary minutiae.
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Excel comes with so many powerful data-analysis tools and features that you might be wondering why you need to learn yet another: the PivotTable. The short answer is that the PivotTable is a useful weapon to add to your data-analysis arsenal. The long answer is that PivotTables are worth learning because they come with not just one or two but a long list of benefits.
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Data analysis, by definition, requires some data to analyze. However, after you’ve imported or entered that data and cleaned it up as best you can. what’s your next move? Ah, that’s where the “analysis” part raises its hand in the air and says, “Pick me, pick me!”Excel is bursting at its digital seams with tools for analyzing data, but there are some that you’ll turn to most often.
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You set up your Excel Solver model by using the Solver Parameters dialog box. You use the Set Objective box to specify the objective cell, and you use the To group to tell Excel Solver what you want from the objective cell: the maximum possible value; the minimum possible value; or a specific value. Finally, you use the By Changing Variable Cells box to specify the cells that Solver can use to plug in values to optimize the result.
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Excel spreadsheet tools such as Goal Seek that change a single variable are useful, but unfortunately most problems in business are not so easy. You’ll usually face formulas with at least two and sometimes dozens of variables. Often, a problem will have more than one solution, and your challenge will be using Excel to find the optimal solution (that is, the one that maximizes profit, or minimizes costs, or matches other criteria).
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To help you analyze data that’s stored in a table or range, you can turn to Excel’s powerful database functions, which enable you to apply calculations such as sum, average, and standard deviation.The database functions all use the same three arguments: database: The range of cells that make up the table you want to work with.
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When it’s time to get down to analyzing your data, a good place to start is with some basic statistics, such as counting items, calculating sums and averages, finding the largest and smallest values, working out the standard deviation, and so on. These measures fall under the general rubric of descriptive statistics, and Excel offers a fistful of functions that help you get the job done.
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To make an Excel PivotTable with a large number of row or column items easier to work with, you can group the items. For example, you can group months into quarters, thus reducing the number of items from twelve to four. Similarly, a report that lists dozens of countries can group those countries by continent, thus reducing the number of items to four or five, depending on where the countries are located.
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A comparison expression — also known as a logical expression or a Boolean expression — is an expression in which you compare the items in a range or table column with a value you specify. In Excel, you use comparison expressions to create advanced filters for a table, as well as in functions that require criteria, such as COUNTIF, SUMIF, and AVERAGEIF.
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The Data Analysis command provides a tool for calculating moving and exponentially smoothed averages in Excel. Suppose, for sake of illustration, that you’ve collected daily temperature information. You want to calculate the three-day moving average — the average of the last three days — as part of some simple weather forecasting.
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Perhaps the most common data analysis tool that you'll use in Excel is the one for calculating descriptive statistics. To see how this works, take a look at this worksheet. It summarizes sales data for a book publisher.In column A, the worksheet shows the suggested retail price (SRP). In column B, the worksheet shows the units sold of each book through one popular bookselling outlet.
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Excel provides a robust toolset for illustrating trends. You can do this by plotting trendlines in your Excel charts to offer a visual of your data. Here, you discover how to plot logarithmic trendlines, power trend lines, and polynomial trend lines in Excel. Plotting a logarithmic trend line in Excel A logarithmic trend is one in which the data rises or falls very quickly at the beginning but then slows down and levels off over time.
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In a general sense, Excel PivotTables take a large amount of information and condense that data into a report that tells you something useful or interesting. For example, take a look at the following table. Some great data, but how do you make sense of it? This table contains well over 100 records, each of which is an order from a sales promotion.
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When you add a second field to the row or column area, Excel displays subtotals for the items in the outer field. Having these outer field subtotals available is a useful component of Excel data analysis because it shows you not only how the data breaks down according to the items in the second (inner) field, but also the total of those items for each item in the first (outer) field.
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