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    Data Analysis for Business Decisions: A Laboratory Manual
    Data Analysis for Business Decisions: A Laboratory Manual
    Data Analysis for Business Decisions: A Laboratory Manual
    Ebook251 pages1 hour

    Data Analysis for Business Decisions: A Laboratory Manual

    By Mercury Learning and Information and Andres Fortino

    ()

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    About this ebook

    This manual is for business analysts to enhance their statistical analysis skills, with case studies focusing mainly on Excel. It covers basic descriptive techniques, linear regression, forecasting, t-Test, chi-square, A/B testing, text data analysis, and Big Data management. Companion files include solution spreadsheets, sample files, and data sets.
    The course starts with data shaping and cleaning, installing the Analysis ToolPak, and descriptive statistics. It progresses through histograms, scatter plots, Pareto analysis, correlation, linear and multivariate regression, and forecasting. Advanced topics include inferential statistics, contingency analysis, and A/B testing. The final chapters cover text analytics, big data sets, and data visualization.
    These techniques are crucial for informed business decisions. This book guides users from basic to advanced analysis, blending theory with practical skills. Companion files enhance learning, making this manual essential for mastering statistical analysis in business.

    LanguageEnglish
    PublisherPackt Publishing
    Release dateJul 29, 2024
    ISBN9781836647942
    Data Analysis for Business Decisions: A Laboratory Manual

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      Book preview

      Data Analysis for Business Decisions - Mercury Learning and Information

      Preface

      This laboratory manual was written for business analysts who wish to increase their skills in conducting statistical analysis of data sets to support business decision-making. Most of the exercises use Excel, today’s most common analysis tool. They range from the most basic descriptive statistical techniques to more advanced techniques, such as multivariate linear regression and forecasting.

      Advanced exercises cover inferential statistics for continuous variables (t-Test) and categorical variables (Chi-square), as well as A/B testing. The manual ends with techniques to deal with the analysis of text data (text data mining) and tools to manage the analysis of large data sets (Big Data) using Excel. A set of cases is provided to assist the analyst to improving their data visualization skills.

      On the Companion Files

      The exercises require access to the data sets used in analyzing the cases. They may be accessed on companion disc. with this book or for downloading by writing to the publisher at [email protected]. A file folder Lab Data has all the files referenced in the exercises. A zip file Lab Data.zip, found in the same repository, can be downloaded to make data available on a local drive. The solution folders within each exercise folder contain some illustrative charts and tables as well as solution spreadsheets. All of the figures (including those in full color) are on the companion files for enlargement and easy-viewing. The analysis techniques presented in each chapter have short companion videos you may use to understand the ideas further. The video lessons may be found on the companion disc. If you wish to stream the video rather than download it, there is a document on the disc with links to all the companion videos to be found on a streaming service. The companion files are also available for download from the publisher by writing to [email protected].

      Acknowledgments

      Practical books such as this, that are full of cases, are created by many years of trying them out on students until you get them right. It’s a matter of keep changing the exercises and trying things out until they seem to work, and in the end, they help people learn. I wish to thank the legion of students who were very patient with me and helped me perfect these cases. Both my graduate students at the NYU School of Professional Studies and the many American Management Association professionals who attended my AMA seminars deserve my gratitude.

      I also wish to thank my colleague, Nicole Morgenstern, for taking a chance with me at AMA. Thank you, Nicole, for sponsoring this work and running interference for me. My thanks to my graduate student, Karen pey-rong Hong, who did a superb job updating all the exercises to the latest version of Excel. The entire team of editors and artists at Mercury Learning was terrific and has my gratitude. A special thanks to Jim Walsh, my editor, who kept asking for more and more and helped shape an excellent book. In the end, it paid off, Jim. Finally, to my loving and patient wife, Kathleen, who not only labored over the manuscript by copyediting, but provided much-needed advice. You were always right, dear.

      Dr. Andres Fortino

      June 2020

      CHAPTER 1

      SHAPING AND CLEANING DATA

      In the first set of exercises, we will look at the importance of shaping and cleaning data files. The initial image in this chapter shows the Data Cleansing Cycle with many activities, starting with importing the data; merging the data sets; standardizing and normalizing data; rebuilding missing data; de-duplicating; and last, verifying and enriching the data set. The object is to produce a data set in Excel in what is called a flat-file format. When expressed in that format, the first row of the table must contain all the variable names (with none missing); every row is of the same nature, and there are no empty rows or columns. All other rows and columns outside of the table area should be clear of data. Once in that format, the table is ready for analysis, and we can safely apply many of the Excel analytic tools.

      The source of the data table varies; sometimes we extract it from a DBMS using the SQL language using queries. Other times we may obtain a comma-separated values file (with a .CSV extension), or a formatted text file (with a .TXT extension), or we may have scraped it from an HMTL formatted Web page. In Analysis Case 1.1, we explore loading and shaping data files from several sources. We study how long it takes to load data files of different sizes, including some huge files that tax the limit of Excel. They not only take a long time to load, but they are unwieldy to analyze.

      Once we practice loading data in various formats, we explore cleaning it in Analysis Case 1.2. We practice using a small data file that contains several errors that need to be corrected. You are directed to the original data to find the original values. The exercise allows you to utilize many tools in Excel that make the data cleaning process efficient.

      The whole process of scraping, uploading, cleaning, annotating, and shaping the data file is referred to as data wrangling. Many studies have shown that this process is not only tedious but can take up to 80% of the overall time needed to perform the analysis. But it is critical for success in the analysis. The more skilled you are in the use of cleaning and shaping tools, and the smarter you are in their use, the sooner you will start the analysis, and the less time you will need to find answers.

      Analysis Case 1.1 – Shaping the Data File

      1. Using the Lab Data set provided, open the Analysis Case 1.1 folder in it, and find the file ORDERS.csv (1.8 MB file with 8,400 records). (The data set was made available courtesy of Tableau, Inc.)

      2. Open ORDERS.csv using Excel.

      3. Excel will automatically recognize the .CSV format and open the file with no further work on your part.

      4. ORDERS is a comma-separated values file.

      5. Save the file as ORDERS.xlsx in the Analysis Case 1 folder.

      6. We will explore the difficulties with scraping, opening, and working with large data files in Excel. Consider the following four data files found in the Analysis Case 1.1 folder (Table 1.1). Each file is progressively larger and more difficult to open in Excel than the

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