Although I have heard of Microsoft Excel since I was in the first grade, I never completely understood what it did or even ever fully grasp the concept of this software program. This semester I had the chance of not only learning how to use Microsoft Excel through my Computer Science course, but also I had the opportunity to use it for my Statistics class to create charts and perform data analysis. It opened my eyes to how much Microsoft Excel was able to do and how it contributes to the world of Statistics.
The definition of Microsoft Excel is that it is a software program that is produced by Microsoft that allows users to organize, format and calculate data with formulas using a spreadsheet system. It is compatible for both Microsoft Windows and Mac OS. Excel is capable of performing basic calculations, use graphing tools, create pivot tables and create macros. The layout of Excel is a spreadsheet and it is a collection of cells that are arranged into rows and columns in order to organize and manipulate data. Excel is also capable of displaying data as charts, histograms and line graphs.
Excel provides enormous capacity to do quantitative analysis and allows you to do statistical analyses of databases with hundreds of thousands of records to complex estimation tools with user-friendly front ends. Unlike other spreadsheet applications, Excel also provides an intuitive interface that allows you to see what happens to the data as you manipulate them. Excel also permits users to arrange data so as to view various factors from different perspectives. Visual Basic is used for applications in Excel, allowing users to create a variety of complex numerical methods. Programmers are given an option to code directly using the Visual Basic Editor, including Windows for writing code, debugging and code module organization.
Because Excel is so simple and easy to use it is everywhere, especially in the business world that focuses on marketing, business development, sales and finance. Excel is helpful for the people in the business world because it is capable of doing many things such as solve easy arithmetic solutions, has formatting options, availability of online access, create charts for analysis, brings data all in one place and human resource planning. Excel also provides an extensive range of statistical functions that perform calculations from basic mean, median and mode to the more complex statistical distribution and probability tests. Some functions include count and frequency, distribution and tests of probability, finding the largest and smallest values, percentiles, quartiles and rank, averages, deviation and variance, confidence intervals, trend line functions and permutations.
The best part of Excel is that you do not even need a fancy statistics package to perform many statistical functions, as long as your Data Analysis Tool pack is installed. The data analysis tool pack provides a wide range of tools to carry on statistical analysis with a single click. It allows us to create histograms, run regression analysis, provide the regression equation and provide the value of correlation coefficient and residual errors. It greatly helps in the hypothesis tests of various types. The output obtained for such tests is very detailed and provides the complete values, which are important in the correct interpretation of the result of the hypothesis test. Excel also has the exclusive option of charts in which is provides a variety of chart options to display the data and make it more presentable and easy to understand. It provides various options of labeling the chart appropriately, providing legends, all types of statistical chart forms, such as column, bar chart, pie chart, box plots and scatter charts. Excel also has the ability to create tables and present data in rows and columns to make it more systematic and organized. It provides the exact data and figures in a format that is quick to understand and interpret. There are also various options in the tool table. Excel allows us to modify the table format, row patters, border colors, border width and also allows us to merge cells and arrange the data accordingly.
Just like everything else in this world, nothing is perfect. With all these advantages that Microsoft Excel has to offer us, it definitely comes with some disadvantages as well. Some common issues that occur with Excel for statistical analysis include missing values are handled inconsistently and occasionally incorrectly, many analysis can only be done on one column at a time, making it inconvenient to implement repetitive analysis on many columns and the output is poorly organized and there is no record of how an analysis was accomplished.
Besides Excel, there are also other forms of technology used for statistical analysis. There is SPSS, which is also built on interactive operation window just like Excel. The software becomes more flexible allowing users to customize the model based on programming language. However, the syntax of SPSS is fairly poor with a complex command structure and its menu-style interface is a major obstacle of stepwise computation in regression analysis. Compared with Excel and SPSS, SAS has shown its superiority in implementing advanced statistics analysis. First, SAS system is highly structured and strict to data, which makes it very flexible and trustworthy. Second, SAS is very powerful in the area of data management, allowing users to manipulate data in almost any format possible. Third, SAS is specifically robust in analysis of variance (ANOVA), general linear model and its extensions. Finally, SAS could show data analysis procedure step by step in its log window.
Regardless of Microsoft Excel’s disadvantages and other statistical analysis software programs that are out there, there is no doubt that Microsoft Excel is the most popular and commonly used program. It is useful for personal use, office use and for school use as well. Without it, this world would be a mess and hopefully, there will be more improvements and additions to Microsoft Excel in the near future, especially relating to statistical analysis.