Is Excel statistical software?

Table of Contents

The everyday use of Excel is supporting data –storage, sorting, filtration, displaying, and manipulation. However, it does have some basic statistical analysis functionalities, which makes it suitable for simple inferences.
Excel is not designed to perform statistical analyses. Therefore, it comes with certain limitations, especially when dealing with large volumes of data. So it is a poor choice for statistical analysis.

Is Excel statistical software
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Frequently, when you are using excel for statistical analysis, you may encounter problems like:

  • Inconsistencies in handling missing values; sometimes, it mishandles values.
  • It’s tedious to do multiple analyses because you have to reorganize your data in different ways – organization of data differs based on the analysis.
  • You can do multiple analyses in one column simultaneously, making it inconveniencing if you want to do a similar analysis in numerous columns.
  • Poorly organized output and, at times, insufficiently labeled.
  • Doesn’t outline the procedure for data analysis – you only get results.

Excel is ubiquitous, and it can handle specific data analyses. But if you are looking for something to collect, explore, and present large data amounts, a statistical analysis tool can are the best option. 

Most researchers and data scientists find MS Excel to be a convenient tool for data entry. Additionally, it works well when storing, arranging, sorting, and filtering data before the real statistical analysis.

In this write-up, we look at excel and SPSS in detail.

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What is statistical software? 

These are simply computer programs designed for analysis of data — in statistics and econometrics. Essentially, statistical software collects, organizes, analyzes, interprets, and presents data in a way that a non-specialist can understand

There are different types of statistical software around. However, the software can only be helpful if you know how to use it.

Look: 

Powerful statistical software can help you discover patterns and trends necessary for making better decisions for your business. Such software can read data directly from an excel spreadsheet or enter the data into the software. Alternatively, you can use specialized data entry software to capture data.

By now, you should know that reports from analyzed data are vital in running a business, making decisions, and uncovering new trends. Businesses rely on such reports to discover patterns that can generate more revenue and profit. 

What does this mean for you?

Further, data analysis offers predictive analytics, business intelligence, data management, and multivariate analysis.

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Data analysis in statistical software 

No doubt about it – analyzing Data is the scariest part of the research. But with the right statistical package, the process can very smooth and very easy.

Currently, there are many tools available for data analysis, but only a few standouts to be the most used. The best software should stand out for its stability, efficiency, and accuracy.  

Common examples include:

  • SPSS: is an efficient, and perhaps the most widely used statistics software tool within human behavior research. Is it descriptive statistics or parametric and nonparametric analyses, SPSS will sort you out. Besides, it gives the best graphical depiction through the graphical user interface (GUI). Best of all, you can create scripts to automate analysis.
  • R: this software package is suitable for statistical computing and graphics. It is highly flexible as it runs on Windows, macOS, and UNIX platforms.
  • SAS: with this statistical software, you can either use the GUI or create scripts for more advanced analyses. It has a wide application in healthcare, business, and human behavior research. 
  • Minitab: this option offers both basic and advanced tools to analyze data. You can execute commands through GUI and scripts, making it great for novices and those who need to do more complex analyses.

You can also try options like GNU PSPP, Statistical Lab, DataMelt, and Dataplot.

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Can Excel be used for statistical analysis?

Good question. 

Excel offers a range of statistical functions, which you can use to do calculations in an excel worksheet. Additionally, Excel has an analysis tool – the data Analysis Toolpak (an add-in). The tool provides additional statistical analysis tools, which makes the process of data analysis easier. 

Well, it is possible to perform statistical analysis in excel. 

Most people who need to understand data’s statistical concepts & behavior begin with Excel before using more advanced statistical software.

Nevertheless, Excel won’t be helpful if you have a large volume of data. Besides, if you need to model your data, e.g., linear and regression analysis, Excel isn’t the right option. In this case, you might need advanced software like R programming or SPSS.

No wonder excel is an excellent tool for basic statistical analyses. 

Now:

Always ensure you enable the Analysis ToolPak in your Excel. This is how you check:

Click on Excel, then Data. Once there, check if you have the Data Analysis – right corner, at the top. 

If not, then:

Excel, click File, go 

to Options, and finally Add-in. While here, enable Analysis ToolPak.

To do so, locate the 

Excel Add-ins in the manage tab, then GO. It opens a tiny window, where you’ll choose Analysis ToolPak and then enable it.

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What statistical tests can you perform using Excel? 

There are lots of tests, including:

Descriptive Analysis

Use this route to perform the descriptive analysis:

Open Excel, 

click Data, then Data Analysis, and finally, Descriptive statistics

Descriptive statistics is a basic analysis that excels offers. Generally, the analysis gives you data’s basic behavior and pattern

If you need a summary of the Data you have, then this can be the best option. Ideally, you can perform the following:

  • Mean, Median, and Standard error
  • Kurtosis and Skewness
  • Sample Variance
  • Range, Minimum, Sum, Maximum, and Count
  • Median, Mode, and Standard Deviation

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Analysis Of Variance

ANOVA is an analysis method that tests whether data sets have means that significantly differ. For instance, variance analysis can be used to analyze 3 different towns’ traffic to determine which town handles its traffic more efficiently. 

Excel offers three types of ANOVA:

  • Single factor ANOVA – ideal for 3 groups of datasets 
  • ANOVA two factors with replication – suitable for two independent variables with multiple observations in each combination of the variables’ levels. 
  • ANOVA two factors without replication – ideal for two independent variables with one observation in each combination of the variables’ levels. It’s suitable for Repeated Measures ANOVA.

To determine the significance of the relationship, look at the summary table, specifically, the P-value. A value bigger than 0.05 indicates that there is a big difference among the groups under study.

Moving Average

If you have data that is spaced at a series of time intervals, then you can use the moving average. That includes things like weather reports, stock prices, etc.; in this case, it is the technical indicator.

For instance, when you need to predict today’s stock price, you’ll use the past ten days’ prices instead of the last one year’s products because of relevancy.

Regression

Excel also lets you determine relationships among many variables –regression analysis. With excel, you can establish a connection between dependent and independent variables. 

For instance, you can see a relation between an increase in revenue of a particular product, but not due to increase advertisement.

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Rank &Percentile

Excel can also calculate ranking & percentile in a data set. You can apply this rank technique to determine a product that brings higher revenue to your business if you sell multiple products. Basically, excel sort’s data and places it in ranks. 

On the other hand, you can apply percentile to show a class that data belongs to, e.g., top 50% or top 30%.

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Random Number Generator

A random number generator gives more flexibility in the process of generating numbers. Ideally, you have more control over the data you are developing.

Sampling

If you need to create samples from a vast population, you can employ this tool in excel. You have the flexibility of selecting data from a dataset, or you can choose every nth item. 

Correlation      

Excel can calculate a matrix of the correlation coefficient for possible pairs of measurements, in case you have more than two measures within a sample of a population.  

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SPSS vs. Excel

The SPSS vs. Excel debate isn’t about to end, but that terminology remains a considerable concern for people who need an efficient and affordable statistical program. 

Therefore, you must understand the difference between these two statistical analysis tools.

SPSS in brief 

SPSS is the market leader when it comes to statistical packaging tools. It standout for:

  • Massive library of machine learning algorithms
  • Advanced statistical analysis
  • Open source extensibility
  • Seamless deployment into applications
  • Integration with big data 

And the best part?

It comes with much flexibility, scalability, and ease of use. So, individuals, all skill levels can use it.

Ideally, many users use it for batch processing for both interactive batches as well as non-interactive batches. Again, SPSS analyses are both qualitative and quantitative. 

SPSS Inc. incubated SPSS, but in 2009, IBM bought SPSS. 

Graphics is a part of any statistical software. With SPSS, some graphics pack more analytical features. Besides creating a standard graphic in this statistical software, you have an opportunity to highlight data and output the graphics in HTML5 / .mht files.

Keep in mind that SPSS is simply a drag and drop software tool with almost all basic and advanced statistical analysis. So research scholars can quickly adapt the software and easily do the analysis part and quickly attain their results.

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The Role of SPSS

Scientists who need to analyze detailed data can easily use SPSS, thanks to its core programs, which include:

  • Statistics Program: it provides a vast amount of essential statistical functions, including cross-tabulation, bivariate statistics, and frequencies.
  • Modeler Program: this function allows you to build as well as verify predictive models using enhanced statistical procedures.
  • Text Analytics (Surveys Program): suitable for survey admins to uncover insights from replies to a survey’s open-ended questions.
  • Visualization Designer: with this function, you can use your data to create an extensive range of visuals such as density charts with ease.

Beyond these programs, SPSS offers a solution in terms of data management. This is handy for researchers as it allows them to execute case selection, do file reshaping, and generate derived data. 

Additionally, SPSS has a data documentation feature – lets you stock the metadata dictionary. The metadata dictionary is a consolidated repository for information that appertains to Data like meaning, usage, connection to different data, origin, and format.

Other modules include:

  • SPSS Exact Tests module allows users to use small samples but still have a confident feeling about the results.
  • SPSS forecasting module: allows users to predict trends and develop forecasts quickly and with much ease 
  • SPSS Missing Values module: can create a relationship between missing values in your data and other variables. 

The statistical methods that you can leverage in SPSS include:

  • Descriptive statistics – it can include methodologies like cross-tabulation and frequencies
  • Bivariate statistics, which include methodologies like ANOVA, means nonparametric tests and correlation.
  • Numeral-outcome-prediction like linear regression 
  • Prediction to identify groups, which include methodologies like factor and cluster analysis 

Excel in brief 

Excel is popular because it is easy to use, yet providing powerful tools for statistical analysis. Its design allows you to store data in a tabular format – rows and columns.

It has powerful formulas that you can employ to filter and sort data. Besides, it has a very powerful tool – pivot tables, which lets you create new insights via data manipulation. 

Additionally, there are multiple ways to excel in which you can import and export data and integrate data into a workflow.

Did you know that excel is the only statistics software that lets you create a custom function using its programming abilities? 

VBA is Excel’s programming engine. When used rightly, it can automate processes and steps to create a custom function.  

Look, the cardinal purpose of excel is to create data records and manipulate data according to the user’s demand. Thanks to its flexibility, you can use external databases to analyze and generate reports, saving lots of time.  

The latest version of excel comes with powerful graphic tools alongside visualization techniques.

By now should be asking yourself:

How does excel compare to SPSS?

The table below shows a comparison between SPSS and Excel.

Parameter SPSSExcel 
Definition/Meaning Statistical package for social science (SPSS) – statistical software for mathematical study of data It’s a Microsoft product suitable for data entry, manipulation, and storage.
Usage Suitable for statistical computations according to the guidelines of IBM. Ideal for managing and storing data according to procedures established by Microsoft 
 
ObjectiveTo manipulate a given set of data and generate authentic and reliable results through reliable methods.It is used for saving information and enables careful examination of the information.
Tool Batch data processing tool A simple data processing tool
Best for Speed and performance Reduces redundancy in the data and information 
Real-time usageIdeal for more advanced and ultra-fast computers and devices such as super-computers. Suitable handling and maintaining large volumes of data 
Industry Data analysts and data scientist Data analysts/data scientists 
Field It covers a whole superset of data science.A subset of network ability; here, it involves making knowledge of information using different techniques and methods.
Application Large scale industries, business and professional activities Management of large scale but sensitive data in businesses 

Here are the differences between the two:

Differences between SPSS and EXCEL

There are glaring differences between SPSS and Excel. Here are some:

  • SPSS is a tool for computations as it contains subjects such as Data Storages and Data formats. By contrast, excel works based on mathematical concepts like algebra, calculus, statistics, and advanced statistics.
  • SPSS built processes in batches. Besides, it teaches memory management in areas of programming. On the other hand, excel teaches you how to enter, process, and store data. Again, it deals with reducing data redundancy and how to make data more meaningful.
  • SPSS deals with computer systems – storage utilization and memory system efficiency. However, excel deals with extracting specific information from the broad set of data using various Excel formats.
  • SPSS uses probabilistic theories, discrete structures, reasoning, and database. By contract, excel has a more mathematical approach – both in operations and analytics. 
  • SPSS uses statistics algorithms for data manipulation, and it’s always ready for new statistics algorithm updates. On the contrary, excel combines both mathematics and data storage in various formats.

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Disadvantages of SPSS over Excel

Though SPSS is a powerful tool, it comes with some minor limitations. Such limitations include:

  • Low graphic quality: default graphics have low quality. However, SPSS allows for editing to improve the quality, but still lower than the latest versions of excel.  
  • SPSS is expensive compared to excel. The thing is, SPSS releases a newer version almost every year, and each version has noticeable changes. That means you should keep on updating your SPSS to a more recent version each year, which translates to spending money. 
  • It’s difficult or impossible to get Documentation about algorithms: there were books with SPSS algorithms in the past. You could buy such books for your personal use. Currently, you can find such books. Besides, SPSS uses statistical names that are not consistent with many statistic books; thus, users can find books to be less useful. For instance, the “kurtosis” statistic SPSS provides is similar to “excess kurtosis,” according to most books. SPSS refers to semi partial correlation a “part” correlation. Some procedures, like methods of binning scores (histograms), are essentially black boxes.
  • The base package lacks essential procedures like Missing Values in the package. You can use these functions as add-ons, which, of course, you purchase. And they are expensive.  
  • You cannot easily access SPSS support for individual users. So should you happen to run into a problem with the license codes, you are on your own.
  • Provides results in a compact form to make it user friendly. However, this comes with the disadvantage of hiding the internal functionality of what’s happening. For instance, it gives P-values without explaining the procedure.  

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Advantage of Excel over SPSS

Excel stands out as easy to use statistical software. It comes with some advantages of SPSS, which include: 

  • In Excel, formulas calculate everything you need in real-time within the worksheets. Now, this happens before your eyes. On the contrary, in SPSS, all calculations or data manipulation is achieved by a command.
  • Excel gives you more flexibility than SPSS when it comes to presenting charts and tables. Ideally, with excel, you can present your reports as want them to appear.
  • Excel is a cheaper option compared to SPSS. When you buy a PC, it comes with MS excel. Or you can install windows which you pay once. By contrast, you have to install SPSS. Though there is a free version, it is as powerful as the premium option. Currently, IBM SPSS Statistics v26 costs range between $99 to $5,730, while MS excel costs $139.99. 
  • Some essential functions are missing in SPSS. To add them, you have to buy them as add-ons, which add to the total cost.
  • It is easier to learn Excel than SPSS. The point is SPSS releases a newer version every year. These versions come with more advanced features that you may not need, and if you need them, you have to learn.

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Does that make Excel a better statistical software? 

That said, SPSS has a better capability than excel considering its features. But in general, they have a similar feel and look – menus, spreadsheets, and built-in statistical functions.

But when you narrow down to analyzing data, SPSS is at the top, considering that its design is meant for analyzing software. Typically, SPSS offers:  

  • Much faster access to basic functions in its pull-down menu
  • A massive range of graphs and charts 
  • Much quicker access to statistical tests

If you do market research, you benefit immensely from SPSS through data management and organization. 

Conclusion 

Now, is excel statistical software? Ideally, yes. With excel, you can find an extensive range of statistical functions that you can apply to calculate values in worksheets. Precisely, the Analysis Toolpak is a vital add-in, offering additional statistical analysis tools. Besides, you can use VBA to enhance the abilities of excel – serious statistical analysis.

Admittedly, there is far much better statistical software than excel. Actually, such statistical packages are professional as they have all the functions. Besides, they produce analyses that are reproducible, modifiable, and communicated better than Excel and VBA.  

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Luis Gillman
Luis Gillman

Hi, I Am Luis Gillman CA (SA), ACMA
I am a Chartered Accountant (SA) and CIMA (SA) and author of Due Diligence: A strategic and Financial Approach.

The book was published by Lexis Nexis on 2001. In 2010, I wrote the second edition. Much of this website is derived from these two books.

In addition I have published an article entitled the Link Between Due Diligence and Valautions.

Disclaimer: Whilst every effort has been made to ensure that the information published on this website is accurate, the author and owners of this website take no responsibility  for any loss or damage suffered as a result of relience upon the information contained therein.  Furthermore the bulk of the information is derived from information in 2018 and use therefore is at your on risk. In addition you should consult professional advice if required.