# Introduction to Real Statistics using Excel

Statistics is a field of study that has two principal objectives:

• Describing data (Descriptive Statistics)
• Making inferences based on experimentally observed data (Inferential Statistics)

Descriptive Statistics involves calculating the mean, median, variance, standard deviation and other properties of the data, and presenting this information in ways that make the data more meaningful, such as histograms, boxplots, QQ plots, etc.

Inferential Statistics involves analyzing data and inferring characteristics of a general population based on the same properties of a sample taken from the population. This is what gives the field of statistics its power since with a relatively small amount of data you are able to make significant assertions, even though such inferences are not 100% certain, but probabilistic in nature.

This website provides a tutorial on statistics plus access to tools that let you calculate various statistical tests from within Excel, including the following:

• Tutorial on basic statistical tests and analyses
• Step by step procedures for carrying out these tests and analyses using Excel
• Numerous examples
• Some of the theory behind these analyses (primarily in the Appendix)

There are a number of commonly used, powerful tools for carrying out statistical analyses. The most popular of these are SPSS, SAS and R. We have chosen instead to use Excel as our analysis tool, even though it offers far fewer built-in statistical tools. The reasons for choosing Excel are as follows:

• It is widely available and so many more people know how to use it
• It is not necessary to incur the cost of yet another tool
• It is not necessary to learn new methods of manipulating data and drawing graphs
• It already contains some basic statistics functions and data analysis tools
• It is much easier to see what is going on since unlike the more popular statistical analysis tools very little is hidden from the user
• It provides the user with a lot of control and flexibility

This makes Excel an ideal tool for learning statistical concepts, although often it is easier to use the standard statistical tools for carrying out more advanced statistical analyses. To address Excel’s shortcomings, therefore, we have created the Real Statistics Resource Pack which contains various supplemental tools that enable you to carry out a wide range of advanced statistical analyses without leaving the Excel environment. The Resource Pack can be downloaded for free by clicking on Download.

While everything in this website can be done with standard Excel, the tools in the Resource Pack make it easier to perform statistical analyses. The website focuses on Excel 2016, 2013, 2010 and 2007, although much of what is described will work fine with previous versions of Excel.

No advanced mathematics is necessary to use this website. In particular calculus is not required. For those readers who are familiar with calculus and are interested in the derivation of some of the statistical concepts described in the website, from time to time you will have the opportunity to click on a link to access more advanced information.

Knowledge of Excel is also not required. Excel Environment contains an introduction to Excel and Excel Capabilities provides more detailed information. There are also many books and online resources that provide a wealth of information about how to use Excel.

In the past few months we have added many new statistical capabilities. More new features are being all the time, so stayed tuned.

### 13 Responses to Introduction to Real Statistics using Excel

1. Doug Marker says:

I would like to ask when the Real Multivariate Statistical Analysis using Excel will be added.

Thanks

Hi Doug,
My current plan is to have the multivariate support up and running by the end of the summer. Please let me know which capabilities are most interesting to you.
Charles

2. Frank Finkenberg says:

I second the request for multivariate support. I’d like to do Excel-based Monte Carlo simulations of real stock returns, real bond returns, and inflation, based on published parameters for the arithmetic mean, geometric mean, std deviation, and cross-correlation coefficients of each variable.

Thanks,
Frank

• Charles says:

Thanks Frank for your input. I am compiling input now for what should be in the releases after the multivariate release later this month. I will certainly consider your suggestion and will likely come back to you for more input about your request.
Charles

3. Colin says:

Thank you very much sir! I am a fan of Excel and I am studying statistics. This website really help!

Colin

4. Dave says:

What a fantastic resource. I’m studying business, and this site will really help me get through it!

5. yakoubi med says:

thank you for this usefuls Web Site and pack

6. Fernando says:

Hi Charles,

I would like say “Thanks” because Real stats is really good! I had used in windows in office 2015, but now I have a macbook + office 2016. I followed the steps that you told. The add-in is there, but i can’t find the icon in excel 2016 on macbook as I used had in excel 2015 in windows laptop. So … i can’t do the Statistical analysis.

Thanks.

• Charles says:

Fernando,
Just press Command-m.
Charles

7. Maria Manuela Perez says:

Hello Doug, my name is Manuela, i am a radiologist doing a pediatric fellowship in Toronto and fall into your site due to pure desperation… !! As part of my training I am expected to produce decent original research and the statistician support is very limited. As a lot of doctors my training in this matters is poor… which has led me to lose a looot of time trying to understand! I have discovered Excel but this page is going to help me a lot, thanks for your time in putting it together!

8. Remy says:

Thanks for this. Simple and effective!

9. Miles says:

Hello Charles,
I’m Excel illiterate and even worse at statistics but part of my job entails correlating between similar groups of numbers in a hospital lab setting. I’ve stumbled around enough to be able to figure out slope, intercept and R2. plus some graphs as our regulatory groups love seeing graphs. What I would love to learn though is what do I really need to prove correlation. I have one ongoing problem that I cannot for the life of me figure how to solve. we have to correlate between automated white blood cell differentials and manual differentials. There are 5 types of WBC’s and they need to be correlated separately but the problem is the instrument counts 10,000 or more cells while a normal manual differential is only 100 cells but I have the technologists count 200 cells because more is better right. I realize the more cells that are counted the better the correlation might be however its very time consuming.. I found something in my search called Rumke tables which helps but I don’t know how to put this in use in excel. The instrument reports the differential as percentages and a print out is available. I make/stain a slide of the patients blood and the techs to look through the microscope and count each type of cell. once complete I evaluate each techs differential in comparison to the instrument automated differential. I just found your site today and haven’t made it very far through the reading but I’ve impressed with it and I wanted to say thanks for this undertaking. I will have more questions in the near future I’m about 110% sure.

• Charles says:

Miles,
I am not familiar with Rumke tables, although I believe that they are related to the binomial distribution in some way. I am not familiar with the concepts in your comment, but when I enter Rumke Table in google, I see that there are quite a few webpages on the topic that you are referencing.
Sorry that I can’t give you any more help on this topic.
Charles