For my sins, I have done more than my fair share of analysis in Excel. I am quite capable of building and maintaining 130Mb spreadsheets (I had a dozen of them for one client). Excel is pretty much installed everywhere, so it is sometimes the only way to get started getting commercial value of the data in the organisation. But I don’t like it and let’s have a look at one reason why. In order not to always pick on Microsoft, we use another application, but you get the same results with Excel.
Y  X1  X2  X3  X4 

5.88  1  1  1  1 
2.56  6  1  1  1 
11.11  1  1  1  1 
0.79  6  1  1  1 
0.00  6  1  1  1 
0.00  0  1  1  1 
15.6  8  1  1  1 
3.7  4  1  1  1 
8.49  3  1  1  1 
51.2  6  1  1  1 
14.2  7  1  1  1 
7.14  5  1  1  1 
4.2  7  1  1  1 
6.15  4  1  1  1 
10.46  6  1  1  1 
0.00  8  1  1  1 
10.42  2  1  1  1 
17.36  5  1  1  1 
13.41  8  1  1  1 
41.67  0  1  1  1 
2.78  0  1  1  1 
2.98  8  1  1  1 
9.62  7  1  1  1 
0.00  0  1  1  1 
4.65  5  1  0  2 
3.13  3  1  0  2 
24.58  6  1  0  2 
0.00  1  1  0  2 
5.56  4  1  0  2 
9.26  3  1  0  2 
0.00  0  1  0  2 
0.00  0  1  0  2 
3.13  1  1  0  2 
0.00  0  1  0  2 
7.56  5  0  1  3 
9.93  6  0  1  3 
0.00  8  0  1  3 
16.67  6  0  1  3 
16.89  7  0  1  3 
13.71  6  0  1  3 
6.35  5  0  1  3 
2.5  3  0  1  3 
2.47  7  0  1  3 
21.74  3  0  1  3 
23.6  8  0  0  4 
11.11  8  0  0  4 
0.00  7  0  0  4 
3.57  8  0  0  4 
2.9  5  0  0  4 
2.94  3  0  0  4 
2.42  8  0  0  4 
18.75  4  0  0  4 
0.00  5  0  0  4 
2.27  3  0  0  4 
However, the predictors are (accidentally) collinear so no meaningful fit is possible, unless one of them are dropped. We see that very easily if we try to do the analysis using the R statistical computing and analysis platform:
> d < read.delim("clipboard") # Read DATA range from clipboard > summary(lm(Y ~ ., data = d)) Call: lm(formula = Y ~ ., data = d) Residuals: Min 1Q Median 3Q Max 11.222 5.821 2.546 3.171 40.750 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>t) (Intercept) 4.1945 3.9749 1.055 0.296 X1 0.3862 0.5652 0.683 0.497 X2 0.2308 3.1590 0.073 0.942 X3 3.7072 2.9922 1.239 0.221 X4 NA NA NA NA Residual standard error: 10.14 on 50 degrees of freedom Multiple Rsquared: 0.04767, Adjusted Rsquared: 0.009466 Fstatistic: 0.8343 on 3 and 50 DF, pvalue: 0.4814
We have highlighted the message that R has automatically dropped one of the predictors.
Everybody likes to pick on Excel, so let us load the data into version 3.3.2 of LibreOffice, the free Open Source personal productivity suite, instead. It faithfully implements many of the worst features of Excel. You can grab a copy of the spreadsheet GSspreadsheeterror.ods yourself and see the results. The relevant function in both Excel and LibreOffice for linear regression is LINEST and applying it to the data set give us:
Of the 16 values returned by the function, fully 12 of them are incorrect (highlighted in red), and the '#VALUE!' entries are the only thing that suggests we may have a problem. (The '#N/A' values are a feature of the function and not a problem.) Excluding the X4 values from the function call gives meaningful (and correct) results:
There is so much wrong with doing even this trivial analysis in a spreadsheet that it is hard to know where to start. Some of the problems:
 Garbage results instead of errors
 Instead of giving meaningful errors or warnings, the spreadsheets simply produce garbage results. This is nearly impossible to debug.
 No help on how to correct the problem
 In the erroneous results of the first figure, there is no clue, no hint, no help to figure out how to correct the problem. You could argue about R correcting the issue ”automagically”, but at least it finds a solution to the problem and tells you about it.
 Error prone output formats
 I put in the row and column headings because otherwise it is just too hard to read the data. Where does the function stuff the F statistics again?
And don’t get me started on version control and documentation. Don’t even mention that the maths in Excel are wrong. Remember: Friends do not let friends do data analysis in spreadsheets.
Jump to comments.
You may also like these posts:

Excel Tip: Array boolean operator
I learn something new every day. Thinking I knew pretty much everythging there is to know about Microsofts Excel spreadsheet application, I was surprised to see that you could turn any array into a boolean array depending on a condition by simply writing ( array = value ) , as in these examples: (A1:A10=foo) SUMPRODUCT((B2:B6=B10)*1, C2:C6) This works in Gnumeric but not in OpenOffice 1.4. More notes and examples below.

R tips: Installing Rmpi on Fedora Linux
Somebody on the Rhelp mailing list asked how to get Rmpi working on his Fedora Linux machine so he could do highperformance computing on a cluster of machines (or a single multicore machine) using the R statistical computing and analysis platform . Since it is unusually painful to get working, I might as well copy the instructions here.

R code for Chapter 1 of NonLife Insurance Pricing with GLM
Insurance pricing is backwards and primitive, harking back to an era before computers. One standard (and good) textbook on the topic is NonLife Insurance Pricing with Generalized Linear Models by Esbjorn Ohlsson and Born Johansson. We have been doing some work in this area recently. Needing a robust internal training course and documented methodology, we have been working our way through the book again and converting the examples and exercises to R , the statistical computing and analysis platform. This is part of a series of posts containing elements of the R code.

R code for Chapter 2 of NonLife Insurance Pricing with GLM
We continue working our way through the examples, case studies, and exercises of what is affectionately known here as “the two bears book” (Swedish björn = bear) and more formally as NonLife Insurance Pricing with Generalized Linear Models by Esbjörn Ohl…

We are seeing the same thing, if a little less and a little delayed. Does it have to be like this? I dont think it is just the tech industry but any new and hot growth area. Fred Wilson writes in Bubble 2.0 that we are heading for a new bubble, similar to…
Rbloggers.com offers daily email updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...