Is Microsoft Excel an Adequate Statistics Package?
It depends on what you want to do, but for many tasks, the answer is ‘No’.

Excel is available to many people as part of Microsoft Office. It contains some statistical functions in its basic installation. It also comes with statistical routines in the Data Analysis Toolpak, an add-in found separately on the Office CD. You must install the Toolpak from the CD in order to get these routines on the Tools menu. Once installed, these routines are at the bottom of the Tools menu, in the "Data Analysis" command. People use Excel as their everyday statistics software because they have already purchased it. Excel’s limitations, and occasionally its errors, make this a problem. Below are some of the concerns with using Excel for statistics that are recorded in journals, on the web, and from personal experience.

Limitations of Excel

1. Many statistical methods are not available in Excel.
Excel's biggest problem. Commonly-used statistics and methods NOT available within Excel include:
* Boxplots
* p-values for the correlation coefficient
* Spearman’s and Kendall’s rank correlation coefficients
* 2-way ANOVA with unequal sample sizes (unbalanced data)
* Multiple comparison tests (post-hoc tests following ANOVA)
* p-values for two-way ANOVA
* Levene’s test for equal variance
* Nonparametric tests, including rank-sum and Kruskal-Wallis
* Probability plots
* Scatterplot arrays or brushing
* Principal components or other multivariate methods
* GLM (generalized linear models)
* Survival analysis methods
* Regression diagnostics, such as Mallow’s Cp and PRESS ( it does compute adjusted r-squared)
* Durbin-Watson test for serial correlation
* LOESS smooths

Excel's lack of functionality makes it difficult to use for more than computing summary statistics and simple univariate regression. Third-party add-ins to Excel attempt to compensate for these limitations, adding new functionality to the program (see "A Partial Solution", below).

2. Several Excel procedures are misleading.
Probability plots are a standard way of judging the adequacy of the normality assumption in regression. In statistics packages, residuals from the regression are easily, or in some cases automatically, plotted on a normal probability plot. Excel’s regression routine provides a Normal Probability Plot option. However, it produces a probability plot of the Y variable, not of the residuals, as would be expected.

Excel’s CONFIDENCE function computes z intervals using 1.96 for a 95% interval. This is valid only if the population variance is known, which is never true for experimental data. Confidence intervals computed using this function on sample data will be too small. A t-interval should be used instead.

Excel is inconsistent in the type of P-values it returns. For most functions of probabilities, Excel acts like a lookup table in a textbook, and returns one-sided p-values. But in the TINV function, Excel returns a 2-sided p-value. Look carefully at the documentation of any Excel function you use, to be certain you are getting what you want.

Tables of standard distributions such as the normal and t distributions return p-values for tests, or are used to confidence intervals. With Excel, the user must be careful about what is being returned. To compute a 95% t confidence interval around the mean, for example, the standard method is to look up the t-statistic in a textbook by entering the table at a value of alpha/2, or 0.025. This t-statistic is multiplied by the standard error to produce the length of the t-interval on each side of the mean. Half of the error (alpha/2) falls on each side of the mean. In Excel the TINV function is entered using the value of alpha, not alpha/2, to return the same number.

For a one-sided t interval at alpha=0.05, standard practice would be to look up the t-statistic in a textbook for alpha=0.05. In Excel, the TINV function must be called using a value of 2*alpha, or 0.10, to get the value for alpha=0.05. This nonstandard entry point has led several reviewers to state that Excel’s distribution functions are incorrect. If not incorrect, they are certainly nonstandard. Make sure you read the help menu descriptions carefully to know what each function produces.

3. Distributions are not computed with precision.
NEW In reference (1), the authors show that all problems found in Excel 97 are still there in Excel 2000 and XP. They say that "Microsoft attempted to fix errors in the standard normal random number generator and the inverse normal function, and in the former case actually made the problem worse." From this, you can assume that the problems listed below are still there in the current versions of the software.

Statistical distributions used by Excel do not agree with better algorithms for those distributions at the third digit and beyond. So they are approximately correct, but not as exact as would be desired by an exacting statistician. This may not be harmful for hypothesis tests unless the third digit is of concern (a p-value of 0.056 versus 0.057). It is of most concern when constructing intervals (multiplying a std dev of 35 times 1.96 give 68.6; times 1.97 gives 69.0) As summarized in reference 2:
"…the statistical distributions of Excel already have been assessed by Knusel (1998), to which we refer the interested reader. He found numerous defects in the various algorithms used to compute several distributions, including the Normal, Chi-square, F and t, and summarized his results concisely: So one has to warn statisticians against using Excel functions for scientific purposes. The performance of Excel in this area can be judged unsatisfactory."

4. Routines for handling missing data were incorrect.
This was the largest error in Excel, but a 'band-aid' has been added in Office 2000. In earlier versions of Excel, computations and tests were flat out wrong when some of the data cells contained missing values, even for simple summary statistics. See (3) , (5), and page 4 of (6). Error messages are now displayed in Excel 2000 when there are missing values, and no result is given. Although this is still inferior to computing correct results it is somewhat of an improvement.

In reference to pre-2000,
"Excel does not calculate the paired t-test correctly when some observations have one of the measurements but not the other." E. Goldwater, ref. (5)

5. Regression routines are incorrect for multicollinear data.
This affects multiple regression. A good statistics package will report errors due to correlations among the X variables. The Variance Inflation Factor (VIF) is one measure of collinearity. Excel does not compute collinearity measures, does not warn the user when collinearity is present, and reports parameter estimates that may be nonsensical. See (6) for an example on data from an experiment. Are multicollinear data of concern in ‘practical’ problems? I think so -- I find many examples of collinearity in environmental data sets.

Excel also requires the X variables to be in contiguous columns in order to input them to the procedure. This can be done with cut and paste, but is certainly annoying if many multiple regression models are to be built.

6. Ranks of tied data are computed incorrectly.
When ranking data, standard practice is to assign tied ranks to tied observations. The value of these ranks should equal the median of the ranks that the observations would have had, if they had not been tied. For example, three observations tied at a value of 14 would have had the ranks of 7, 8 and 9 had they not been tied. Each of the three values should be assigned the rank of 8, the median of 7, 8 and 9.

Excel assigns the lowest of the three ranks to all three observations, giving each a rank of 7. This would result in problems if Excel computed rank-based tests. Perhaps it is fortunate none are available.

7. Many of Excel's charts violate standards of good graphics.
Use of perspective and glitz (donut charts?) violate basic principles of graphics. Excel's charts are more suitable to USA Today than to scientific reports. This bothers some people more than others.

"Good graphs should….[a list of traits]…However, Excel meets virtually none of these criteria. The vast majority of chart types produced by Excel should never be used!" -- Jon Cryer, ref (3).

"Microsoft Excel is an example of a package that does not allow enough user control to consistently make readable and concise graphs from tables."
- A. Gelman et al., 2002, The American Statistician 56, p.123.

A partial solution:
Some of these difficulties (parts of 1,2, 6 and 7) can be overcome by using a good set of add-in routines. One of the best is StatPlus, which comes with an excellent textbook, "Data Analysis with Microsoft Excel". With StatPlus, Excel becomes an adequate statistical tool., though still not in the areas of multiple regression and ANOVA for more than one factor. Without this add-in Excel is inadequate for anything beyond basic summary statistics and simple regression.
Data Analysis with Microsoft Excel by Berk and Carey
published by Duxbury (2000).

Opinion: Get this book if you're going to use Excel for statistics.
(I have no connection with the authors of StatPlus and get no benefit from this recommendation. I'm just a satisfied user.)

Some advice from others:
"If you need to perform analysis of variance, avoid using Excel, unless you are dealing with extremely simple problems."
- Statistical Services Centre, Univ. of Reading, U.K. (at A, below)

"Enterprises should advise their scientists and professional statisticians not to use Microsoft Excel for substantive statistical analysis. Instead, enterprises should look for professional statistical analysis software certified to pass the (NIST) Statistical Reference Datasets tests to their users' required level of accuracy."
- The Gartner Group

References:
1) On the accuracy of statistical procedures in Microsoft Excel 2000 and Excel XP
B.D. McCullough and B. Wilson, (2002), Computational Statistics & Data Analysis, 40, pp 713 - 721

(2) On the accuracy of statistical procedures in Microsoft Excel ‘97
B.D. McCullough and B. Wilson, (1999), Computational Statistics & Data Analysis, 31, pp 27-37

(3) Problems with using Microsoft Excel for statistics [pdf Download]
J.D. Cryer, (2001), presented at the Joint Statistical Meetings, American Statistical Association, 2001, Atlanta Georgia
[pdf download]

(4) Use of Excel for statistical analysis
Neil Cox, (2000), AgResearch Ruakura

(5) Using Excel for statistical data analysis
Eva Goldwater, (1999), Univ. of Massachusetts Office of Information Technology
[pdf download]

(6) Statistical analysis using Microsoft Excel [pdf download]
Jeffrey Simonoff, (2002)
[pdf download]

(7) Spreadsheet addiction
Patrick Burns

(8) On the Accuracy of Statistical Distributions in Microsoft Excel 97
Leo Knuesel
[pdf download]

(9) Statistical flaws in Excel
Hans Pottel
[pdf download]


Guides to Excel on the web:
(A) A Beginner's Guide to Excel - Univ. of Reading, UK

(B) An Intermediate Guide to Excel - Univ. of Reading, UK

Note: All opinions other than those cited as coming from others are my own.