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STATISTICS QUIZ
by Leland Wilkinson
Statistical Package Test Problems
Here are some problems which reviewers should consider in
evaluating statistical packages. Now that comparisons of microcomputer
programs are being made, it is important to go beyond running the
"Longley" data and tallying the number of statistical procedures
a package contains.
It is easy to select a set of problems to "show off" a particular
package. Just pick several esoteric statsitics contained in only that
package. The following problems are different. They involve basic,
widely encountered statistical issues. A package which cannot solve one
of the problems or solves it incorrectly doesn't "lack a feature." It
has a serious defect. You might want to try these problems on mainframe
packages as well. You may be surprised by the results.
I. READING AN ASCII FILE
Many programs claim to import data files directly. The problem is
that many other programs do not create "ASCII" files in the same way.
Here is a file called ASCII.DAT which contains various formats for
character and numeric data from programs like dBase, Lotus, Supercalc,
FORTRAN, and BASIC. If a program cannot read every record in this file
without preprocessing, then there are some ASCII files on microcomputers
and mainframes which it cannot read.
1 2 3 4 5 "ONE"
1 2 3
4 5 "TWO"
1,2,3,4,5,'THREE'
1 2 3 4 . FOUR
1.0E0 2.E0 .3E1 4.00000000E+0 5D0 FIVE
1 2 3 4 5 SIX
The case labeled ONE is the most common form of ASCII data:
numerals separated by blanks and character strings surrounded by quotes.
Most programs can handle this case.
The case laveled TWO spans two records (there is a carriage return
and linefeed after the 3). Some spreadsheets and word processors do this
when they impose margins on a page. A statistical package ought to be
able to read these two records as a single case without special
instructions. If, on the other hand, this is considered an error, then
the program should be able to flag it.
The case labeled THREE has comma delimters (as in BASIC). It also
uses apostrophes rather than quotes for character stringss (PL/I and
other mainframe packages do this).
The case laveled FOUR has a missing value (on variable E). SAS,
SPSS, and other packages put missing values in a file this way. It also
lacks quotes around the character variable, a common occurrence.
page one
The case laveled FIVE has various forms of exponential notation.
FORTRAN uses a "D" instead of E for double precision exponents. The
other forms were taken from various microcomputer and mainframe
packages.
The case labeled SIX does not belong in an ASCII file. It is so
common, however, that statistical packages should not be bothered by it.
Namely, there are tab characters in the record.
You can create this file on a word processor (be sure to use the
ASCII, or "nondocument" mode) or with an editor. For the last line,
use the tab key to separate the number (the way most secretaries do
when told to enter data this way) and be sure to hit the Return (Enter)
key after yping SIX so that the last record ends in a carriage return.
Since you can use an editor to create the file, you could correct some
of the problems with an editor as well before using a statistical
program. What yould you do with a file containing 30,000 records on 100
variables?
A. Read this file into 6 variables: A, B, C, D, E, and NAME$ (or
whatever the package uses to name a character variable). Print the
file so that 6 cases appear, with A=1, B=2, C=3, D=4, E=5, and
NAME$=<label>.
B. Read this file so that the program will flag the second case as
an error. In other words, the program should accept as valid
records only cases with all six data items on a record.
II. REAL NUMBERS
Every statistical package is capable of computing basic statistics
or simple numbers such as relatively small integers. Scientific work,
financial analysis, and other serious applications require more,
however. The following dataset called NASTY.DAT has been designed to
test the limits of packages. These are not unreasonable numers. The
ability to deal with data like these should be regarded as a minimum.
For example, the values on BIG are less than the U.S. population. HUGE
has values in the same order of magnitude as the U.S. deficit. TINY is
comparable to many measurements in engineering and physics.
LABEL$ X ZERO MISS BIG LITTLE HUGE TINY ROUND
ONE 1 0 . 99999991 0.99999991 1.0E12 1.0E12 0.5
TWO 2 0 . 99999992 0.99999992 2.0E12 2.0E12 1.5
THREE 3 0 . 99999993 0.99999993 3.0E12 3.0E12 2.5
FOUR 4 0 . 99999994 0.99999994 4.0E12 4.0E12 3.5
FIVE 5 0 . 99999995 0.99999995 5.0E12 5.0E12 4.5
SIX 6 0 . 99999996 0.99999996 6.0E12 6.0E12 5.5
SEVEN 7 0 . 99999997 0.99999997 7.0E12 7.0E12 6.5
EIGHT 8 0 . 99999998 0.99999998 8.0E12 8.0E12 7.5
NINE 9 0 . 99999999 0.99999999 9.0E12 9.0E12 8.5
page two
A. Print ROUND with only one digit. You should get the
numbers 1 to 9. Many language compilers, such as Turbo Pascal and
Lattice C, fail this test (they round numbers inconsistently).
Needless to say, statical packages written in these languages
may fail the test as well. You can also check the following
expressions:
Y = INT(2.6*7 0.2) (Y should be 18)
Y = 2INT(EXP(LOG(SQR(2)*SQR(2)))) (Y should be 0)
Y = INT(3EXP(LOG(SQR(2)*SQR(2)))) (Y should be 1)
INT is the integer function. It converts decimal numbers to
integers by throwing away numbers after the decimal point. EXP
is exponential, LOG is logarithm, and SQR is suqare root. You may
have to substitute similar names for these functions for different
packages. Since the square of a square root should return the same
number, and the exponential of a log should return the same number,
we should get back a 2 from this function of functions. By taking
the integer result and subtracting from 2, we are exposing the
roundoff errors. These simple functions are at the heart of
statistical calculations.
IBM and Microsoft BASIC (any any statiscal packages written in
them) fail these tests. If a statistical package fails these
tests, you cannot trust it to compute any functions accurately. It
might even fail on simple arithmetic.
B. Plot HUGE against TINY in a scatterplot. The values should fall
on a line. Plot BIG against LITTLE. Again, the values should fall
on a line. Plot X against ZERO. Some programs cannot produce this
last plot because they cannot scale an axis for a constant.
C. Compute basic statistic on all the variables. The means should
be the fifth value of all the variables (case FIVE). The standard
deviations should be "undefined" or missing for MISS, 0 for ZERO,
and 2.738612788 (times 10 to a power) for all the other variables.
D. Compute a correlation matrix on all the variables. All the
correlations, except for ZERO and MISS, shoud be exactly 1. ZERO
and MISS should have undefined or missing correlations with the
other variables. The same should go for SPEARMAN corelations, if
your program has them.
E. Tabulate X against X, using BIG as a case weight. The values
should appear on the diagonal and the total should be 899999955.
If the table cannot hold these values, forget about working with
census data. You can also tabulate HUGE against TINY. There is no
reason a tabulation program should not be able to digtinguish
different values regardless of their magnitude.
F. Regress BIG on X. The constant should be 99999990 and the
regression coefficient should be 1.
page three
III. MISSING DATA
Many programs claim to process missing data. This usally means
that they delete missing values before computing statistics. Missing
data processing goes considerably beyond this, however. Most social
science research requires consistent processing of missing values in
logical and arithmetic expressions. Here are some simple tests.
A. Use the NASTY dataset above on the following transformation:
IF MISS = 3 THEN TEST = 1
ELSE TEST = 2
If a package does not have an else statement (a serious omission if
you are doing a lot of IFTHEN transformations), you can code the
second statement as "IF MISS <> 3 THEN TEST = 2" where <> is "not
equal". TEST should have the value 2 for all cases because MISS
does not anywhere equal 3 (i.e. missing values do not equal 3).
Some packages have an "indeterminate" value if MISS=<missing> and
assign neither 1 or 2 to TEST. That is OK provided they assign
a missing value to TEST. IF the package assigns any other value to
TEST (say, 1), don't trust it for any logical comparisons.
B. Use the NASTY dataset on the following calculation:
IF MISS = <missing> THEN MISS = MISS + 1
This transformation should leave the values of MISS as missing (you
cannot add 1 to something that is missing).
C. Use the NASTY dataset and tabulate MISS against ZERO. You
should have one cell with 9 cases in it. This ability to tabulate
missing values against other variables is essential for analyzing
patterns of missing data in a file. Now tabulate MISS against ZERO
excluding missing values from the tablulation. You should be
notified that there are no nonmissing values to tabulate.
IV. REGRESSION
Regression is one of the most widely used statistical procedures.
By now, almost every regression program can compute most of the digits
of the Longley data. This particular dataset measures only one kind of
illconditioning, however. Here are some additional problems designed
to expose whether the programmer thought about boundary conditions. If
a program blows up on these problems, you should worry about the times
it might not blow up and give you an innocuous looking wrong answer.
A. Take the NASTY dataset above. Use the variable X as a
basis for computing polynomials. Namely, compute X1=X, X2=X*X,
X3=X*X*X, and so on up to 9 products. Use the algebraic
transformation language within the statistical package itself. You
will end up with 9 variables. Now regress X1 on X2X9 (a perfect
fit). If the package balks (singular or roundoff error messages),
try X1 on X2X8, and so on. Most packages cannot handle more than
a few polynomials.
page four
B. Regress X on X. The constant should be exactly 0 and the
regression coefficient should be 1. This is a perfectly valid
regression. The program should not complain.
C. Regress X on BIG and LITTLE (two predictors). The program
should tell you that this model is "singular" because BIG and
LITTLE are linear combinations of each other. Cryptic error
messages are unacceptable here. Singularity is the most
fundamental regression error.
D. Regress ZERO on X. The program should inform you that ZERO has
no variance or it should go ahead and compute the regression
and report a correlation and total sum of squares of exactly 0.
V. ANALYSIS OF VARIANCE
Every statistical package has some sort of ANOVA routine. Only a
few have least squares ANOVA, which is the only widely used method for
dealing with unequal cell sizes. These few packages all offer a variety
of factorial, repeated measures ANOVA, MANOVA, and analysis of
coavariance. There are major differences in the way least squares is
implemented, however. Here are some simple examples which exploit the
differences and involve widely needed features. If you cannot set these
examples up with the help of the user manual, call technical suport.
That is a test in itself.
A. Simple contrasts. The following data contain an unbalanced
design with a significant interaction. A least squares analysis
shows the main efefct for A is not significant, but this test is
not particularlrly meaningful because of the interaction. Test,
therefore, the simple constrast between A1 and A2 within B1. Then
test A1 vs. A2 within B2. Both tests should use the same residual
error term (separate ttests are unacceptable). Several widely
used mainframe programs fail this test. Unless the program can
constrast any terms in a model (not just main effects), it cannot
handle this frequently encountered type of problem.
B1 B2

 2  3 
A1  1  4 
 3  5 
 2  

 4  2 
A2  6  4 
 5  5 
  

page five
B. Random effects. Now assume that factor B in the above design is
a random factor (and A is fixed). This means that the A*B
interaction is the appropriate error term for testing A. Calculate
the F statistic for this hypothesis using the sum of squares for
A*B as the error term. You could do this with a calculator, since
all the sums of squares are on the same printout. If this were a
MANOVA, however, you would need a command to specify an error term
other than residual (within cell) error. Otherwise, mixed model
analyses are impossible. You can save yourself some trouble by
checking the index or table of contents before trying this problem.
If there is no listing for mixed models or random factors, forget
it. These models are widely used in biology, accounting,
marketing, psuchology, linguistics, education, and the physical
sciences.
C. Diagnostics. Statisticians have demonstrated recently the
importance of diagnostics in linear models. It has long been
believed, for example, that ANOVA is robust to outliers and other
unusual cases. This is generally false, however. A single
observation can cause an interaction to become significant in a
twoway design. That is the case with the data above, for example.
Your assignment is to identify that case. The best way to identify
influential observatitions is to plot certain "influence statistics."
One of the most widely used is called "Cook's D." It is output in
all the better regression packages. Try to find it in your ANOVA
package, where it is needed just as much, if not more. In fact,
see if you can save into a file all the other reiduals and
diagnostic information you get from a good regression package.
These same tools, as well as graphical displays of statistics like
residuals and Cook's D and least squares means, are required in any
professional ANOVA package. Otherwise, you will never know if you
have a rotten apple in the barrel. Incidentally, the main effect
for A becomes highly significant when one case is dropped from the
analysis above.
VI. OPERATING ON A DATABASE
Every statistical package of any worth can transform columns of
numbers. What distinghishes some packages for research and business
applications if the abilitiy to manipulate a database which may not be
rectangular. This means more than being able to sort, append, and merge
(join) files. Here are a few problems which frequently arise in various
fields.
A. The following problem was encountered by the National Park
Service. There are two files. One (BOATS) has names of boats
(NAME$), the day they left port (DEPART), and the day they returned
home (RETURN). The other file (WEATHER) has the daily temperature
(TEMP) for each day (DAY) of the year, numbered from 1 to 365
during the year the boats were out. Here are the files:
page six
BOATS WEATHER
 
NAME$ DEPART RETURN DAY TEMP
Nellie 1 10 1 48
Ajax 4 6 2 40
Queen 3 3 3 45
Ajax 2 3 4 52
6 44
7 48
8 49
9 50
10 52
11 50
12 49
Now, neither file has the same number of records, of course, but
the BOATS file may have multiple records for each boat, since each
went on one or more cruises during the year. Your task is to
create a file (CRUISE) with a separate record for each boat and the
average temperature (AVGTEMP) during its cruise. Warning: the
weather for day 5 is missing. Ignore it in computing the average
temperature.
B. The following file contains sales on various dates during the
year (MONTH, DAY, YEAR, SALES).
1/12/84 400
1/15/84 131
1/16/84 211
1/18/84 312
1/15/84 220
1/12/84 200
2/1/84 312
1/12/84 350
11/5/83 200
1/15/84 400
Some dates are duplicated in the file. This is because the sales
were recorded several times in the day before closing.
1) Read this file exactly as given. This is an additional test of
the package's ability to deal with unusual ASCII files.
2) Print a new file sorted by date. For days having two or more
sales records, print only the record with the highest (closing)
sales.
page seven
======================================================================
The above seven pages are verbatim from Wilkinson's "Statistics
Quiz". The remaining 20 pages presented computer output showing
the (correct) solutions obtained by the software package SYSTAT.
Since the correct solution frequently is given in the problem,
here we include only the SYSTAT solutions to those problems for
which the solution was not given.
II. C. Basic Statistics
variable mean standard deviation
X 5.000000000 2.738612788
ZERO 0.000000000 0.000000000
MISS . .
BIG 99999995.00 2.738612788
LITTLE 0.999999950 2.73861E08
HUGE 5.00000E+12 0.22739E+13
TINY 0.000000000 0.000000000
ROUND 4.500000000 2.738612788
II.D. Correlation
X ZERO BIG LITTLE HUGE TINY ROUND
X 1.0
ZERO . .
BIG 1.0 . 1.0
LITTLE 1.0 . 1.0 1.0
HUGE 1.0 . 1.0 1.0 1.0
TINY 1.0 . 1.0 1.0 1.0 1.0
ROUND 1.0 . 1.0 1.0 1.0 1.0 1.0
IV. A. Determining these coefficients will stress most any
leastsquares package, so do not be alarmed if your coefficients
are different than these. However, your package should
provide an R**2 close to unity. Theoretically, the integrated
square error (ISE) of the estimated polynomial should be zero,
but in practice it will not be. However, the ISE should be
close to the value provided below; if yours is much large
your package has a problem. See Sawitizki (1994) for
more details on these matters.
variable coefficient
constant 0.353486
X**2 1.14234
X**3 0.70495
X**4 0.262353
X**5 0.061635
X**6 0.00920536
X**7 0.000847477
X**8 0.000043835
X**9 0.000000974112
ISE = 0.0393
To check the calculation of the ISE with your coefficients,
first use Sawitizki's coefficients and obtain his answer
to make sure your ISE program works, before trying to obtain
the ISE with your coefficients.
V. A. Simple Contrasts
DEP VAR: X N: 13 MULTIPLE R: 0.780 SQUARED MULTIPLE R: 0.608
ANALYSIS OF VARIANCE
SOURCE SUMOFSQUARES DF MEANSQUARE FRATIO
A 5.689 1 5.689 4.800
B 0.356 1 0.356 0.300
A*B 8.889 1 8.889 7.500
ERROR 10.667 9 1.185
TEST A WITHIN B1
A MATRIX
1 2 3 4
0.000 1.000 0.000 1.000
TEST OF HYPOTHESIS
SOURCE SS DF MS F
HYPOTHESIS 15.429 1 15.429 13.018
ERROR 10.667 9 1.185
TEST A WITHIN B2
A MATRIX
1 2 3 4
0.000 1.000 0.000 1.000
TEST OF HYPOTHESIS
SOURCE SS DF MS F
HYPOTHESIS 0.167 1 0.167 0.141
ERROR 10.667 9 1.185
V.B. RANDOM EFFECTS
TEST OF HYPOTHESIS
SOURCE SS DF MS F
HYPOTHESIS 5.689 1 5.689 0.640
ERROR 8.889 1 8.889
V.C. DIAGNOSTICS
The large Cook's distance for Case 11 stands out.

References

Sawitzki, G. (1994) "Testing Numerical Reliability of Data
Analysis Systems", Computational Statistics and Data
Analysis 18, 269286
Sawitzki, G. (1994) "Report on the Numerical Reliability
of Data Analysis Systems", Computational Statistics and
Data Analysis 18, 289301
