check your calculation for the sums of squares.  They _cannot_ be negative.

the computational form appears to allow a negative value, but if you transform
it mathematically into the definitional form (or the reverse) you will see
that: the definition form cannot be negative, therefore the computational form
cannot.

Jay

Ronny Richardson wrote:

> Several people wrote to tell me that my results from Excel were correct and
> the Bluman textbook had the wrong answer. I'm continuing to experiment with
> ANOVA and I'm hoping that the list can help me answer two additional
> questions I have.
>
> First, in experimenting around with a 3x3 ANOVA without replication, I
> filled the cells with =RAND() to generate random numbers and I happened to
> get the following:
>
>                    SS    df                MS              F
> Rows     -2.105562167     2      -1.052781083    -1.336268078
> Columns  -0.226230909     2      -0.113115455    -0.143574551
> Error     3.151406819     4       0.787851705
> Total     0.819613743     8
>
> Unfortunately, I did not convert the random number functions to numbers so
> the specific data I used to generate this table was lost when the worksheet
> recalculated.
>
> I've repeated this experiment several times and sometimes the sums of
> squares are positive and sometimes one or more of them are negative. Is
> this a bug in Excel? I don't see how a sum of *square* can be negative even
> though I am using random numbers so all of the SS should be in error.
>
> Second, Excel offers a two-factor ANOVA with and without replication. None
> of the examples of two-way ANOVA in any of the textbooks that I own shows a
> two-way ANOVA without replication. It seems counter intuitive to me to
> perform ANOVA with a sample size of one in each cell. Am I missing
> something here?
>
> The output from with and without replication is significantly different as
> well. Using made up data, without replication, I get the following:
>
>                   SS     df      MS     F     P-value       F crit
> Rows           56.25      1   56.25    25      0.1257     161.4462
> Columns       110.25      1  110.25    49      0.0903     161.4462
> Error           2.25      1    2.25
> Total         168.75      3
>
> Using made up data, with replication, I get the following
>
>                   SS     df      MS         F    P-value     F crit
> Sample        234.38      1  234.38  130.8140     0.0000     4.3513
> Columns       693.38      1  693.38  387.0000     0.0000     4.3513
> Interaction     0.38      1    0.38    0.2093     0.6522     4.3513
> Within         35.83     20    1.79
> Total         963.96     23
>
> It makes no sense to me to call them "rows and columns" without replication
> and "sample and columns" with replication. It sort of makes sense not to
> get interaction without replication. I am assuming that with a sample size
> of one in each cell that there is not enough information to compute
> interactions.
>
> Ronny Richardson
> ..
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--
Jay Warner
Principal Scientist
Warner Consulting, Inc.
4444 North Green Bay Road
Racine, WI 53404-1216
USA

Ph: (262) 634-9100
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