To provide the answer you need, I'll explain each component:

1. Mean Square (MS): This is a measure used in ANOVA or similar analyses. It's calculated by dividing the sum of squares (SS) by the degrees of freedom (df).

   Formula:
   $MS = \frac{SS}{df}$

2. F-value: This is the ratio of two mean squares, typically the mean square of the effect of interest divided by the mean square of the error.

   Formula:
   $F = \frac{MS_{\text{effect}}}{MS_{\text{error}}}$

3. p-value: This is the probability that the observed F-value occurred by chance, assuming the null hypothesis is true. It is obtained by comparing the F-value to a statistical distribution (F-distribution) with the relevant degrees of freedom.

Steps to Calculate:

1. Compute MS for each group (effect and error).
2. Compute the F-value as the ratio of MS values.
3. Use an F-distribution table or statistical software to find the p-value corresponding to the calculated F-value and degrees of freedom.

Provide Specific Details:

If you have the values for the sum of squares, degrees of freedom, or the F-value, share them, and I’ll calculate it directly. If you have a dataset, upload it for analysis.

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Follow-Up Questions:

1. What values of sum of squares and degrees of freedom are given?
2. Is this for a one-way or two-way ANOVA test?
3. Should the p-value be calculated for a one-tailed or two-tailed test?
4. Do you need the significance threshold ($\alpha$) for decision-making?
5. Would you like to see a demonstration using a dataset?

Tip: Always ensure you use the correct degrees of freedom for the numerator and denominator in F-tests to