The answer is: 34 persons received the $50 gift card.
If we need to know how many many people received a $50 gift card over the two days (Sunday and Monday) we need to divide the total attendance by the number 75 because every 75 persons, there is a $50 gift card.
So, calculating we have:
Then, dividing the total attendance by 75 to know how many people received a $50 gift card over the two days, we have:
Hence, rounding to the nearest whole number, 34 persons received the $50 gift card.
It provides three different hypotheses in such a two-factor ANOVA:
In point A:
H o: With all factor A levels, the ways are equivalent
Ha: A least another element A level does have a transfer to another
In point B:
Ha: The least one Factor A the level does have a transfer to another
H o: With all Factor B levels the results are about the same.
In point C:
Ha: At most one Variable B level does have a transfer than any other level.
H o: There are no interactions among the factors
Ha: The interactions of factors are important
When ANOVA is executed, they get three p-sets (one for all 3 hypotheses)
(a) If Variable A's p-value is much less alpha, we will reject the null and embrace Ha and infer that Factor A is important. Anything else, H o also isn't rejected and that there is no evidence which Factor A is important
(b) If p- is < alpha, otherwise we reject The null, accept Ha, and infer which Factor B is relevant. Factor B is significant. Conversely, we may not condemn H o but claim there isn't enough proof which Factor B is important
If they reject H o and agree to the point p- for the A x B interaction is a < alpha Ha, and conclude that the interaction from A to B is important. So, perhaps we can deny H o and claim, that neither proof of interactions is sufficient From A to B.