More often than not, interactions between various factors are important. Conside

Question

More often than not, interactions between various factors are important. Consider some possible interactions in the Swamp Palace regression: Interaction variables in the last 2 rows are created by simple multiplication. That is FeelGood * Price Perception = FeelGood times PricePerception, and AgeGroup * PricePerception = AgeGroup times PricePerception. Quite often we encounter the situation illustrated here, where while individual variables are not significant (FeelGood, PricePerception, AgeGroup), some of their interactions are significant. What do these results tell us?

Explanation / Answer

In the above Result we see that both the interection effect are significant because both the interection effect p-value less than the level of significance.

Interection between Age group*price perception has less p-vlaue and less coefficient as compare to interection between feelgood*price perception so its gives the good fit of the regression.

In the other hand we that individula variable are not significant because it's p-value are more than the level of significance.

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