For yield of corn suppose there are two factors affecting, nitrogen level and depth of ploughing. Say there are three nitrogen categories(1,2,3) and two depth categories(1,2).

There's a interaction term of nitrogen*depth as well.

If the introduced dummy variables are as

*E_{i1}=1*if ith observation has nitrogen level 1 ,0 otherwise.

*E_{i2}=1*if ith observation has nitrogen level 2 ,0 otherwise.

And

*D=1*if depth category is 1,0 otherwise .

Then the model would be

Y=beta_0+beta_1E_1+beta_2E_2+beta_3D+beta_4[E_1.D]+beta_5[E_2.D]+epsilon

Is this correct?

When the t-statistics are found for the category

*[nitrogen=1*depth=1]*significance value was 0.029 and for

*[nitrogen=2*depth=1]*it was 0.290.

I was asked to interpret if the interaction term significantly affects the yield of corn.

Under 5% confidence level clearly the coefficient of the variable

*[nitrogen=2*depth=1]*is not significant. But since the coefficient of the variable

*[nitrogen=1*depth=1]*is significant can I say that,

**the interaction term significantly affects the yield of corn**.

If both had insignificant coefficients then I could have said that there isn't a significant effect from the interaction term right?

Since the interaction term $[nitrogen=2*depth=1]$ had a insignificant coefficient then my fitted model would be Y=beta_0+beta_1E_1+beta_2E_2+beta_3D+beta_4[E_1.D]

leaving out E2D.IS this correct?

Can someone please help me to figure this out.