Consider the multiple regression model with two regressors x1 and x2, where both variables are determinants of the dependent variable y. you first regress y on x1 and find no relationship. however, while regressing y on x1 and x2 the slope coefficient of the variable x1 changes by a large amount. this suggests that your first regression suffers from:
a. perfect multicollinearity
b. dummy variable trap
c. omitted variable bias
d. heteroskedasticity

C. Omitted variable bias

Step-by-step explanation:

In mathematics and statistics, omitted-variable bias (OVB) happens if one or more important variables is left out by a statistical model.

The bias results in the equation being related to the expected effects of the included variables by the influence of the excluded variables.

it is definatly the 3rd one. very much looks like it, for a good chance, the third one would be best for you.

8(4)^8=72^feet to say that the total can conclude to 8(4) which as you know would mean 8 x 4. that equals 32w. 32^ could make a 72 answer as well. but your coefficent will remain w so your answer is 72w.

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