1. You want to develop a scary monster to star in a horror film. You’re pretty s

Question

1. You want to develop a scary monster to star in a horror film. You’re pretty sure that blood and teeth make the monster more scary, whereas cuteness makes the monster less scary. You want to put this to the test with a linear regression model. Which of the following formulas would be appropriate?

a. Scariness = a - 1.6*(blood) - 2.8*(teeth) + 3.2*(cuteness)

b.Scariness = a + 1.6*(blood) - (teeth) - (cuteness)

c. Scariness = a - 1.6*(blood)*(teeth) + 3.2*(cuteness)

d. Scariness = a + 1.6*(blood) + 2.8*(teeth) - 3.2*(cuteness)

2. Your friend says that your scary monster model is useless, and the only thing that makes a difference to scariness is whether the monster has a weapon. What could you show your friend to prove that your model is useful?

a. Compare the slope values for each predictor.

b. The model’s R-squared.

c. The model’s intercept.

d. The correlation between scariness and each predictor.

Explanation / Answer

1. You want to develop a scary monster to star in a horror film. You’re pretty sure that blood and teeth make the monster more scary, whereas cuteness makes the monster less scary. You want to put this to the test with a linear regression model. Which of the following formulas would be appropriate?

a. Scariness = a - 1.6*(blood) - 2.8*(teeth) + 3.2*(cuteness)

b.Scariness = a + 1.6*(blood) - (teeth) - (cuteness)

c. Scariness = a - 1.6*(blood)*(teeth) + 3.2*(cuteness)

d. Scariness = a + 1.6*(blood) + 2.8*(teeth) - 3.2*(cuteness)

blood and teeth both increases the scaryness , hence there coefficients must be positive . Cuteness decreases scariness , its coefficient must be negative. Hence the only option satisfying the 3 conditions is D

2. Your friend says that your scary monster model is useless, and the only thing that makes a difference to scariness is whether the monster has a weapon. What could you show your friend to prove that your model is useful?

a. Compare the slope values for each predictor.

b. The model’s R-squared.

c. The model’s intercept.

d. The correlation between scariness and each predictor. This would show the relation between each variable and the dependent variable scariness.

Other options tell us about the model as a whole , they do not tell us how the independent variables relate to dependent variable

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