Question

Omitted variables

Answer 1

Answer:

C. can cause hypothesis tests to be unreliable

Explanation:

Omitted variables are those variables that, when left out of a statistical model, for example linear regression, affects the outcome of the model. It either ignores the impact of the omitted variable to the results or it may assign the effect of the omitted variable incorrectly to the effect of the included variable on the statisical model. This of courses results in the model depicting an upward bias or a downward bias. This is referred to as the Omitted Variable Bias (OVB).

Lets take an example. If you were to run multiple regressions to figure out the factors that affect the prices of houses in an area, you would include multiple variables that you deem significant. The variables you would include in the regression model would include the age of the house, the size of the house, the number of rooms and so on and so forth. However, lets assume that some of the houses are located near an industrial waste plant which negatively impact the price of the house. You forget to include thos proximity variable in your model which would likely make your model biased since the proximity to a waste plant would drastically impact the price of a house that is similar in all aspects with a house that is located further away.

Furthermore, the aren't any statistical models that would located omitted variables therefore, in the context of the question, omitted variables can cause hypothesis tests to be unreliable