Which of these statements is best? The errors in a regression model are assumed to have an increasing mean. The regression model assumes the error terms are dependent. The errors in a regression model are assumed to have zero variance. The regression model assumes the errors are normally distributed.

And the expected value for a vector of zeros and the covariance matrix is given by:

So we can see that the error terms not have a variance of 0. We can't assume that the errors are assumed to have an increasing mean, and we other property is that the errors are assumed independent and following a normal distribution so then the best option for this case would be:

The regression model assumes the errors are normally distributed.

Step-by-step explanation:

Assuming that we have n observations from a dependent variable Y , given by

And for each observation of Y we have an independent variable X, given by

We can write a linear model on this way:

Where i a matrix for the error random variables, and for this case we can find the error ter like this:

And the expected value for a vector of zeros and the covariance matrix is given by:

So we can see that the error terms not have a variance of 0. We can't assume that the errors are assumed to have an increasing mean, and we other property is that the errors are assumed independent and following a normal distribution so then the best option for this case would be:

The regression model assumes the errors are normally distributed.

⊂⊃⊂⊃ω

step-by-step explanation:

i hope this : )