Which of the following assumptions is not required for the Least Squares principle to produce the line of best fit:. a. the error term (e) is a normally distributed random variable
b. R-square must be greater than 1.0
c. the variance of the error term is constant
d. all of the above are correct answers

b. R-square must be greater than 1.0

Step-by-step explanation:

For a least square regression fit, Assumptions include : Absence of heteroscedasticity, which is the variation must not occur in the variance of the error term. That is the variance between error term and each observation is consistent.

Also, Lack of correlation between observations of the error term and also between independent variables and the error term. It is highly recommended that error term be randomized and normally distributed variable.

The value of R - squared being greater Than 1 is. not an assumption of the least square principle for fitting a regression line.

answer: 15

step-by-step explanation:

answer: domain and range mean all possible values of x and y that could be substituted to the inequality. a solution means all possible values that make the inequality statement true. so while the range is still all real numbers, the solution is the shaded area, for which y will always be greater than the line of the function.