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.

final total: 63

step-by-step explanation:

60 times sales tax

60 times 0.05

3

60 + 3

63

d

step-by-step explanation:

the two points from this line are   (0,4)   & (2,-2)

we can call

now, lets find the slope (m):

now finding b (the y intercept). this is the point at the y-axis where the line cuts the axis. looking at the graph, it is y = 4. so the point is 4. hence b = 4

the slope-intercept form is given by  

putting the points, we have:

d is the correct answer.