Please explain the example below A to E step by step in details, I need to practice on this example and understand the steps.

Dependent variable is: Cost No Selector R squared = 28.5 R squared (adjusted) = 14.2 s = 1,759.7 with 7 - 2 = 5 degrees of freedom Source Sum of Squares df Mean Square F-ratio Regression 6170350 6170350 1.99 Residual 15483162 5 3096632 Variable Coefficient s.e. of Coeff t-ratio Constant 525 541 0.97 LengthDays 2723 484 5.63A magazine reported the average charge and the average length of hospital stay for patients in a sample of 7 regions. The printout is shown below. Complete parts a through e. .Dlick the icon to view the regression printout. 3. Write the equation of a straight-line model relating cost (y) to the average length of hospital stay (x). \"A. y=o+1x+e 5- V=Bo+l51x 6- Y=X+1 +50 9- y=BoX+B1X2 b. The model, part a, was t to the data using Excel/DDXL, as shown in the printout. Find the least squares prediction equation on the printout. v=( +( x (Type integers or decimals.) b. The model, part a, was fit to the data using Excel/DDXL, as shown in the printout. Find the least squares prediction equ y = () + (0) x (Type integers or decimals.) c. List the assumptions required for the regression analysis. Select all that apply. A. The probability distribution is normal. OB. The values of e associated with any two observed values of y are independent. O C. The variance of the probability distribution of e is constant for all settings of the independent variable x. D. The mean of the probability distribution of & is 0. d. Locate the estimated standard error of the regression model, s. on the printout. s =$ (Type an integer or a decimal.) e. Use the value of s to find a range where most (about 95%) of the errors of prediction will fall. Most of the errors of the prediction will fall within $ dollars of the least squares line. (Round to the nearest dollar as needed.)