Find the equation y = β0 + β1x of the least-squares line that best fits the given data pts. (0,2), (1,2), (2,3), (3,3) The line y is __ + (__)x. (Type integers or decimals.)

Hello there, can you help me solve the problem with two subparts (b and c) Problem: (the image is attached) Subparts: b. Find the least-squares curve of the form above to fit the data (4, 1.57), (6, 2.05), (8, 2.5), (10, 2.7), (12, 3.1), (14, 3.6), (16, 3.8), and (18, 4.35), where x and y represent sales and costs in thousands. Produce a graph that shows the data pts and the graph of the cubic approximation. y = __x + (__)x2 + (__)x3   c. (the image is attached)

Which of the following best describes the least-squares line fit to the data shown in the plot? (i) bo = 0, bị =-1 (ii) bo = -3, b₁ = 1 (iii) bo-5, b₁ = 2 (iv) bo = −3, bị =-1 (v) bo = 0, b₁ = -3 2 X

We use the form  ŷ = a + bx  for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from a report showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in a state. A Minitab printout provides the following information. Predictor Coef SE Coef T P Constant 315.27 28.31 11.24 0.002 Elevation -31.812 3.511 -8.79 0.003 S = 11.8603 R-Sq = 96.8%       Notice that "Elevation" is listed under "Predictor." This means that elevation is the explanatory variable x. Its coefficient is the slope b. "Constant" refers to a in the equation  ŷ = a + bx. (a) Use the printout to write the least-squares equation. ŷ =   +   x (b) For each 1000-foot increase in elevation,…