Answer:
Y = 6.6406 + 0.2068x2 + 0.7995x3 - 3.607x4
Step-by-step explanation:
y 63 53 77 53 38 43 34 20 13
x1 45 42 40 37 27 24 20 14 12
x2 74 62 80 53 42 48 35 16 13
x3 H H L M H M L L M
For regression analysis : all variables must be quantitative ;
X3 variable may be represented as thus;
L = 1 ; M = 2 ; H = 3
Hence ;
X3 = 3 3 1 2 1 2 1 1 2
y= ____ + (____)x2+ (____)x3 + (____)x4
Using the online multiple regression calculator :
Y = 6.6406 + 0.2068x2 + 0.7995x3 - 3.607x4
From the general formular of a linear model:
y = w1x1 + w2x2 +... + wnxn + c = 0
Where c = intercept
w1,.. wn = weight of explanatory variable
Hence, the weights of x2 = 0.2068
Weight of x3 = 0.7995
Weight of x4 = - 3.607
Intercept = 6.6406
Answer:
A. see below for a graph
B. f(x, y) = f(0, 15) = 90 is the maximum point
Step-by-step explanation:
A. See below for a graph. The vertices are those defined by the second inequality, since it is completely enclosed by the first inequality: (0, 0), (0, 15), (10, 0)
__
B. For f(x, y) = 4x +6y, we have ...
f(0, 0) = 0
f(0, 15) = 6·15 = 90 . . . . . the maximum point
f(10, 0) = 4·10 = 40
_____
<em>Comment on evaluating the objective function</em>
I find it convenient to draw the line f(x, y) = 0 on the graph and then visually choose the vertex point that will put that line as far as possible from the origin. Here, the objective function is less steep than the feasible region boundary, so vertices toward the top of the graph will maximize the objective function.
Answer:
18 ???
Step-by-step explanation:
Answer:
4.5341 < 6.9 < 6.906 < 6.96
Step-by-step explanation:
4.5341 is closer to 0 than 6.96 is.