Answer:
d) Squared differences between actual and predicted Y values.
Step-by-step explanation:
Regression is called "least squares" regression line. The line takes the form = a + b*X where a and b are both constants. Value of Y and X is specific value of independent variable.Such formula could be used to generate values of given value X.
For example,
suppose a = 10 and b = 7. If X is 10, then predicted value for Y of 45 (from 10 + 5*7). It turns out that with any two variables X and Y. In other words, there exists one formula that will produce the best, or most accurate predictions for Y given X. Any other equation would not fit as well and would predict Y with more error. That equation is called the least squares regression equation.
It minimize the squared difference between actual and predicted value.
Answer:
(0, 4.5)
Step-by-step explanation:
*The equation can be put into Desmos, to find the point, but the work to prove it is here*
f(x)=c/1+Ae^-Bx
Y=C
C=18 A=3 -B=-0.1
*Replace x with 0 in the equation, so you know 0 is the x value, and it leads you to the y value*
f(0)=18/1+3e^-o.1(0)
= 18/1+3e^0
=18/1+3(1)
=18/1+3
=18/4
=4.5
x=0 y=4.5
Maximum growth rate = (x,y) --> (0, 4.5)
Hope this helps:))!!
Answer:
hi
Step-by-step explanation:
1+1=2
