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.
I think it’s 5 but don’t get mad if it’s wrong
The answer is that his average is 75.5 for the second nine weeks
Answer:
60-9pi if you need that rounded then 31.73
Step-by-step explanation:
10*6= 60
the two semi circles make one full circle. a circle formula is pi*r^2 = pi*3^2= 9pi
then subtract 9pi from 60
60-9pi = 31.73
Answer:
-0.04
Step-by-step explanation: