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 six because 3n=6-5 3 n=6-5 then subtract 5 5 from 6 6
Ummm if its multiply then its 1 and 6. is its addition then its 1 and 5 or 6 and 0. if its divide it would be 6 and 1. is its subtraction then it would be 7 and 1 or 6 and 0
V=π * r^2 * h/3 = π * 2^2 * 8/3 ≈ 33.51032 or about 34 units^2
