Answer = <span>6 US gallons</span>
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 agree with you on this problem. It is A. If that is the final answer, the negative exponent must be flipped in order to be positive. According to the equation, it is not positive so it is not a simpflied polynomial.
Answer:
where is your shape
Step-by-step explanation:
firstly give shape then we find out perimeter
na