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.
8+(6)(3)^2
8+(6)(9)
8+(54)
8+54
62
I hope it helps
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Answer:
Hope this solution helps you
Answer:

Step-by-step explanation:
Hello,
As the polynomial has only real coefficients, it means that 3-i is a zero too, because we apply the Conjugate Zeros Theorem.
It means that we can write the expression as below, k being a real number that we will have to identify.

And for x = 0, y = -90 so we can write
-90=k*3*10, meaning that k=-3
Hope this helps.
Do not hesitate if you need further explanation.
Thank you
(3x-4)(5x^2-2x+6)
15x^3-6x^2+18x-20x^2+8x-24
15x^3-26x^2+26x-24