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:
change to improper fraction
24/5 × 20/6=16 (you cross multiply)
Answer:
64 hope it helps
Step-by-step explanation:
the correct question in the attached figure
(2^5)/8=2^2
we have
8----------- >2^3
(2^5)/8----------- > (2^5)/(2^3)=2^(5-3)=2^2
therefore
2^2=2^2-- ------> is ok
the answer is by simplifying 8 to 2^3 to make both powers base two and subtracting the exponents
Answer: Distributive because 6 has to be multiplied by x and 7
