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.
The coordinates of the vertices of ΔABC are:A( x1, y1), B( x2, y2) and C( x3, y 3 ). After it is reflected across the x-axis, coordinates are ( x1, -y1), (x2, -y2), (x3, -y3). Finally, the coordinates of the vertices of ΔA´B´C´ after translation are: A´( x1, 4-y1), B´( x2, 4- y2), C´( x3, 4-y3 )
The nearest tenth would be 10.1
Answer: The tip is $17
Step-by-step explanation: You multiply $85 by .2 to get this answer.
Yes because you multiply first so in this case you multiply 7 and 3 first. Since the second expression is in parenthesis you do that first just like you do 7 times 3. Then you just add 2 to that. The value of both expressions is 23
