Answer:
Squared differences between actual and predicted y
Step-by-step explanation:
The least squares regression method used in predictive modeling for linear regression models produces a best fit line which will minimize the square of the mean difference between the actual and projected or predicted values of the dependent, y variable. Hence, the when the sum of the squared value of the difference between the actual and predicted values (residual) are taken, the fit which gives the minimum sum of squared value is the best fit line upon which the estimated regression equation is based.
The x and y intercept for the equation 7x-2y= 14 is x=2 and y=7
Answer:
5/6
Step-by-step explanation:
155+137+25=317°
360° -317°= 43°
Answer:
hey hope this helps
<h3 /><h3>Comparing sides AB and DE </h3>
AB =


DE

So DE = 2 × AB
and since the new triangle formed is similar to the original one, their side ratio will be same for all sides.
<u>scale factor</u> = AB/DE
= 2
It's been reflected across the Y-axis
<em>moved thru the translation of 3 units towards the right of positive x- axis </em>
for this let's compare the location of points B and D
For both the y coordinate is same while the x coordinate of B is 0 and that of D is 3
so the triangle has been shifted by 3 units across the positive x axis