Answer:
A line fitted to data points that minimizes the sum of the squared residuals
Step-by-step explanation:
The principle of least square(LS) consists of determining the values of the unknown parameters that will minimize the sum of squares of errors (or residuals) where errors are defined as the differences between observed values and the corresponding values predicted or estimated by the fitted model equation.
Thus, the correct definition of least square regression line is "<em>A line fitted to data points that minimizes the sum of the squared residuals</em>"
a) if AB = CD, then 3CD = 3(CD + 1) NOT TRUE
example: AB = CD = 1, then 3CD = (3)(1)=3 and 3(CD + 1) = 3(1 + 1) = 3(2) = 6
b) if AB = CD, then AB - CD = A - C + B - D NOT TRUE (it does not make sense because A, B, C and D are points)
c) if AB = CD, then AB + EF = CD + EF TRUE
because
AB = CD |add EF to both sides
AB + EF = CD + EF
d) if AB = CD, then 2AB = 2A + 2B NOT TRUE (it does not make sense because A and B are points).
Answer:
Exact Form:
1/4
Decimal Form:
0.25
Step-by-step explanation:
Answer: x and y intercepts are where the line crosses over the x and y axis.
Step-by-step explanation:
