Answer:
B) The sum of the squared residuals
Step-by-step explanation:
Least Square Regression Line is drawn through a bivariate data(Data in two variables) plotted on a graph to explain the relation between the explanatory variable(x) and the response variable(y).
Not all the points will lie on the Least Square Regression Line in all cases. Some points will be above line and some points will be below the line. The vertical distance between the points and the line is known as residual. Since, some points are above the line and some are below, the sum of residuals is always zero for a Least Square Regression Line.
Since, we want to minimize the overall error(residual) so that our line is as close to the points as possible, considering the sum of residuals wont be helpful as it will always be zero. So we square the residuals first and them sum them. This always gives a positive value. The Least Square Regression Line minimizes this sum of residuals and the result is a line of Best Fit for the bivariate data.
Therefore, option B gives the correct answer.
Answer:
9
Step-by-step explanation:
r/3 +5 ≤ 8
Subtract 5 from each side
r/3 +5 -5≤ 8-5
r/3 ≤ 3
Multiply each side by 3
r/3 *3 ≤ 3*3
r ≤ 9
The only value that is less than or equal to 9 is 9
Answer: 12:14 pm.
Explanation: 27 minutes after when Sally started, 11:48, is 12:14.
11/12 is the answer, I did this math problem before
Answer:
340 degrees
Step-by-step explanation:
So the key thing here is to notice that we are given the circumference which will allow us to find a value for the radius of the circle and hence the angle subtended by the arc (the central angle).
So the circumference of a circle = 2pi(r)
This means:
6 = 2pi(r)
Which means that
r = 6/2pi or r = 3/pi
Now we can use this value of r to find our angle in conjunction with the value of the arc length. So:
Arc length is defined by: length = θr
Where θ is our angle value.
So lets plug in:
Multiply by pi to get:
Divide by 3 to get that:
θ = 17pi/9
So if we convert that from radians to degrees we get 340 degrees.