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.
The answer is b bc it’s all real numbers since it won’t stop
Answer:
y + 4 = -3 (x - 5)
In other words,
y = -3 x + 11
Step-by-step explanation:
The slope of the tangent line to y = g(x) at x = 5 is the same as the value of g'(x). g'(5) = 3. Therefore, 3 will be the slope of the tangent line.
The tangent line goes through the point of tangency (5, g(5)). g(5) = -4. Therefore, the tangent line passes through the point (5, -4).
Apply the slope-point form of the line. The equation for a line with slope <em>m</em> that goes through point (a, b) will be y - b = m(x - a). For the tangent line in this question,
What will be the equation of this line?
Answer:
r = 7
Step-by-step explanation:
To solve this, we can plug in a pair of x and y values and solve for r.
y = rx | Plug in a pair
42 = r*6 | Now divide both sides by 6
7 = r.
We can test this by plugging in r with a pair.
y = (7)x
77 = 7*11, 77 = 77, This equation is correct.
