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:
13.9188 dollars
Step-by-step explanation:
1.1599 * 12 = 13.9188
Answer: x=133
Step-by-step explanation:
This is a hexagon that has a total of 720 degrees (which you can get by the formula of degrees = (# of sides – 2) * 180).
You first add up all your side:
150+130+97+120+90 +x = 587 + x
587+x = 720
x=133
Answer:
a. $14.50
b. $43.5
Step-by-step explanation:
a. $7.00 + $7.50 = $14.50
b. y = 14.5x x=3
y = $14.5 x 3 = $43.5
Answer:
Step-by-step explanation:
The number of t-shirts a in month n is 70% of the number of t-shirts in month n-1 plus 25 because they always sell 30% of the t-shirts and get 25 t-shirts in shipments. 
can be included as an after-thought.
