Answer:
A
Step-by-step explanation:
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:
The answer would be "no because boys on the basketball team are likely to be taller than other boys at the school"
Step-by-step explanation:
Hope this helps:)...if not then sorry for wasting your time and may God bless you:)
Answer:
b) 33
Step-by-step explanation:
You reverse the process of finding the second average, 21, which will help you find the original average, 25. First you multiply the second average, 21, to retrace your steps. We do this because the last thing we do when we find the average is divide. when you multiply 21 you get 42. Now you have the value of the first two numbers. Then you experiment with the numbers. To save time I'll get to the point. When you add 33 + 42 you get 75. Then you divide by 3 to get 25. You divide by three because the instructions say there was 3 numbers to start with. Hope this helps.
