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:
Step-by-step explanation:
Hey dude we’re both from Nepal so here’s the written worksheet hope it helps :)
Answer:
Step-by-step explanation:
Given that Heights of 10-year-olds, regardless of gender, closely follow a normal distribution with a mean of 55 inches and a standard deviation of 6 inches.
If X is height then X is N(55,6)
Z score would be 
b) 
c) Tallest 10% z = 1.28
X score = 
inches
d)
i.e. 16.37% cannot go on this ride.
