Answer:
II. The sum of the residuals is always 0.
Step-by-step explanation:
A least squares regression line is a standard technique in regression analysis used to make the vertical distance obtained from the data points running to the regression line to become very minimal or as small as possible.
For any least-squares regression line, the sum of the residuals is always zero.
Basically, residuals are used to measure or determine whether or not the line of regression is a good fit or match for the data by subtracting the difference between them i.e the predicted y value and the actual y value, for the x value respectively.
Hence, the statement about residuals which is true for the least-squares regression line is that the sum of the residuals is always zero (0).
First, we find the area of the circle.
A = pi * r^2
A = pi * (1 cm)^2
A = pi cm^2
The area of the circle is pi cm^2.
The area of the triangle is also pi cm^2.
Now we use the area of a triangle.
A = (1/2)bh
(1/2)bh = A
(1/2)(3 cm)h = pi cm^2
(3 cm)h = 2pi cm^2
h = (2/3)pi cm
The exact height is 
If you want an approximate height, then it is 2.09 cm.
Answer:
the smallest sample size is 163
Step-by-step explanation:
The computation of the smallest sample size that meets these criteria is shown below:
n = (Z a/2 × Standard deviation ÷ Margin of error ) ^2
Zα/2 at 0.05% LOS is = 1.96 ( From Standard Normal Table )
Standard Deviation ( S.D) = 2.6
Margin of error i.e. ME =0.4
n = ( 1.96 × 2.6 ÷ 0.4) ^2
= 163
Hence, the smallest sample size is 163
4x^3-16x^2+12-3x
Rearrange: (4x^3-16x^2)-(3x+12)
Factor out GCF : 4x^2(x-4)-3(x-4)
Answer: (4x^2-3)(x-4)
I hope this helped! :)
