Answer:
The standard deviation of the residuals calculates how much the data points spread around the regression line. The result is used to measure the error of the regression line's predictability.
Step-by-step explanation:
<h2>How do you find the standard deviation around the regression line?</h2>
STDEV. S(errors) = (SQRT(1 minus R-squared)) x STDEV. S(Y). So, if you know the standard deviation of Y, and you know the correlation between Y and X, you can figure out what the standard deviation of the errors would be be if you regressed Y on X.
<h2>What does standard deviation tell you?</h2>
A standard deviation (or σ) is a measure of how dispersed the data is in relation to the mean. Low standard deviation means data are clustered around the mean, and high standard deviation indicates data are more spread out.
Answer:
giving each employee a unique number from 1 to 250 and randomly selecting 50 people to take the survey
Step-by-step explanation:
your welcome.
Answer:
Cubic binomial
Step-by-step explanation:
There are 2 terms, so it is a binomial.
The degree is 3, so it is cubic.
Therefore, the polynomial is a cubic binomial.
One A
y = e^x
dy/dx = e^x The f(x) = the differentiated function. Any value that e^x can have, the derivative has the same value. x is contained in all the reals.
One B
y = x*e^x
y' = e^x + xe^x Using the multiplication rule.
You want the slope and the value of the of y to be the same. The slope is y' of the tangent line
xe^x = e^x + xe^x
e^x = 0
This happens only when x is very "small" like x = - 4444444
y = e^x * ln(x) Using the multiplication rule again, we need the slope of the line with is y'
y1 = e^x
y1' = e^x
y2 = ln(x)
y2' = 1/x
y' = e^x*ln(x) + e^x/x So at x = 1 the slope of the line =
y' = e^1*ln(1) + e^1/1
y' = e*0+e = e
y = mx + b
y = ex + b
to find b we use y= e^x ln(x)
e^x ln(x) = e*x + b
e^1 ln(1) = e*1 + b
ln(1) = 0
0 = e + b
b = - e
line equation and answer.
y = e*x - e
