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.
They are building blocks of geometry
The confidence interval is based on
mean square error. T<span>he </span>mean squared error<span> (</span>MSE<span>) </span><span>of an </span>estimator<span> measures the </span>average<span> of the squares of the </span>errors<span> or </span>deviations.<span> MSE is calculated by the formula attached in the picture, where Xbar is a vector of predictions, X is the vector of predicted values. </span>
Triangle, not a triangle, not a triangle, triangle
Determining if three side lengths can make a triangle is easier than it looks. All you have to do is use the Triangle Inequality Theorem, which states that the sum of two side lengths of a triangle is always greater than the third side. If this is true for all three combinations of added side lengths, then you will have a triangle.[
Answer: It would be d,15m
