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What is the S value in linear regression?

What is the S value in linear regression?

S represents the average distance that the observed values fall from the regression line. Conveniently, it tells you how wrong the regression model is on average using the units of the response variable. Smaller values are better because it indicates that the observations are closer to the fitted line.

How do you find S in linear regression?

S(errors) = (SQRT(1 minus R-squared)) x STDEV. 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.

What is predicted value in linear regression?

We can use the regression line to predict values of Y given values of X. For any given value of X, we go straight up to the line, and then move horizontally to the left to find the value of Y. The predicted value of Y is called the predicted value of Y, and is denoted Y’.

What does an R-squared value of 0.9 mean?

What does an R-squared value of 0.9 mean? Essentially, an R-Squared value of 0.9 would indicate that 90% of the variance of the dependent variable being studied is explained by the variance of the independent variable.

How are recovered values represented in linearity analysis?

Displays your individual recovered values for each linear dilution level (effective X value) versus your calculated target values. Your recovered values are represented by the blue squares and the target values are represented by the Xs on the graph. The Xs may not be visible if recovered values are close to target values.

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When to use least squares fit in linearity analysis?

First, simple linear regression, ‘least squares fit’, is performed on two or three consecutive levels of recovered values to calculate the target values. Appropriate equal delta values are input as the ‘x’ values and recovered values for the levels are used as ‘y’ values to calculate a regression equation.

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