Contents
How do you determine if an outlier is influential?
With respect to regression, outliers are influential only if they have a big effect on the regression equation. Sometimes, outliers do not have big effects. For example, when the data set is very large, a single outlier may not have a big effect on the regression equation.
How do you find influential points?
A data point is influential if it unduly influences any part of a regression analysis, such as the predicted responses, the estimated slope coefficients, or the hypothesis test results.
What would give rise to an outlier?
One of the potential sources for outliers in our data are values that are not correct. There are different potential sources for these “incorrect values”. Two potential sources are missing data and errors in data entry or recording.
How do outliers affect the correlation coefficient?
Influence Outliers In most practical circumstances an outlier decreases the value of a correlation coefficient and weakens the regression relationship, but it’s also possible that in some circumstances an outlier may increase a correlation value and improve regression.
How does increasing the sample size affect the confidence interval?
Increasing the sample size decreases the width of confidence intervals, because it decreases the standard error. c) The statement, “the 95% confidence interval for the population mean is (350, 400)”, is equivalent to the statement, “there is a 95% probability that the population mean is between 350 and 400”.
What is the property of a 95% confidence interval?
All 95% confidence intervals have the property that they come from a procedure that has a 95% chance of yielding an interval that contains the true value. The confidence interval method automatically accounts for sample size in the standard error.
How to determine if a data point is influential?
An easy way to determine if the data point is influential is to find the best fitting line twice — once with the red data point included and once with the red data point excluded. The following plot illustrates the two best fitting lines:
Why does the standard error of B 1 increase when a data point is influential?
It is because the red data point is an outlier — in the y direction — that the standard error of b 1 increases, not because the data point is influential in any way. In each case, the P -value for testing H 0: β 1 = 0 is less than 0.001.