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The residuals in regression estimation are estimates of error. Most commonly, the errors are assumed to be statistically independent, identically distributed and normally distributed, that is, to have a Gaussian distribution.
If these were the assumptions under which the regression was calculated then the residuals could (at least potentially) be examined for any departures from the assumptions. Usually they are plotted against the independent variable to see if there is any systematic relationship between the two sets of values. The residuals might also be tested for normality.
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What is the residual for the point (36)?
A residual is defined in the context of some "expected" value. There is no information in the question regarding expected values.
How can you interpret the values of the slope?
The values of the slope of a line is a measure of the amount of change in the dependent (vertical) variable which accompanies a unit change in the ndependent (horizontal) variable.
When you use your regression line to predict values that are not in the sample and discovered large residual values in each prediction what can you say about your regression line?
There are several possible explanations: Leaving aside the two most obvious reasons: calculation error and attempted extrapolation, there are the following possibilities: The true relationship is non-linear. A relevant variable has been missed omitted. The observations are very variable: leading to a very large residual error. There is not enough variation in the independent (or predictive) variable so that Sxx is very small.
How do you interpret z-test result?
interpret it by letters...........
How do you work with interpret and prepare numerical data?
Understanding and interpret numerical data
