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Which one of the following can be used to detect heteroscedasticity in model residuals?
Correct! The Durbin Watson test is one for detecting residual autocorrelation, but it is designed to pick up first order autocorrelation (that is, a statistically significant relationship between a residual and the residual one period ago).
What are the formal and informal methods of detecting heteroscedasticity?
Detecting Heteroskedasticity There are two ways in general. The first is the informal way which is done through graphs and therefore we call it the graphical method. The second is through formal tests for heteroskedasticity, like the following ones: The Breusch-Pagan LM Test.
How to check for heteroscedasticity in regression plots?
Heteroscedasticity produces a distinctive fan or cone shape in residual plots. To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically. Typically, the telltale pattern for heteroscedasticity is that as the fitted values increases, the variance of the residuals also increases.
How to check for heteroskedasticity with graphed residuals?
Then you can construct a scatter diagram with the chosen independent variable and the squared residuals from your OLS regression. The following figure illustrates the typical pattern of the residuals if the error term is homoskedastic.
Which is the best way to detect heteroscedasticity?
The simplest way to detect heteroscedasticity is with a fitted value vs. residual plot. Once you fit a regression line to a set of data, you can then create a scatterplot that shows the fitted values of the model vs. the residuals of those fitted values.
How is the accuracy of a residual plot determined?
In the plot on the right, each point is one day, where the prediction made by the model is on the x-axis and the accuracy of the prediction is on the y-axis. The distance from the line at 0 is how bad the prediction was for that value.