Exploratory Data Analysis
1.3. EDA Techniques
1.3.3. Graphical Techniques: Alphabetic
Check for Shifts in Location and Scale and Outliers
Run sequence plots
are an easy way to graphically summarize a
univariate data set. A common assumption of univariate data
sets is that they behave like:
Last Third of Data Shows a Shift of Location
This sample run sequence plot of the MAVRO.DAT data set shows that the location shifts up for the last third of the data.
y(i) Versus i
Run sequence plots are formed by:
The run sequence plot can be used to answer the following
Check Univariate Assumptions
For univariate data, the default model is
Even for more complex models, the assumptions on the error term are still often the same. That is, a run sequence plot of the residuals (even from very complex models) is still vital for checking for outliers and for detecting shifts in location and scale.
|The run sequence plot is demonstrated in the Filter transmittance data case study.
|Run sequence plots are available in most general purpose statistical software programs.