1.
Exploratory Data Analysis
1.3. EDA Techniques 1.3.3. Graphical Techniques: Alphabetic


Purpose: Check for Shifts in Location and Scale and Outliers 
Run sequence plots
(Chambers 1983)
are an easy way to graphically summarize a
univariate data set. A common assumption of univariate data
sets is that they behave like:


Sample Plot: 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. 

Definition: y(i) Versus i 
Run sequence plots are formed by:


Questions 
The run sequence plot can be used to answer the following
questions


Importance: 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. 

Related Techniques 
Scatter Plot Histogram Autocorrelation Plot Lag Plot 

Case Study  The run sequence plot is demonstrated in the Filter transmittance data case study.  
Software  Run sequence plots are available in most general purpose statistical software programs. 