1. Exploratory Data Analysis 1.1. EDA Introduction 1.1.7. General Problem Categories
	The following table is a convenient way to classify EDA problems.
Problem Classification	UNIVARIATE Data: A single column of numbers, Y. Model: y = constant + error Output: 1) A number (the estimated constant in the model). 2) An estimate of uncertainty for the constant. 3) An estimate of the distribution for the error. Techniques: 4-Plot Probability Plot PPCC Plot CONTROL Data: A single column of numbers, Y. Model: y = constant + error Output: A yes or no to the question "Is the system out of control?". Techniques: Control Charts
	COMPARATIVE Data: A single respone variable, k independent variables (Y, X1, X2, ... ,Xk), primary focus is on one (the primary factor) of these independent variables. Model: y = f(x1, x2, ..., xk) + error Output: A yes or no to the question "Is the primary factor significant?". Techniques: Block Plot Scatter Plot Box Plot SCREENING Data: A single respone variable, k independent variables (Y, X1, X2, ... ,Xk). Model: y = f(x1, x2, ..., xk) + error Output: 1) A ranked list (from most important to least important) of factors. 2) Best settings for the factors. 3) A good model/prediction equation relating Y to the factors. Techniques: Block Plot Probability Plot Bihistogram
	OPTIMIZATION Data: A single respone variable, k independent variables (Y, X1, X2, ... ,Xk). Model: y = f(x1, x2, ..., xk) + error Output: Best settings for the factor variables. Techniques: Block Plot Least Squares Fitting Contour Plot REGRESSION Data: A single respone variable, k independent variables (Y, X1, X2, ... ,Xk). The independent variables can be continuous. Model: y = f(x1, x2, ..., xk) + error Output: A good model/prediction equation relating Y to the factors. Techniques: Least Squares Fitting Scatter Plot 6-Plot
	TIME SERIES Data: A column of time dependent numbers, Y. In addition, time is an indpendent variable. The time variable can be either explicit or implied. If the data are not equi-spaced, the time variable should be explicitly provided. Model: yt = f(t) + error The model can be either a time domain based or frequency domain based. Output: A good model/prediction equation relating Y to previous values of Y. Techniques: Autocorrelation Plot Spectrum Complex Demodulation Amplitude Plot Complex Demodulation Phase Plot ARIMA Models MULTIVARIATE Data: k factor variables (X1, X2, ... ,Xk). Model: Model not explicit. Output: Identify underlying correlation structure in the data. Techniques: Star Plot Profile Plot Principal Components Clustering Discrimination/Classification