Lesson 3: Graphical Display of Multivariate Data
An important step in the analysis of any dataset is Exploratory Data Analysis (EDA), including the graphical display of data.
Why do we look at graphical displays of the data? Graphical displays may:
- suggest a plausible model for the data,
- assess validity of model assumptions,
- detect outliers, or
- suggest plausible normalizing transformations
Many multivariate methods assume that the data have a multivariate normal distribution. Exploratory data analysis through the graphical display of data may be used to assess the normality of data. If evidence is found that the data are not normally distributed, then graphical methods may be applied to determine appropriate normalizing transformations for the data.
In this course we will use SAS and Minitab to demonstarte graphical methods as well as for other applications later. Both SAS and Minitab diagrams are provided side-by-side as far as possible. If diagrams require extensive instructions, tabs are provided separately for SAS and Minitab.
Learning Objectives & Outcomes
The objectives of this lesson are:
- Introduce graphical methods for summarizing multivariate data including histograms, scatterplot matrices, and rotating 3-dimensional scatterplots;
- Produce graphics using interactive data analysis in SAS and Minitab;
- Understand when transformations of the data should be applied and what specific transformations should be considered;
- Learn how to identify unusual observations (outliers), and understand issues regarding how outliers should be handled if they are detected.