Students are instructed on how to create the most common graphs in public administration research and data visualization.Correct data setup for each graph is illustrated through the use of datasets in the Companion Site.Graphs include bar graphs, histograms, line graphs, boxplots, and scatterplots.Steps to produce these graphs in R Commander are covered using public administration examples and datasets.

Chapter 2 covers univariate distributions and includes the following specific topics, among others: Frequency and Percent Distribution Tables, Bar Charts, Pie Charts, Stem-and-Leaf Displays, Histograms, Line Graphs, Shape of a Distribution, Cumulative Percent Distributions, Percentiles, Percentile Ranks, and Boxplots.

Chapter 2 covers univariate distributions and includes the following specific topics, among others: frequency and percent distribution tables, bar charts, pie charts, stem-and-leaf displays, histograms, line graphs, shape of a distribution, cumulative percent. Distributions, Percentiles, Percentile Ranks, and Boxplots.

This chapter reviews the computer simulation of simple lattice models for uniaxial and biaxial nematic systems. Beyond being interesting in their own right for understanding the orientational properties of LCs, these models, e.g. the Lebwohl–Lasher one, are computationally unexpensive in relative terms, and provide a useful test bed for developing techniques for studying LCs that can then be employed also for off-lattice and atomistic models. Here the investigation of the orientational phase transition, assessing its type, as well as the identification of topological defects and the calculation of DNMR spectra in bulk and confined nematics (droplets, films) are discussed.

The chapter on visual models discusses basic ways that scientists create visual representations of their data, including charts and graphs, in order to understand their data better. Like all models, visual models are a simplified version of reality. Two of the visual models discussed in this chapter are the frequency table and histogram. The histogram, in particular, is useful in the shape of the distribution of data, skewness, kurtosis, and the number of peaks. Other visual models in the social sciences include frequency polygons, bar graphs, stem-and-leaf plots, line graphs, pie charts, and scatterplots. All of these visual models help researchers understand their data in different ways, though none is perfect for all situations. Modern technology has resulted in the creation of new ways to visualize data. These methods are more complex, but they provide data analysts with new insights into their data. The incorporation of geographic data, animations, and interactive tools give people more options than ever existed in previous eras.

The last chapter using the dataset from chapter 6, this time the dataset is expanded using 80 thousand satellite images obtained from NASA for relative humidity density as it relates to vegetation. This image information is not visible information and thus pre-trained models are not useful for this problem. Instead, traditional computer vision techniques are showcased, involving histograms, local feature extraction, gradients and histograms of gradients. This domain exemplifies how to deal with high level of nuisance noise, how to normalize your features so that a small changes in the way the information was acquired does not completely throws off the underlining machine learning mechanism. These takeaways are valuable for anybody working with sensor data where the acquisition process has a high level of uncertainty. The domain also exemplifies working with large number of low-level sensor data.