A leaf is the least significant digit of the stem value. Each stem has a number of leaves, or data points. You construct stem-and-leaf displays by dividing the data set in to ranges, or stems. However, stem-and-leaf displays indicate every data point, which is useful for determining exact values and seeing the distribution of the data. Technically, stem-and-leaf displays are not plots because they are text-based. You can overcome this weakness in the boxplot by creating a stem-and-leaf display, shown as the right display in the previous figure. Although an evenly divided boxplot can give the impression of a normally distributed data set, the boxplot still suppresses the exact values of the data set. Data that is skewed upward or downward on the range has a boxplot in which the median does not divide the box evenly. This example program can be found in filepath \examples\Mathematics\Probability and Statistics\Exploratory Data Analysis.vi.Ä«oxplots are valuable because they quickly show the median, upper and lower quartiles, and potential outliers of the data set. The Exploratory Data Analysis VI example shows you how to use each of the statistical visualization techniques discussed below. This document provides information about each of these display types. For example, you can use LabVIEW to construct run sequence plots, scatter plots, boxplots, steam-and-leaf displays, histograms, lag plots, normal probability plots, and quantile-quantile plots. LabVIEW also has a powerful means of displaying data that allows it to address the visual nature of EDA statistical techniques. These numerical statistics involve straightforward computation, making them the basis for traditional statistical analysis. LabVIEW provides many VIs for several types of statistical analyses, such as mean and standard deviation. By highlighting patterns and trends, these plots provide an intuitive way to understand the core statistics of any data set. An entire branch of statistics, exploratory data analysis (EDA), evolved when mathematicians discovered they could use computers to create graphical plots of data quickly. Data visualization is becoming increasingly important to modern statistical analysis. This framework provides a way to see data so you do not have to rely on abstract numerical values. Statistical visualization is a useful framework for gaining valuable insight into data and for helping you choose technique for further analysis.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |