![]() ToĬreate a frequency polygon with SPSS, click on Graphs | Line Right: SPSS: To create a histogram, click on Graphs | Histogram. It was condensed into class intervals) is shown in the figure to the A histogram of the age distribution data (before Histograms are bar charts that displayįrequencies or relative frequencies in the form of contiguous The investigator may choose to graphically review frequencies in The data from Table 1 can now be displayed as follows: Look at age distribution of children with ages grouped as preschool (2-4 years), elementary school (5-11-years), Statistics | Summarize | Frequencies command can be directed against the newly recoded variable.Īt times we might want to use nonuniform class-intervals when describing frequencies. After recoding the data to these new class intervals (ranges), a This willĪllow you to set up ranges to serve as class intervals. TOTAL 10 100% - SPSS: To group data in SPSS, click on Transform | Recode | Into Different Variable. The data set rounded to two significant digits is: with 15-year age class-interval grouping can In such instances, we first round the data to two significant digits. These data have 3 significant digits and a decimal point. Second Illustrative Example of a Stem-and-Leaf Plot: The next illustrative example shows how a stem-and-leaf plot canīe modified to accommodate data that might not immediately lend itself to this type of plot. With a little practice, a distribution's shape, location, and spread can be visualized through the stem-and-leaf plot. (Nothing is perfect in statistics, especially when the sample is small.) Also notice that the data demonstrate pretty-good symmetry around the mode. ![]() Particular data set in the interval 20 to 30. This peak represents the distribution's "mode." The mode of this Notice the "skyscraper" in the middle of the distribution. ![]() The shape of the distribution can be seen as a "skyline silhouette" of the data. The spread of the distribution is seen as the dispersion of values around the distribution's center. For example, the center of the above stem-and-leaf plot is located The location of the data can be summarized by its center. To flip the stem-and-leaf to a horizontal orientation to better display these features. For example, a value 21 isĭata are now sorted in approximate rank order, and the shape, location and spread of the distribution are evident. The rightmost digit of each data point (the "leaf") is then plotted against the stem-like axis. To start, draw a stem-like axis that extends from the data set's minimum to its maximum:Īn axis multiplier ( x 10) is included to allow the viewer to decipher the value of each data point. To illustrate stem-and-leaf plots, let us consider a data set with the following numerical values: (D) Place each leaf value adjacent to its associated stem value, one leaf on top of the other. The rightmost digit the leaf component consists of the rightmost digit. (C) Separate each data-point into a stem component and leaf component. (B) Round the data to two or three significant digits. To construct a stem-and-leaf plot: (A) Draw a stem-like axis that covers the range of potential values. The stem-and-leaf plot is an excellent way to start an analysis. � Raw Data � Uniform Class Intervals � Nonuniform Class Intervals 2: Stem-&Leaf Plots, Frequency Tables, and Histograms 2: Stem-&-Leaf Plots, Frequency Tables, and Histograms
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