Box plots, stem-and-leaf plots and normal Q-Q plots are important exploratory tools that allow you to visualize the distribution of your data when performing statistical analysis. This is crucial as it allows you to get a sense of the shape of your data's distribution and search for outliers that may threaten to invalidate your statistical tests. SPSS can generate all three of these plots from your data quickly and easily.
An Extreme Values table plots the highest and lowest cases for each variable, so you can visually inspect them to see whether their values are reasonable or whether they might derive from measurement error.
Open your data in SPSS. From the "Analyze" menu, select "Descriptive Statistics," then "Explore."
Select the variables from your data you'd like to explore, and click the left-pointing arrow to move each one over to the "Dependents" box (the one on the top right).
Click "OK." SPSS will generate a box plot, a stem-and-leaf plot, and two normal Q-Q plots (one detrended, the other not) of your data. You'll also see a table of descriptives, including several descriptive statistics that aren't available from the normal" Descriptives" window on the menu, such as the interquartile range, 5 percent trimmed mean, and 95 percent confidence interval for the mean.
- An Extreme Values table plots the highest and lowest cases for each variable, so you can visually inspect them to see whether their values are reasonable or whether they might derive from measurement error.
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