Exploratory Data Anaylsis

    1
    Click on "Analyze." Select "Descriptive Statistics" followed by "Explore."
   
    2
    Drag and drop the columns containing the dependent variable data into the box labeled "Dependent List." Click "OK."

    3
    Remove any outliers identified by SPSS in the stem-and-leaf plots or box plots by deleting the individual data points. Alternatively, you can set up a filter to exclude these data points.

    4
    Select "Data" and then "Select Cases" and click on a condition that has outliers you wish to exclude. Determine a value for this condition that excludes only the outliers and none of the non-outlying data points.

    5
    Choose "If Condition is Satisfied" in the "Select" box and then click the "If" button just below it. Enter the rule to exclude outliers that you determined in the previous step into the box at the upper right. For example, if you were excluding measurements above 74.5 inches from the condition "height," you would enter "height < = 74.5." Click "Continue" and "OK" to activate the filter.

   

    Regression Analysis
        
        6
        In the "Analyze" menu, select "Regression" and then "Linear." Select the dependent and independent variables you want to analyze.

        7
        Click "Save" and then select "Cook's Distance." The values calculated for Cook's distance will be saved in your data file as variables labeled "COO-1."

        8
        Run a boxplot by selecting "Graphs" followed by "Boxplot." Click on "Simple" and select "Summaries of Separate Variables." Enter "COO-1" into the box labeled "Boxes Represent," and then enter an ID or name by which to identify the cases in the "Label Cases By" box.

        9
        Enlarge the boxplot in the output file by double-clicking it. Make a note of cases that lie beyond the black lines---these are your outliers. You may choose to remove all of the outliers or only the extreme outliers, which are marked by a star (*).

        10
        Go back into the data file and locate the cases that need to be erased. Working from the bottom up, highlight the number at the extreme left, in the gray column, so the the entire row is selected. Click on "Edit" and select "Clear." Repeat this step for each outlier you have identified from the boxplot.

非常感谢您
有点不是很明白，在我的分析中，只是想借助回归分析来删除异常值，并没有因变量和自变量之分，把哪个定义为因变量，有没有标准呢？

The researcher that means you who will do regression analysis must know which is the dependent variable that means depend on independent (predictors) variables.

我用验证性因素分析探讨的结构问题，只是用回归来删除各个变量中存在的异常值，不做回归分析。

非常感谢你