SOCR EduMaterials AnalysisActivities KolmogorovSmirnoff
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The Kolmogorov-Smirnoff test (KS-test) compares how distinct two datasets are. The KS-test has makes no assumption about the distribution of data, and therefore is a [http://en.wikipedia.org/wiki/Non-parametric_statistics non-parametric, or distribution-free, test]. Other [http://en.wikipedia.org/wiki/Parametric_statistics parametric tests], e.g., Student's t-test, Normal Z-test, etc., may be more [[AP_Statistics_Curriculum_2007_Hypothesis_Basics#Type_I_Error.2C_Type_II_Error_and_Power | sensitive] if the ''data meet the requirements'' of the test. | The Kolmogorov-Smirnoff test (KS-test) compares how distinct two datasets are. The KS-test has makes no assumption about the distribution of data, and therefore is a [http://en.wikipedia.org/wiki/Non-parametric_statistics non-parametric, or distribution-free, test]. Other [http://en.wikipedia.org/wiki/Parametric_statistics parametric tests], e.g., Student's t-test, Normal Z-test, etc., may be more [[AP_Statistics_Curriculum_2007_Hypothesis_Basics#Type_I_Error.2C_Type_II_Error_and_Power | sensitive] if the ''data meet the requirements'' of the test. | ||
| - | ===Examples== | + | ===Examples=== |
This example is based on a dataset from the '''R package''' under the library '''MASS'''. The dataset's name is '''oats'''. The quantitative variable '''Y''' (yields of oats field) is grouped by three varieties (the categorical variable here), '''Victory''', '''Golden.rain''' and '''Marvelous'''. | This example is based on a dataset from the '''R package''' under the library '''MASS'''. The dataset's name is '''oats'''. The quantitative variable '''Y''' (yields of oats field) is grouped by three varieties (the categorical variable here), '''Victory''', '''Golden.rain''' and '''Marvelous'''. | ||
Revision as of 19:36, 20 October 2008
Contents |
SOCR Analysis Activities - SOCR Analyses Example on Kolmogorov-Smirnoff Test
Overview of the Kolmogorov-Smirnoff Test
The Kolmogorov-Smirnoff test (KS-test) compares how distinct two datasets are. The KS-test has makes no assumption about the distribution of data, and therefore is a non-parametric, or distribution-free, test. Other parametric tests, e.g., Student's t-test, Normal Z-test, etc., may be more [[AP_Statistics_Curriculum_2007_Hypothesis_Basics#Type_I_Error.2C_Type_II_Error_and_Power | sensitive] if the data meet the requirements of the test.
Examples
This example is based on a dataset from the R package under the library MASS. The dataset's name is oats. The quantitative variable Y (yields of oats field) is grouped by three varieties (the categorical variable here), Victory, Golden.rain and Marvelous.
The analysis steps
- As you start the SOCR Analyses Applet, click on Kolmogorov-Smirnoff Test from the combo box in the left panel. Next, click on Example 4 and then the Data tab on the top of the right panel. You will see something like below. The data have been divided into 6 columns by groups.
- Click on the Mapping tab to map the groups you would like to include in the analysis. Select two groups.
- Now you will click on Compute to let the program produce the results. Click on Result to view the results.
- Click on Graph to view plots.
- Note: if you happen to click on the "Clear" button in the middle of the procedure, all the data will be cleared out. Simply start over from step 1.
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