Scatter Plot with Linear Regression
A scatter plot displays values for two variables within a dataset, with a line that best fits the slope of the data.
To begin the analysis, see Running the Analyses, then perform the following steps.
To perform a scatter plot with linear regression analysis:

Click the Advanced Workflow tab, then open the Analysis menu.

Select Scatter Plot with Linear Regression.
The Variable Selection section appears.

Define an independent variable and a dependent variable. Both variables should be continuous (for example, Age) and can be high dimensional data.

If you included high dimensional data in either variable box, click the High Dimensional Data button for that box.
The Compare SubsetsPathway Selection dialog box appears.
Specify the platform and other filters for the analysis.
For information, see High Dimensional Data.

Click Apply Selections.

Click Run.

Your analysis appears below:
Log_{10} Transformation
Often there will be a large spread between values in the xaxis of a scatter plot analysis. You can use the log_{10} option to transform the values in the xaxis, making the graph easier to analyze.
To use the log_{10} transformation:

Select the study you want to use and drag it into a Subset Definition box.

Select the Scatter Plot with Linear Regression analysis.

Enter the independent and dependent variables.

Check the box next to Perform log10 transformation on independent variable (below the Independent Variable box):

Click Run. Your analysis appears below:
Note
The difference between the xaxis on the scatter plot shown previously (no log_{10} transformation) and the graph shown immediately above. On the first graph, the xaxis values are plotted by multiple of 50 — 50, 100, 150. When the log_{10} transformation is applied, the xaxis values are plotted per much lower values — 3, 4, and

The Linear Regression Result values reflect the recalculated data.
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