GeoDa menus, scatterlplotting matrix vs. standalone plot, Pearson's r vs. slope b

[Kevin Byrne]

My search no discussions yet on this, so here goes....

Attached: BivarScatterplotsMatrixQuestion.png

Lower left plot is circled by me in yellow.
Atop is 0.946**, circled in red

Q’s:
Q1: Wouldn’t the calc. atop typically be a display of (say) Pearson’s r?
Q2: What does the ** annotation mean? (I could guess but instead I’ll just ask.)

Re Q1: I know it in fact to be slope b, as that’s how it is labeled when I run Scatterplots Variables menu, attached: BivarScatterplotsUOF>ResClrQuestion.png

There is no calc. display atop BivarScatterplotsUOF>ResClrQuestion.png. (Everything is below the plot, due to need for detail presumably.)

Q3: Is it that in GeoDa's Scatterplot standalone displays parameter b by default instead of (say) Pearson’s r?

Thx, best, Kevin

[Julia Koschinsky]

Hi Kevin,

Q1+3: To get the Pearson correlation coefficient for two variables, right-click on the scatterplot and go to Data-View Standardized data -- then look at the slope.

G2: The * correspond to significance at 0.05* and 0.01** levels 

More info:

https://geodacenter.github.io/workbook/2a_eda/lab2a.html#bivariate-analysis-the-scatter-plot

Best,

Julia

Julia Koschinsky, Ph.D.

Executive Director

Center for Spatial Data Science

University of Chicago

https://spatial.uchicago.edu/

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Subject: [Openspace] GeoDa menus, scatterlplotting matrix vs. standalone plot, Pearson's r vs. slope b

 

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[MOKHTAR SAADA]

Hello everybody

Can you please tell me why the charts in the scatterplot matrix are not symmetrical? Shouldn't V1 X V2 be the same as V2 X V1?

I can't find an explanation in the manual. Only this paragraph that I can't explain:

"In general, however, while the scatter plot matrix shows the linear association between variables, the graphs are not symmetric. The main interest is in the magnitude and sign of the slope in each of the scatter plots, and the extent to which this points to a significant bivariate relationship, similar to the insight provided by a correlation matrix. "

Thank you to enlighten me.