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These are a convenient way of observing the relationship between two variables, or visualizing OLS regressions. They are especially useful when working with large datasets. The Examples section of the help file contains a clickable walk-through of binscatter’s various features. Introductory Slide Deck This slide deck provides a thorough introduction to binscatter. It explains: Technical Description Binned scatterplots are a non-parametric method of plotting the conditional expectation function which describes the average y-value for each x-value.

To generate a binned scatterplot, binscatter groups the x-axis variable into equal-sized bins, computes the mean of the x-axis and y-axis variables within each bin, then creates a scatterplot of these data points. By default, binscatter also plots a linear fit line using OLS, which represents the best linear approximation to the conditional expectation function.

All procedures in binscatter are optimized for speed in large datasets. Example The following graph shows the relationship between quality of teaching in elementary or middle school and a student’s earnings at age This graph is a visual representation of a multivariate regression with , observations. The graph was created using binscatter: Note that this is the first step of a partitioned regression. We could regress the y-residuals on the x-residuals and obtain the coefficient from the full multivariate regression.

Each dot shows the average “Earnings at Age 28” for a given level of “Teacher Value Added”, holding the controls constant. Finally, binscatter plotted the best linear fit line, constructed from an OLS regession of the y-residuals on the x-residuals. The slope of the fit line matches the coefficient of the multivariate regression. Source of Example: Raj Chetty, John N. Friedman, and Jonah E.

Acknowledgements binscatter is based on a program first written by Jessica Laird. This program was developed under the guidance and direction of Raj Chetty and John Friedman.

Laszlo Sandor provided suggestions which improved the program considerably, and offered abundant help testing it. Thank you also to the users of early versions of the program who devoted time to reporting the bugs that they encountered.

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