Simpson's Paradox

A phenomenon that appears when a key variable is omitted from the analysis of a relationship between one or more independent variables and a dependent variable. For instance, this shows the more bedrooms houses have, the lower the home price:

which seems counter-intuitive, and is easily resolved by plotting all the data points that make up the average for each area, on the same graph. Here, the greater number of bedrooms correctly indicate pricier homes when also observing the neighborhood variable:

If you'd like to read more about the above example and get a far better explanation than I was able to provide, click here.

Simpson's Paradox

A phenomenon that appears when a key variable is omitted from the analysis of a relationship between one or more independent variables and a dependent variable. For instance, this shows the more bedrooms houses have, the lower the home price:

_{(source: ba762researchmethods at sites.google.com)}

which seems counter-intuitive, and is easily resolved by plotting all the data points that make up the average for each area, on the same graph. Here, the greater number of bedrooms correctly indicate pricier homes when also observing the neighborhood variable:

_{(source: ba762researchmethods at sites.google.com)}

If you'd like to read more about the above example and get a far better explanation than I was able to provide, click here.

Simpson's Paradox

A phenomenon that appears when a key variable is omitted from the analysis of a relationship between one or more independent variables and a dependent variable. For instance:

The above graph seems counter-intuitive, and is easily resolved by plotting all the data points that make up the average for each area, on the same graph:

If you'd like to read more about the above example and get a far better explanation than I was able to provide, click here.

Simpson's Paradox

A phenomenon that appears when a key variable is omitted from the analysis of a relationship between one or more independent variables and a dependent variable. For instance, this shows the more bedrooms houses have, the lower the home price:

which seems counter-intuitive, and is easily resolved by plotting all the data points that make up the average for each area, on the same graph. Here, the greater number of bedrooms correctly indicate pricier homes when also observing the neighborhood variable:

If you'd like to read more about the above example and get a far better explanation than I was able to provide, click here.