Occasionally, I need to find how closely two things are correlated. This generally isn't too difficult. I just plug a few vectors into my favorite statistical software and boom: correlations.

However, once the math is done, I need to communicate this information my intelligent, non-quantitatively oriented co-workers. They all "get" the concept of correlation, but if I say two variables have an r-squared of .57 (like here), they don't have any idea what that means.

Usually I say something like 'r-squared is a measure of how well variation in X explains variation in Y'. Which is true, but doesn't tell anyone if .57 is high or low.

So to give some context to these numbers, I put together this table of experimental r-squared values. Now I'll have real-world relationships between variables to compare to my findings. So in the example I linked to above (bounce rate vs difference in reporting), the r-squared of .57 tells me those variables are almost as closely related as an athlete's height and weight. So a pretty strong relationship.

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**Very Weak Relationships**

.07 - Job tenure and earnings (US 79-94) (source)

.11 - Height and earnings (US, meta-study) (source)

.14 - Fathers' & sons' IQs (Norway 60s-80s) (source)

**Weak Relationships**

.22 - Brothers' IQs (Sweden) (Bjorklund et al 2010)

.24 - Net worth and BMI (in young baby boomers) (Zagorsky 2004)

.30 - Parents' years of schooling and child's years of schooling (source)

.38 - A country's life expectancy and GDP per capita (source)

**Strong Relationships**

.42 - Twin brothers' IQs (Sweden, includes both fraternal & identical twins) (Bjorklund et al 2010)

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.49 - Parent and child wealth (US 68-99) (source)*

.51 - Death rate and % population without a diploma (US states) (source)

**Very Strong Relationships**

.61 - Athletes' height and weight (Olympic athletes) (source)

The normal caveats of r-squared apply: this doesn't give you any information about a model's bias, patterns, multicollinearity, non-significance, or data quality issues that might be leading you to an incorrect conclusion. But if you're comfortable with your model, this will hopefully help you explain how well it's working.

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If you have any other examples of r-squareds (with citations), please send to me or share below. I hope to add to this over time.

*These are sources where I'm a little confused what is going on: if anyone has corrections, please put them in the comments