I don't know anything about mission statements, valuations, IPOs, or yoga. I do know a little about regression, though, and that regression line is pure bullshi

I don't know anything about mission statements, valuations, IPOs, or yoga. I do know a little about regression, though, and that regression line is pure bullshit. (Ironic, considering what they're purporting to analyze.) The downward trend is mostly due to a single high leverage[1] outlier[2] in the upper left. Take that out, and you still have a datset that not only exhibits heteroskedasticity[3] (much higher variance on the left than on the right) which invalidates many of the assumptions of ordinary least squares regression[4]. Even if it didn't, the slope is so mild, the sample size so small, and the residual variance so large that's there's no way you're going to be able to reject the hypothesis that the true slope is equal to zero[5]. I recommend you throw away the scatter plot and regression line and instead use Kendall's tau[6], a non-parametric statistic which considers only the rank order of each variable and is therefore appropriate for ordinal variables like the 0-9 "bullshit" scale. [1]: https://en.wikipedia.org/wiki/Leverage_(statistics) https://en.wikipedia.org/wiki/Leverage_(statistics) [2]: https://en.wikipedia.org/wiki/Outlier https://en.wikipedia.org/wiki/Outlier [3]: https://en.wikipedia.org/wiki/Heteroscedasticity https://en.wikipedia.org/wiki/Heteroscedasticity [4]: https://statisticsbyjim.com/regression/heteroscedasticity-regression/ https://statisticsbyjim.com/regression/heteroscedasticity-re... [5]: https://stats.stackexchange.com/questions/344006/understanding-t-test-for-linear-regression https://stats.stackexchange.com/questions/344006/understandi... [6]: https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient https://en.wikipedia.org/wiki/Kendall_rank_correlation_coeff...