Please note that I normalized the ratings for "like" and "probability" (of getting yes) because the actual rating scale differs from person to person.
You subtract the mean from the data to center the data around zero and then divide by the standard deviation to normalize the value range.
This image is of a poster, and the copyright for it is most likely owned by either the publisher or the creator of the work depicted.
It is believed that the use of scaled-down, low-resolution images of posters qualifies as fair use under the copyright law of the United States.
Interesting article, but surely the data set would have been more useful to us if they’d been considerate enough to include “matlab programming” and “data analysis” in the interest category ratings. On a more serious note, for histograms with non-uniform bin widths it’s generally good practice to scale the areas rather than the heights, so perhaps the pdf normalisation option would be more appropriate. So far I have only one experience getting somewhat positive response from a lady by mentioning “MATLAB”, and I think she was an astrophysicist at MIT.
The second chart gives the impression that the mode of the % matches distribution is around 15 rather than 0.
There is no point in asking a person for a second date no matter how much you like him or her when you feel there's no chance of getting a yes.
You can see a fairly good correlation between Yes and No using those two factors.One possible reason we are not so good at judging our own attractiveness is that for majority of people it it in the eye of the beholder.If you plot to standard deviations of ratings people received, the spread is pretty wide, especially for men.If you can correctly guess the probability of getting a yes, that should help a lot.Can we make such a prediction using observable and discoverable factors only?Features were generated using normalization and other techniques - see using the Classification Learner app.