« This headline writer has the secret to everlasting life | Main | Counting is hard, especially when you don't have theories »


Feed You can follow this conversation by subscribing to the comment feed for this post.

Chris K

I know this is all for fun, but let's remember that for a given movie there are actually multiple posters. There are pre-release "teaser" posters as well as different versions for different markets.


Chris: you beat me to it. i have a comment on this in my next post.


This is a good example of how it is essential to understand the data. I remember a colleagues data from some years ago, a time series of pollution data. When plotted there was a consistent pattern of missing data, which resulted from them removing observations which were outside the calibration range of the sensor. One way of getting the average down.


Isn't this true of much of the data that would be used in climate models ? Historical data either wasn't measured(if you go back far enough) or was measured very differently(for more recent data). I know you can use ice cores and tree rings, etc to get a picture of what the climate was like before we started keeping records, but that is all computed or modeled data, right ? How have climate scientists addressed this? Have they used ice cores and tree rings from the last 30 years to calibrate their models of how that data corresponds to global temperature? No matter how carefully they have, the resulting historical data can't be anything other than an estimation, a range of what the temperature might have been? So, then when you try to build a climate model using that data do you effectively randomly sample your confidence interval of what the temps were at any particular point in time? Or do they just take the mean or median value of the interval and use that as THE value ? Just wondering....

Verify your Comment

Previewing your Comment

This is only a preview. Your comment has not yet been posted.

Your comment could not be posted. Error type:
Your comment has been posted. Post another comment

The letters and numbers you entered did not match the image. Please try again.

As a final step before posting your comment, enter the letters and numbers you see in the image below. This prevents automated programs from posting comments.

Having trouble reading this image? View an alternate.


Post a comment

Your Information

(Name is required. Email address will not be displayed with the comment.)


Link to Principal Analytics Prep

See our curriculum, instructors. Apply.
Business analytics and data visualization expert. Author and Speaker. Founder of Principal Analytics Prep, MS Applied Analytics at Columbia. See my full bio.

Next Events

Oct: 31 Webinar on Data Visualization, online at JMP

Nov: 1 NYU unCOMMON Salon Public Lecture, New York, NY

Nov: 8 Tufts Gordon Institute: A Conversation with Kaiser Fung, Facebook Live

Nov: 8 Tufts TGI Careers & Networking Night panel, Somerville, MA

Nov: 26 Data Visualization New York Meetup, New York, NY

Nov: 27 NYPL Data Analytics Resume Workshop, New York, NY

Nov: 30 Purdue School of Engineering Seminar, West Lafayette, IN

Dec: 1 Purdue Mathematics, Data Science, and Industry Conference, West Lafayette, IN

Past Events

See here

Future Courses (New York)

Summer: Statistical Reasoning & Numbersense, Principal Analytics Prep (4 weeks)

Summer: Applied Analytics Frameworks & Methods, Columbia (6 weeks)

Junk Charts Blog

Link to junkcharts

Graphics design by Amanda Lee


  • only in Big Data