September 9, 2016

Heteroskedasticity

Heteroskedasticity is a term used to describe a relationship between two variables which has unequal variance over the range. For example, the variance in the length of a cat's tail almost certainly changes (grows) with age. On the other hand, the average amount of chewing gum a person consume probably has a consistent variance over a wide range of human heights.

We also discuss some issues with the visualization shown in the tweet embedded below.

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Heteroskedasticity

\n

Heteroskedasticity is a term used to describe a relationship between two variables which has unequal variance over the range. For example, the variance in the length of a cat's tail almost certainly changes (grows) with age. On the other hand, the average amount of chewing gum a person consume probably has a consistent variance over a wide range of human heights.

\n

We also discuss some issues with the visualization shown in the tweet embedded below.

\n

More traffic tickets are issued per person in zip codes with lower average household incomes. [OC] … pic.twitter.com/Xtclzs7Lx8

— /r/DataIsBeautiful (@DataIsBeautiful) August 23, 2016
\n\n","pathname":"/blog/episodes/2016/heteroskedasticity","contributor":{"prettyname":"Kyle Polich","img":"https://s3.amazonaws.com/dataskeptic.com/contributors/kyle-polich.png","twitter":"@dataskeptic","linkedin":"https://www.linkedin.com/in/kyle-polich-5047193","bio":"Kyle studied computer science and focused on artificial intelligence in grad school. His general interests range from obvious areas like statistics, machine learning, data viz, and optimization to data provenance, data governance, econometrics, and metrology.","sort-rank":1}},"postLoading":false},"cart":{"invalid_submit":false,"cart_items":[],"paymentError":"","total":0,"country_long":"United States of America","invoice":{"submitDisabled":false,"paymentError":"","paymentComplete":false},"shipping":0,"stripeLoading":false,"go_to_checkout":false,"cart_visible":false,"country_short":"us","submitDisabled":false,"address":{"zip":"","city":"","phone":"","state":"","last_name":"","street_1":"","street_2":"","first_name":"","email":""},"prod":true,"focus":"first_name","token":null,"stripeLoadingError":false,"focus_msg":"","sizeSelected":{},"paymentComplete":false},"episodes":{"episodes":[],"loaded":false,"years":["2017","2016","2015","2014"],"focus_episode":{"episode":{"img":"https://static.libsyn.com/p/assets/7/6/5/3/7653f17b473c748b/heteroskedasticity.png","num":124,"guid":"3e131404de03f5bfd862747ee5399225","pubDate":"2016-09-09T15:00:00.000Z","mp3":"http://traffic.libsyn.com/dataskeptic/heteroskedasticity.mp3?dest-id=201630","desc":"

Heteroskedasticity is a term used to describe a relationship between two variables which has unequal variance over the range.  For example, the variance in the length of a cat's tail almost certainly changes (grows) with age.  On the other hand, the average amount of chewing gum a person consume probably has a consistent variance over a wide range of human heights.

We also discuss some issues with the visualization shown in the tweet embedded below.

\"Image

","duration":"08:57","title":"[MINI] Heteroskedasticity","link":"http://dataskeptic.com/epnotes/heteroskedasticity.php"},"loaded":1}},"advertise":{"card":"
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Thanks to Periscope Data for sponsoring this week's episode of Data Skeptic.

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Please visit https://www.periscopedata.com/skeptics
\n\t\tto learn more about what you can do with their tools.

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