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JGEER.COM

John Geer

I build things with statistics, code, and prose. How to Tell When You Have Enough Data to Act. Statistical principles for decision making. How to Analyze Your Predictable Data: Anomaly Detection. Noticing unexpected observations among an ocean of expected ones. Featured on DataScienceCentral.com and @analyticbridge. Customer Retention with Statistical Learning. Predicting if customers plan to leave and finding the best intervention. Using Data Science to Estimate Annual Cycles in Housing. I have a master...

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I build things with statistics, code, and prose. How to Tell When You Have Enough Data to Act. Statistical principles for decision making. How to Analyze Your Predictable Data: Anomaly Detection. Noticing unexpected observations among an ocean of expected ones. Featured on DataScienceCentral.com and @analyticbridge. Customer Retention with Statistical Learning. Predicting if customers plan to leave and finding the best intervention. Using Data Science to Estimate Annual Cycles in Housing. I have a master...
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John Geer | jgeer.com Reviews

https://jgeer.com

I build things with statistics, code, and prose. How to Tell When You Have Enough Data to Act. Statistical principles for decision making. How to Analyze Your Predictable Data: Anomaly Detection. Noticing unexpected observations among an ocean of expected ones. Featured on DataScienceCentral.com and @analyticbridge. Customer Retention with Statistical Learning. Predicting if customers plan to leave and finding the best intervention. Using Data Science to Estimate Annual Cycles in Housing. I have a master...

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Customer Retention with Statistical Learning | John Geer

https://jgeer.com/customer-retention-with-statistical-learning

Customer Retention with Statistical Learning. March 26, 2014. Machine learning can be a valuable tool for keeping customers. We can use statistical learning methods to predict which customers are about to leave. However, we shouldn’t stop there. We can also use statistical learning methods to find the responses which best encourage customers to stay. Predicting When Customers Are About to Leave. The difference between a customer leaving or staying could be a simple email at just the right time. Unfortuna...

2

Using Data Science to Estimate Annual Cycles in Housing | John Geer

https://jgeer.com/time-series-analysis-of-interest-in-homes-for-sale

Using Data Science to Estimate Annual Cycles in Housing. September 15, 2013. Home prices follow repeating annual patterns. By analyzing search volume and home prices we can better understand, and possibly predict, these patterns. The seasonality of people’s interest in homes for sale should also be reflected in the prices they are willing to pay for a home. To investigate this connection, let’s also look at home price data collected by the US Federal Housing Finance Agency. With the help of R and some ti...

3

A Better Way to Rank a Huge Number of Things | John Geer

https://jgeer.com/a-better-way-to-rank

A Better Way to Rank a Huge Number of Things. May 5, 2013. Vote-based news sites are biased towards posts that are already well ranked. As a result, they often miss good posts with just a few votes. We should use statistics to focus votes on the posts that we know the least about. Problem: Blind Spot Caused by Limited Resources. This focus of voters on highly-ranked posts creates another, more straight-forward, problem. Votes aren’t where they are most needed: on the posts we don’t know a...Be good. ...

4

How to Analyze Your Predictable Data: Anomaly Detection | John Geer

https://jgeer.com/anomaly-detection-how-to-analyze-your-predictable-data

How to Analyze Your Predictable Data: Anomaly Detection. April 26, 2015. There is predictable data as far as the eye can see. Millions of variables quietly tracing the path we thought, and perhaps hoped, they would. Because there are so many, noticing when one of these variables does something unexpected is a task that is unsolvable by diligence alone. In order to spot these rare unexpected observations, we need an often-overlooked statistical analysis: anomaly detection. The ability to notice unexpected...

5

Better Estimation When Perfection Is Unlikely: A Bayesian Example | John Geer

https://jgeer.com/better-estimation-when-perfection-is-unlikely

Better Estimation When Perfection Is Unlikely: A Bayesian Example. May 27, 2013. The simple estimator ( frac{1 textrm{ Number Successes} {2 textrm{ Number Attempts} ) is better than ( frac{ textrm{Number Successes} { textrm{Number Attempts} ) when “success” is unlikely to happen 0% or 100% of the time. Such situations are often the most interesting and worthy of measurement. Frac{ textrm{Number Successes} { textrm{Number Attempts} $. Let’s try using Bayesian statistics to find our better estimate. Begin{...

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How the Budget Changes

http://explainwell.com/budget

All Together Now I wonder why Medicare, Justice, and Health have moved together, but only somewhat with the group. More and Less Check out National Defense, it completely changes direction depending on how you measure it. Possible Political Influence Who knew the party of the President isn't significantly correlated with Social Security? Mixed Responsibility Medicare went up under both parties control. Also, check out National Defense, almost the same change under Republican and Democratic presidents.

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I build things with statistics, code, and prose. How to Tell When You Have Enough Data to Act. Statistical principles for decision making. How to Analyze Your Predictable Data: Anomaly Detection. Noticing unexpected observations among an ocean of expected ones. Featured on DataScienceCentral.com and @analyticbridge. Customer Retention with Statistical Learning. Predicting if customers plan to leave and finding the best intervention. Using Data Science to Estimate Annual Cycles in Housing. I have a master...

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