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Ripple

Random (non-uniform) musings on big data, analytics, machine learning, data mining, and social graph analysis. Friday, February 8, 2013. Statistical Methods for Performance Engineers. Of a system (population) given a small glimpse (sample) of how it behaved at one time or another. Is to repeatedly re-sample the baseline data, calculating the mean of each sample. By the. Central Limit Theorem (CLT). Of the sample mean will approach a. Distribution as more and more samples are taken from the baseline data.

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Ripple | kalecoripple.blogspot.com Reviews

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Random (non-uniform) musings on big data, analytics, machine learning, data mining, and social graph analysis. Friday, February 8, 2013. Statistical Methods for Performance Engineers. Of a system (population) given a small glimpse (sample) of how it behaved at one time or another. Is to repeatedly re-sample the baseline data, calculating the mean of each sample. By the. Central Limit Theorem (CLT). Of the sample mean will approach a. Distribution as more and more samples are taken from the baseline data.

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1 ripple

2 mathjax

3 this so called

4 sampling distribution

5 gaussian

6 notes

7 systems

8 curl

9 notebook here

10 posted by

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Ripple | kalecoripple.blogspot.com Reviews

https://kalecoripple.blogspot.com

Random (non-uniform) musings on big data, analytics, machine learning, data mining, and social graph analysis. Friday, February 8, 2013. Statistical Methods for Performance Engineers. Of a system (population) given a small glimpse (sample) of how it behaved at one time or another. Is to repeatedly re-sample the baseline data, calculating the mean of each sample. By the. Central Limit Theorem (CLT). Of the sample mean will approach a. Distribution as more and more samples are taken from the baseline data.

INTERNAL PAGES

kalecoripple.blogspot.com

1

Ripple: Where did my Tweet go ? Tracking retweets through the social graph.

http://www.kalecoripple.blogspot.com/2012/10/where-did-my-tweet-go-tracking-retweets.html

Random (non-uniform) musings on big data, analytics, machine learning, data mining, and social graph analysis. Friday, October 12, 2012. Where did my Tweet go? Tracking retweets through the social graph. D3 visualization of a tweet propagation for @OReillyMedia. A simple method to follow the propagation of a tweet is to do a breadth-first traversal of followers. Script below performs this walk through the social graph. For economy against the Twitter API, the script caches follower. Lists in a Redis.

2

Ripple: September 2012

http://www.kalecoripple.blogspot.com/2012_09_01_archive.html

Random (non-uniform) musings on big data, analytics, machine learning, data mining, and social graph analysis. Friday, September 28, 2012. Web Request Rate Analysis with R. Output (.jtl files) but the concept can easily be extended to any format which includes timestamped entries, preferably labelled somehow to distinguish one service type from another. Epoch timestamp) and lb. Service label). The shell command below turns the jtl file into a format that R can read into a dataframe. This includes...Now w...

3

Ripple: October 2012

http://www.kalecoripple.blogspot.com/2012_10_01_archive.html

Random (non-uniform) musings on big data, analytics, machine learning, data mining, and social graph analysis. Friday, October 12, 2012. Where did my Tweet go? Tracking retweets through the social graph. D3 visualization of a tweet propagation for @OReillyMedia. A simple method to follow the propagation of a tweet is to do a breadth-first traversal of followers. Script below performs this walk through the social graph. For economy against the Twitter API, the script caches follower. Lists in a Redis.

4

Ripple: Statistical Methods for Performance Engineers

http://www.kalecoripple.blogspot.com/2013/02/statistical-methods-for-performance.html

Random (non-uniform) musings on big data, analytics, machine learning, data mining, and social graph analysis. Friday, February 8, 2013. Statistical Methods for Performance Engineers. Of a system (population) given a small glimpse (sample) of how it behaved at one time or another. Is to repeatedly re-sample the baseline data, calculating the mean of each sample. By the. Central Limit Theorem (CLT). Of the sample mean will approach a. Distribution as more and more samples are taken from the baseline data.

5

Ripple: Web Request Rate Analysis with R

http://www.kalecoripple.blogspot.com/2012/09/web-request-rate-analysis-with-r.html

Random (non-uniform) musings on big data, analytics, machine learning, data mining, and social graph analysis. Friday, September 28, 2012. Web Request Rate Analysis with R. Output (.jtl files) but the concept can easily be extended to any format which includes timestamped entries, preferably labelled somehow to distinguish one service type from another. Epoch timestamp) and lb. Service label). The shell command below turns the jtl file into a format that R can read into a dataframe. This includes...Now w...

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Ripple

Random (non-uniform) musings on big data, analytics, machine learning, data mining, and social graph analysis. Friday, February 8, 2013. Statistical Methods for Performance Engineers. Of a system (population) given a small glimpse (sample) of how it behaved at one time or another. Is to repeatedly re-sample the baseline data, calculating the mean of each sample. By the. Central Limit Theorem (CLT). Of the sample mean will approach a. Distribution as more and more samples are taken from the baseline data.

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