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How we sent Field Notes into Space , or almostanyhow.


Six Books to Help You Around the House




3½" × 5½" 48 Pages Dot-Graph Paper
$ 19.95

Our 27thQUARTERLYEdition

The right tool for the job isn’t always drop-forged from chrome vanadium steel or plugged into a 220-volt outlet. Whether you’re rebuilding the engine in a 1967 Pontiac GTO or converting the home office into a nursery, any big project starts with careful planning. Step away from the power tools and collect your thoughts, specs, and calculations with the new Summer 2015 Field Notes Workshop Companion.

This 27th Quarterly Edition is a set of six books, boxed in a sturdy 60-pt custom slipcase with a sheet of crack-and-peel decals. Each of the books focuses on one DIY discipline Wood Working, Automotive, Gardening, Painting, Plumbing, and Electrical — each containing tips, reference materials and the usual Field Notes wise-cracking.


The six covers are color-coded to compliment six tones of 100-lb cover stock from the French Paper Company’s terrific new “Kraft-Tone” paper, their first new grade in five years. The 70-lb text Kraft-Tone “Standard White Kraft” body pages feature our dot-graph, and are bound with tough brass staples. Anyone fixing a switch, planting a bush, or painting a door jamb will find these books make a nice addition to their workbench, junk drawer or toolbox.


Proudly printed on a Heidelberg Speedmaster XL 105 40" 6-color printing press by the good people of eDOC Communications, Mount Prospect, Ill.

Covers: French Paper Company's brand-new Kraft-Tone 100#C "Index Off-White," "Manila Yellow," "Memo Orange," "Ledger Green," "Parcel Wrap," and "Chip Board" with a brute-force application of two soy-based Saphira inks.

Innards: French Kraft-Tone 70#T "Standard White Kraft," with a fine, 1-color application of "Maple Lacquer" light brown soy-based Saphira ink.

Packaged in a custom slipcase manufactured by Jessup Paper Box, Lafayette, Ind.

Bound with a Heidelberg Stitchmaster ST 270 5-pocket saddle stitcher with cover feeder/scorer Rima RS 10S in-line stacker.

Figure 2: At 5 percent, an SLRT using the delete-a-group jackknife variance resulted in a far lower rate of false positives than the sequential likelihood ratio test with independence assumption.

Since our initial testing, this staged rollout framework has proven indispensable to many feature deployments at Uber. It has been widely adopted by different product teams and has caught regressions caused by features at a low rollout stage, thereby reducing their impact on users.

One such regression occurred during a login feature rollout that led to an outage in one small population of users. A new feature that allows users to login using their phone numbers as opposed to their usernames was deployed in a single city as a staged rollout. When we deployed this new feature for app users in this geographic area, we noticed a drop in their trip rates; through some investigation, we found many users in the treatment group were not able to successfully sign in to the app using their phone numbers. Thankfully, our XP’s staged analysis functionality was able to detect this before it was deployed to all of our users in the country and prevent a full-blown outage.

As Uber continues to expand to new cities and grow its operations in existing ones, the need to deliver real-time experiment results has become more important than ever before. For instance, city operation management teams frequently use the XP to fine tune new features, including testing out new SMS text messages and tailoring the driver onboarding experience for each city.

Due to the difficulties of working in different time zones, it previously took around a few days for the city operation managers to collaborate with the XP team to run an analysis report following a rollout. With our new framework in place, we have been able to cut down on execution time by a magnitude of days, delivering the experiment results to city operation teams no matter their location with very short latency.

To optimize the speed of our analytics, we initially pre-computed the statistical value of our business metrics in . However, this method of computing statistical values did not afford our end users enough flexibility to customize the definitions of their metrics.

The new analysis tool does not pre-compute the data of the metrics, which cut down on our data storage spend and reduced our analysis generation time. Now, when the data is ready for analysis, we use a query file to generate metrics on the fly whenever people make a request on the WebUI. After that, we use Scala as our service engine to compute the probability ( SONOMA Goods for Life™ Arya Womens Mules lowest price s3Ic2rl
) that the treatment group mean is significantly different than the control group mean, determining if the experiment reached the target sample size.

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