Wednesday, April 20, 2016

Innovative Thinking


IDEAS AND INSIGHT

Looking for some fresh ideas or new perspectives that you can use to improve your company’s performance? If so, we can help. Start by taking our quick “IQ” test to gauge your ability to innovate consistently. Then take a journey through the other resources here. And if that doesn’t help, give us a call or send us a email and we’d be glad to point you in the right direction…

BOOKS

We believe that some of the best life lessons were learned when we were young…
In Lessons from the Sandbox, learn to rediscover the gifts you had as a child and use them to improve corporate performance.
Learn more »

GREAT MOMENTS IN INNOVATIONS

Looking for a touch of inspiration from others who looked at the world with different eyes…
If so, here’s a timeline of some of our favorite innovations. Use it as a starting point for your own exploration of a world full of ideas, opportunities and possibilities
Learn more »

TESTING YOUR CORPORATE IQ

How innovative are you? How innovative can you become. Take the test…
We’ve designed a fun and straightforward “test” that provides a snapshot of your company’s ability to nurture the right new ideas.  It’s fast, easy to take, and doesn’t require any studying in advance!
Learn more »

IDEAS TO SPARK YOUR GENIUS

Looking to spark innovation and growth in your organization? We have a few resources to start you off…
We’ve outlined some of our favorite quick start activities and places around the web that offer plenty of insight nuggets. We’ll bet you can’t eat just one.
Learn more »

For the enlightened individuals, simply awesome site

http://www.socialresearchmethods.net/kb/index.php

Regression to the Mean


A regression threat, also known as a "regression artifact" or "regression to the mean" is a statistical phenomenon that occurs whenever you have a nonrandom sample from a population and two measures that are imperfectly correlated. The figure shows the regression to the mean phenomenon. The top part of the figure shows the pretest distribution for a population. Pretest scores are "normally" distributed, the frequency distribution looks like a "bell-shaped" curve. Assume that the sample for your study was selected exclusively from the low pretest scorers. You can see on the top part of the figure where their pretest mean is -- clearly, it is considerably below the population average. What would we predict the posttest to look like? First, let's assume that your program or treatment doesn't work at all (the "null" case). Our naive assumption would be that our sample would score just as badly on the posttest as they did on the pretest. But they don't! The bottom of the figure shows where the sample's posttest mean would have been without regression and where it actually is. In actuality, the sample's posttest mean wound up closer to the posttest population mean than their pretest mean was to the pretest population mean. In other words, the sample's mean appears to regress toward the mean of the population from pretest to posttest.

Why Does It Happen?

Let's start with a simple explanation and work from there. To see why regression to the mean happens, consider a concrete case. In your study you select the lowest 10% of the population based on their pretest score. What are the chances that on the posttest that exact group will once again constitute the lowest ten percent? Not likely. Most of them will probably be in the lowest ten percent on the posttest, but if even just a few are not, then their group's mean will have to be closer to the population's posttest than it was to the pretest. The same thing is true on the other end. If you select as your sample the highest ten percent pretest scorers, they aren't likely to be the highest ten percent on the posttest (even though most of them may be in the top ten percent). If even just a few score below the top ten percent on the posttest their group's posttest mean will have to be closer to the population posttest mean than to their pretest mean.
Here are a few things you need to know about the regression to the mean phenomenon:
  • It is a statistical phenomenon.
Regression toward the mean occurs for two reasons. First, it results because you asymmetrically sampled from the population. If you randomly sample from the population, you would observe (subject to random error) that the population and your sample have the same pretest average. Because the sample is already at the population mean on the pretest, it is impossible for them to regress towards the mean of the population any more!
  • It is a group phenomenon.
You cannot tell which way an individual's score will move based on the regression to the mean phenomenon. Even though the group's average will move toward the population's, some individuals in the group are likely to move in the other direction.
  • It happens between any two variables.
Here's a common research mistake. You run a program and don't find any overall group effect. So, you decide to look at those who did best on the posttest (your "success" stories!?) and see how much they gained over the pretest. You are selecting a group that is extremely high on the posttest. They won't likely all be the best on the pretest as well (although many of them will be). So, their pretest mean has to be closer to the population mean than their posttest one. You describe this nice "gain" and are almost ready to write up your results when someone suggests you look at your "failure" cases, the people who score worst on your posttest. When you check on how they were doing on the pretest you find that they weren't the worst scorers there. If they had been the worst scorers both times, you would have simply said that your program didn't have any effect on them. But now it looks worse than that -- it looks like your program actually made them worse relative to the population! What will you do? How will you ever get your grant renewed? Or your paper published? Or, heaven help you, how will you ever get tenured?
What you have to realize, is that the pattern of results I just described will happen anytime you measure two measures! It will happen forwards in time (i.e., from pretest to posttest). It will happen backwards in time (i.e., from posttest to pretest)! It will happen across measures collected at the same time (e.g., height and weight)! It will happen even if you don't give your program or treatment.
  • It is a relative phenomenon.
It has nothing to do with overall maturational trends. Notice in the figure above that I didn't bother labeling the x-axis in either the pretest or posttest distribution. It could be that everyone in the population gains 20 points (on average) between the pretest and the posttest. But regression to the mean would still be operating, even in that case. That is, the low scorers would, on average, be gaining more than the population gain of 20 points (and thus their mean would be closer to the population's).
  • You can have regression up or down.
If your sample consists of below-population-mean scorers, the regression to the mean will make it appear that they move up on the other measure. But if your sample consists of high scorers, their mean will appear to move down relative to the population. (Note that even if their mean increases, they could be losing ground to the population. So, if a high-pretest-scoring sample gains five points on the posttest while the overall sample gains 15, we would suspect regression to the mean as an alternative explanation [to our program] for that relatively low change).
  • The more extreme the sample group, the greater the regression to the mean.
If your sample differs from the population by only a little bit on the first measure, there won't be much regression to the mean because there isn't much room for them to regress -- they're already near the population mean. So, if you have a sample, even a nonrandom one, that is a pretty good subsample of the population, regression to the mean will be inconsequential (although it will be present). But if your sample is very extreme relative to the population (e.g., the lowest or highest x%), their mean is further from the population's and has more room to regress.
  • The less correlated the two variables, the greater the regression to the mean.
The other major factor that affects the amount of regression to the mean is the correlation between the two variables. If the two variables are perfectly correlated -- the highest scorer on one is the highest on the other, next highest on one is next highest on the other, and so on -- there will no be regression to the mean. But this is unlikely to ever occur in practice. We know from measurement theory that there is no such thing as "perfect" measurement -- all measurement is assumed (under the true score model) to have some random error in measurement. It is only when the measure has no random error -- is perfectly reliable -- that we can expect it will be able to correlate perfectly. Since that just doesn't happen in the real world, we have to assume that measures have some degree of unreliability, and that relationships between measures will not be perfect, and that there will appear to be regression to the mean between these two measures, given asymmetrically sampled subgroups.

The Formula for the Percent of Regression to the Mean

You can estimate exactly the percent of regression to the mean in any given situation. The formula is:
Prm = 100(1 - r)
where:
Prm = the percent of regression to the mean
r = the correlation between the two measures
Consider the following four cases:
  • if r = 1, there is no (i.e., 0%) regression to the mean
  • if r = .5, there is 50% regression to the mean
  • if r = .2, there is 80% regression to the mean
  • if r = 0, there is 100% regression to the mean
In the first case, the two variables are perfectly correlated and there is no regression to the mean. With a correlation of .5, the sampled group moves fifty percent of the distance from the no-regression point to the mean of the population. If the correlation is a small .20, the sample will regress 80% of the distance. And, if there is no correlation between the measures, the sample will "regress" all the way back to the population mean! It's worth thinking about what this last case means. With zero correlation, knowing a score on one measure gives you absolutely no information about the likely score for that person on the other measure. In that case, your best guess for how any person would perform on the second measure will be the mean of that second measure.

Estimating and Correcting Regression to the Mean


Given our percentage formula, for any given situation we can estimate the regression to the mean. All we need to know is the mean of the sample on the first measure the population mean on both measures, and the correlation between measures. Consider a simple example. Here, we'll assume that the pretest population mean is 50 and that we select a low-pretest scoring sample that has a mean of 30. To begin with, let's assume that we do not give any program or treatment (i.e., the null case) and that the population is not changing over time on the characteristic being measured (i.e., steady-state). Given this, we would predict that the population mean would be 50 and that the sample would get a posttest score of 30 if there was no regression to the mean. Now, assume that the correlation is .50 between the pretest and posttest for the population. Given our formula, we would expect that the sampled group would regress 50% of the distance from the no-regression point to the population mean, or 50% of the way from 30 to 50. In this case, we would observe a score of 40 for the sampled group, which would constitute a 10-point pseudo-effect or regression artifact.

Now, let's relax some of the initial assumptions. For instance, let's assume that between the pretest and posttest the population gained 15 points on average (and that this gain was uniform across the entire distribution, that is, the variance of the population stays the same across the two measurement occasions). In this case, a sample that had a pretest mean of 30 would be expected to get a posttest mean of 45 (i.e., 30+15) if there is no regression to the mean (i.e., r=1). But here, the correlation between pretest and posttest is .5 so we expect to see regression to the mean that covers 50% of the distance from the mean of 45 to the population posttest mean of 65. That is, we would observe a posttest average of 55 for our sample, again a pseudo-effect of 10 points.
Regression to the mean is one of the trickiest threats to validity. It is subtle in its effects, and even excellent researchers sometimes fail to catch a potential regression artifact. You might want to learn more about the regression to the mean phenomenon. One good way to do that would be to simulate the phenomenon. If you're not familiar with simulation, you can get a good introduction in the  The Simulation Book. If you already understand the basic idea of simulation, you can do a  manual (dice rolling) simulation of regression artifacts or a  computerized simulation of regression artifacts.

EQ Test

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Free EQ Quiz

Sunday, April 17, 2016

9 things mentally strong people do every day

strengths

Mental strength is just like any other skill: It takes time to develop.
In her book "13 Things Mentally Strong People Don't Do," psychotherapist Amy Morin writes that your genetics, personality, and life experiences all play a role in your mental strength.
Since we know what mentally strong people don't do, we asked Morin about the key habits they do follow.
Here are nine things mentally strong people do every day.
This is an update of an article originally written by Steven Benna.
1. They monitor their emotions.
1. They monitor their emotions.
People often assume mentally strong people suppress their emotions, Morin says, but they are actually acutely aware of them.

"They monitor their emotions throughout the day and recognize how their feelings influence their thoughts and behaviors," she says. "They know sometimes reaching their greatest potential requires them to behave contrary to how they feel."
2. They practice realistic optimism.
2. They practice optimism.
Having a positive outlook all the time is impossible, and too much negativity is counterproductive.
Mentally strong people "understand that their thoughts aren't always true, and they strive to reframe their negativity," Morin says. "They replace exaggeratedly negative thoughts with a more realistic inner monologue." 

3. They solve problems.

3. They solve problems.
To put it simply, "mentally strong people refuse to engage in unproductive activities," Morin says. Instead of sitting there complaining about your bad day at work and wishing bad things wouldn't happen, evaluate why something went wrong and fix it. Learn how to calculate risk and move forward from there, she says.
4. They practice self-compassion.

4. They practice self-compassion.
Rather than beating themselves up for making mistakes, mentally strong people practice self-compassion and speak to themselves as they would speak to a good friend, Morin says.
"They respond to their inner critic as if they were standing up to the schoolyard bully," she says. "They forgive themselves for mistakes and cheer themselves on as they work toward their goals."

5. They set healthy boundaries.

5. They set healthy boundaries.
One thing mentally strong people avoid is giving away their power. People give away their power when they lack physical and emotional boundaries, Morin says. They can establish healthy boundaries, however, by behaving assertively, she says. 
"They accept full responsibility for how they think, feel, and behave," she says, "and they refuse to let other people dictate whether they're going to have a good day or a bad day."

6. They manage their time wisely.

6. They manage their time wisely.
Mentally strong people describe time as a finite resource, Morin says. That's why they try to use it in a meaningful way. "Rather than waste energy dwelling on the past or resenting other people for taking up their time, they focus on more productive activities," she says.

7. They strive to fulfill their purpose.

7. They strive to fulfill their purpose.
Successfully fulfilling your purpose in life takes time. Mentally strong people understand this and focus on the big picture, keeping in mind that today's choices impact their future.

8. They seek to grow stronger.

8. They seek to grow stronger.
"Mentally strong people view everyday challenges as opportunities to grow stronger," Morin says. Additionally, they never settle or consider themselves strong enough. There is always room for improvement.
"They know that just like physically strong people need to work out to stay in good shape, they need to keep working out their mental muscles to prevent atrophy," she says.

9. They monitor their progress.

9. They monitor their progress.
Doing whatever it takes to improve can help you reach your greatest potential. It starts with acknowledging your weaknesses and having a "no excuses" approach.
"Rather than make excuses for their mistakes or failures, they seek explanations that will help them perform better moving forward," Morin says.

Fiber Optic Fusion Splicers and How They Work

What is a fiber optic fusion splicer?
A fiber optic fusion splicer is a device that uses an electric arc to melt two optical fibers together at their end faces, to form a single long fiber. The resulting joint, or fusion splice, permanently joins the two glass fibers end to end, so that optical light signals can pass from one fiber into the other with very little loss.

How does a fusion splicer work?
Before optical fibers can be successfully fusion-spliced, they need to be carefully stripped of their outer jackets and polymer coating, thoroughly cleaned, and then precisely cleaved to form smooth, perpendicular end faces. Once all of this has been completed, each fiber is placed into a holder in the splicer’s enclosure. From this point on, the fiber optic fusion splicer takes over the rest of the process, which involves 3 steps:

  • Alignment: Using small, precise motors, the fusion splicer makes minute adjustments to the fibers’ positions until they’re properly aligned, so the finished splice will be as seamless and attenuation-free as possible. During the alignment process, the fiber optic technician is able to view the fiber alignment, thanks to magnification by optical power meter, video camera, or viewing scope.

  • Impurity Burn-Off: Since the slightest trace of dust or other impurities can wreak havoc on a splice’s ability to transmit optical signals, you can never be too clean when it comes to fusion splicing. Even though fibers are hand-cleaned before being inserted into the splicing device, many fusion splicers incorporate an extra precautionary cleaning step into the process: prior to fusing, they generate a small spark between the fiber ends to burn off any remaining dust or moisture.

  • Fusion: After fibers have been properly positioned and any remaining moisture and dust have been burned off, it’s time to fuse the fibers ends together to form a permanent splice. The splicer emits a second, larger spark that melts the optical fiber end faces without causing the fibers’ cladding and molten glass core to run together (keeping the cladding and core separate is vital for a good splice – it minimizes optical loss). The melted fiber tips are then joined together, forming the final fusion splice. Estimated splice-loss tests are then performed, with most fiber fusion splices showing a typical optical loss of 0.1 dB or less.

With fiber optic components from industry leaders like Corning, Leviton, & Kingfisher, CableOrganizer.com is your online source for all things fiber optic. Check out our incredible selection of fiber optic patch cords, bulk cable, media converters, splice enclosuresconnectorstermination kits, consumables and testers.

©2016 CableOrganizer.com, LLC. This article may not be reproduced in part or in full without the written permission of CableOrganizer.com.
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Guide to Fiber Optics and Premise Cabling


FOA Guide Table Of Contents

FOA Guide - Table of Contents

This is the FOA's Guide To Fiber Optics & Premises Cabling. It includes almost a thousand pages of materials created by the FOA covering the basics to advanced topics on fiber optics and premises cabling.The goal of this website is educating students, users, designers, installers or anyone interested in the subject of fiber and cabling for communications systems.

Reference Topics

Directions For Using The FOA Guide

Jump To Reference Topics - click on any of the topic below to jump to the complete listing of topics on the subject in the FOA Guide.


Basic Topics

These are essentially the online versions of the FOA textbooks, complete with online topic quizzes, and you can purchase a printed or ebook version. Books 
Fiber Optics, The Basics( CFOT) 
  NEW Ahora en español! - Now in Spanish! 
  NEW  en français - Now in French too!

Premises Cabling Systems (CPCT) 
Outside Plant Fiber Optics (CFOS/O)  
Specific Topics
Applications of Fiber Optics
Fiber Optic Technology and Standards
Fiber Optic Components
Designing Fiber Optic Networks
Installation of FO Cable Plants
Testing & Troubleshooting Fiber Optic Systems
Using Fiber Optic Systems for System Owners and Operators
FOA Tech Bulletins  
FOA videos FOA YouTube Videos  

FOA Standards For Testing and Links

Self-Study Programs  

Resources For Teachers In K-12 And Technical Schools  

Additional References - FOA Books and Apps  

LINKS To Other Sources of Technical Information  

Linking And Use of The FOA Guide Materials   

Image result for Unicam termination kit pic

Directions For Using The FOA Guide
This is the FOA's Guide To Fiber Optics & Premises Cabling. It includes almost a thousand pages of materials created by the FOA covering the basics to advanced topics on fiber optics and premises cabling.The goal of this website is educating students, users, designers, installers or anyone interested in the subject of fiber and cabling for communications systems.

The Guide is intended to be used as reference material for those working in the industry, studying for FOA Certifications, teaching fiber optic training classes or giving refresher tutorials for FOA CFOTs. It includes what is essentially an online version of the FOA textbooks that you can also purchase a printed or ebook version. Books 

There is a tremendous amount of information on this website, so finding things can be a challenge. Here are some guidelines to make it easier.
If you want to look for a subject like standards, components or testing, go directly to the Table of Contents
If you want a self-study guide, go to  Fiber U.
If you are looking for a specific topic, we suggest you use our web site's Google Custom Search which will search just the FOA Online Reference Guide to find relevant materials.
This website will be constantly changing as new information is added. Suggestions regarding the content are welcomed. Send suggestions to  info@foa.org .
Printed, Kindle, iBook and iPad/iPhone App versions of the FOA textbooks are available. Details here.
Key To Using the Table of Contents
  • Some links are simple documents as webpages or PDF files formatted for printing, some are videos on YouTube and some are online PowerPoint presentations.
  •     Unmarked links are webpages
  •     The links marked (TT) are FOA Tech Topics From The FOA website.
  •     Links marked with the "YouTube" logo take you to the isted video on the FOA YouTube channel "thefoainc".
  •     The links marked VHO are "virtual hands-on" online PowerPoint presentations that provide a tutorial showing actual installation practices in a step-by-step process designed to lead the reader through a hands-on procedure exactly as it would be performed in the real world. VHO tutorials do not work well with portable web devices like iPhones.
  •     The links marked (Tutorial) are online PowerPoint presentations that provide a tutorial on the subject. Tutorials do not work well with portable web devices like iPhones.
  •     All links open in new windows, so you can close that window and return to the Table of Contents.
Sign up to receive the FOA eMail Newsletter where we keep you up to date on changes we're making in this site and the latest news in fiber optics.

Fiber Optics, The Basics (CFOT Level)  

NEW Ahora en español! - Now in Spanish!    


NEWLe Guide de référence pour la fibre optique de la FOA est maintenant disponible en français - Now in French too!.
Basics of Fiber Optics Home Page  
Basic overview   
The jargon and the technology
Fiber Optic Networks: basic applications and transmission systems 
Fiber Optic DatalinksFiber Optic Transceivers for Datalinks  
Optical Fiber
Fiber Optic Cables    VHO:   Cable preparation
Termination and splicing   Termination VHO:  Epoxy/Polish,  Anaerobic,   Hot Melt   Pre-Polished Splice termination  Singlemode termination    Splice VHO: Mech splice  Fusion: single fiber  ribbon
Fiber Optic Testing  VHO Insertion loss testing  Using an OTDR    
Network Design 
Installation
About Standards
Glossary of terms
FAQs on Fiber Optics  

See the "Fiber Optic Technology and Standards" Section below for information on networks, WDM, etc. 

FOA Video Lectures on FOA videos

FOA videos about cable preparation, termination, splicing and testing on FOA videos

*This is another website worthy of book marking. I've only shared a fragment of what this site has to offer in training as well as better means and practices.
 If you're an apprentice or still in a Bicsci class, this site is a must.

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