Sentences with phrase «used wrong data»

He did, however, respond on Twitter a number of times over the next couple days without ever acknowledging he had used the wrong data set.

He knows he used the wrong data set, he continued to talk about the topic, but for whatever reason, he chose not to say anything about his error.

After he updated his post (without me realizing the extent of the change), I wrote several comments at his site (and more on Twitter, to which responded) pointing out his code clearly showed he used the wrong data set.

When I pointed out his code proved he had used the wrong data set, he promptly stopped responding.

JonA, Sven, there is no doubt Steve McIntyre used the wrong data set.

But still, I think the most important point at this moment is this post was written based upon Steve having used the wrong data set.

If half your inbound leads from online efforts actually come via the phone, but you only acknowledge «completed web forms,» etc., you are kidding yourself about existing conversion rates, and probably using the wrong data to optimize your spending.

You did per capita wrong even with using the wrong data is is the abortion number / population number not the other way around, then multiplied by 100k to get rid of the number.

Perhaps you were using the wrong data or method?

--------------------------- Perhaps you were using the wrong data or method?

Using the wrong data from the airport makes the warming much weaker than the real data tells — even after the UHI - adjustments are made.

Not exact matches

In the same way that a chef gets frustrated working with someone else's dull knives, data scientists will become equally frustrated if forced to use the wrong tools for the job.

Ursula Adams, director of employee engagement at United Way for Southeastern Michigan, says using the daily - engagement app Niko Niko — which tracks employees» mood data with its mobile «happiness meter» — has helped her avoid sinking money into fixing the wrong cultural problems.

But both use political narrative for data so both are wrong on forecasts and long on hyperbole.

Automated tools developed for criminal sentencing and policing, for instance, have given old wrongs new life by using flawed data to create their models, resulting in a propensity to overstate the dangerousness of black people.

I think this data is «bad», the students are «wrong», the scence is «awful» and they should «think» and use «reason» as they «question» the ideas behond the tweets.

You have to admit though — everyone believes something and I using the word believe to mean not fully able to know all knowledge about everything in the world, and so we guess based on the knowledge of what we know, and assume we are right until some other data comes into our life to prove us wrong.

They may occur because: there is something wrong with the instrument or its data handling system, or because the instrument is wrongly used by the experimenter.

Just because the manufacturer doesn't want to spend the money on the research needed to apply for licences for those indications doesn't mean that doctors are doing anything wrong by using it for those indications, when the experiential data is that it is both safe and effective for IOL and PPH.

«Assuming you can't use big data to improve public health is simply wrong,» added Ayers.

In the same critical spirit, The Engine Room, a network - based organisation exploring political, social and other non-academic uses of data, ran a session on when open data goes wrong.

Such clarification would be useful because the industry data appear to be full of potential faults, including, in the analysis of one dispersant, the use of the wrong reference toxicant.

Using simple statistics, without data about published research, Ioannidis argued that the results of large, randomized clinical trials — the gold standard of human research — were likely to be wrong 15 percent of the time and smaller, less rigorous studies are likely to fare even worse.

Researchers created clinical scenarios related to four common pitfalls of EDIS use in emergency departments: communication failure, poor data display, wrong order / wrong patient errors and alert fatigue.

«If you don't use the right data,» Tang says, «whatever model you develop and apply, no matter how innovative, is likely to give you the wrong conclusions.»

Every cellphone company, and even many home Internet providers, are imposing limits on the amount of data we use each month — at precisely the wrong time in technological history.

Consider the odds that various international scientists using quite different data and quite different data analysis techniques can all be wrong in the same way.

The site has a clear and transparent privacy policy and you can rest assured that your particulars will not be used for the wrong purpose and your private and sensitive data will not be stolen and put to wrong use.

If you do not use the right methods to analyze your data, then your intuitions and decisions may very well be completely wrong.

Rather than using data to create a laundry list of «what's going wrong with our schools» or to assign blame to a group or individual, it is more effective to look at equity - related data with the goal of building capacity for improvement.

Unthinking use of this sort of data can lead teachers to spend time either worrying about or working with the «wrong» students.

Well, I've read the comments for a few months now, and it's incredible to see how yourself have commented on almost everything, talking trash on everything, hating everything, (using punctuation the wrong way, as demonstrated just above), putting up facts about losses (and I'm not talking about Tesla) that everybody is supposedly making without any real data to support your claims (except you just have to open your eyes, son - thanks, now I can see...).

With wrong information flying everywhere these days it would be best to not take this information as the gospel truth but the kernels of data within the screenshot would put a smile on the faces of those of you who use Verizon's HTC Rezound, Motorola Droid RAZR and RAZR Maxx or Motorola XOOM 3G / 4G tablets.

Further to my point that if your valuation models use forward estimates rather than twelve - month trailing data, you're doing it wrong, here are the results of our Quantitative Value backtest on the...

Further to my point that if your valuation models use forward estimates rather than twelve - month trailing data, you're doing it wrong, here are the results of our Quantitative Value backtest on the use of consensus Institutional Brokers» Estimate System (I / B / E / S) earnings forecasts of EPS for the fiscal year (available 1982 through 2010) for individual stock selection:

If your valuation models use forward estimates rather than twelve - month trailing data, you're doing it wrong.

By using completely segregated databases, TrackStar ™ protects your data (your customers» sensitive private information) from falling into the wrong hands.

Now, there's nothing wrong with making mistakes when pursuing an innovative observational method, but Spencer and Christy sat by for most of a decade allowing — indeed encouraging — the use of their data set as an icon for global warming skeptics.

Mal Adapted @ 40 When alarmist language is used about our sea levels and local topographic data says otherwise and anybody who disputes it is called a denier then something has gone terribly wrong.

If you are going to criticize his graph because he doesn't use 2009 and 2010 data, then you should tell us what you expect temperatures in those years to be, so we can revisit the issue should your expectations be wrong.

When alarmist language is used about our sea levels and local topographic data says otherwise and anybody who disputes it is called a denier then something has gone terribly wrong.

You are wrong — and part of why you are wrong is that you wrongly think that cause and effect relationships must create real world data that forms graphs of one - to - one or even just monotonic functions, even after smoothing techniques are used such as running means.

In fact, this overweening clamor for raw data seems to miss the obvious point that if Mann or Briffa or the legions of others working in this arena are so wrong in their conclusions, it should be an easy task to disprove their claims using various experiments entirely independent of the data in question.

I would now hope that Rob will go to WUWT and CA and explain to people that trying to use tree ring data without any kind of selection methodology to weed out those which don't appear to be mostly responding to temperature changes is wrong, wrong, wrong.

Moreover, ACRIM composite uses the data as they are published, while the other two composites alter the data with hypothetical models that might have large errors and might be wrong.

There is nothing «wrong» with the models; they are always growing and improving, but are not in any sense broken, nor are they the «doing» of science in the sense so many deniers try to falsely claim, but the result of having done science and using that data to make educated guesses about the future.

Beran is wrong: he is using seasonal data (in this case, monthly), and not accounting for that.

What is known for a certainty at this point is that the existing models are wrong — because they failed to accurately predict the data which have now been observed — and they are alarmist — because when globalist bureaucrats use faulty models as their justification for confiscating trillions of dollars from those who have earned them, and giving them to those who did not.

In it, they acknowledged one wrong date and the use of some tree - ring data that hadn't been cited in the original paper, and they offered some new details of the statistical methods.

This is not unimportant because temperature is used in planning climate policy.To avoid errors from this, use satellite data whenever available.This should be enough to explain why their baseline is wrong.