Sentences with phrase «when simple data»

Actually, in Dr Mann's case, it's not easy when simple data handling and statistical modeling is your day job - hence, all his problems with California bristlecones, double - counted lone Gaspà © cedars, upside - down Finnish lake sediments, transposed eastern and western hemispheres, invented statistical methods, truncated late 20th - century tree - rings, etc, etc..

They're using spreadsheets to collect data when simple data integration tools would do that automatically.

Not exact matches

It sounds simple, but the five Ws (who, what, when, where, why) are a great starting point for questioning your data.

Plasticity, which charges clients $ 3 to $ 5 per employee per month for use of its platform, begins its workplace data collection with a simple question every user answers when they sign in: «How happy are you today?»

The basic idea is simple: Equifax and the other credit reporting agencies don't pay you when they sell your data.

When a customer comes to you on live chat with a bug fix or a product suggestion, your agents can use a simple command to initiate a data transfer.

When sales reps have data at their fingertips, finding prospects that match their ideal customer profile and are within their territory becomes much simpler and less time - consuming.

The basis is relatively simple when you have good market data: back teams that the public isn't.

When the publicised data was introduced we emphasised that it should be kept simple so that the public could see infection rates for their local hospitals, however it would seem that the Government wish the public to think infection rates are lower in our hospitals than they actually are.

When the LSST is completed, it will be far simpler to assemble a data set of millions of quasars.

He says the simple model worked well enough when data on bursts was relatively unrefined.

However, the second approach yielded results more in line with experimental data for gases adsorbed into carbon materials when equations are amended through simple corrections pertaining to energy levels, rather than by corrections related to the difference in the size of the various molecules involved.

For rare film expert Jim Moye, the goal when scanning the films is simple: Create as exact a copy of the data as possible.

When we started, it was just a simple hypothesis based on a correlation, and correlations are, of course, something that could be quite dubious, and they could go away if you get better data.

The accelerator's computers only record data when prompted by certain triggers, which are set for expected outcomes, like the particles produced when the simplest version of the Higgs decays.

When a set of data is inputted into the reservoir, the reservoir identifies important time - related features of the data, and hands it off in a simpler format to a second network.

The combined, data - driven approach that includes validation allows researchers to systematically determine when models are too simple, too complex or just right — the «Goldilocks» approach.

(At the position recorded by Messier, which also found its way into John Herschel's GC as GC 1594 and, consequently, into Dreyer's NGC as NGC 2478, no cluster is found, so that this object was missed, until T.F. Morris, in 1959, identified it correctly as Herschel's cluster H VIII.38 (NGC 2422), and realised that Messier had done a simple sign error in RA difference when reducing the positional data.)

The premise is simple and straightforward: a team of astronauts gathering data and specimens on the surface of Mars is forced to abandon the planet when a violent storm erupts.

What is so amazing is that such a simple common - sense approach is often far from the norm in many organizations when it comes to Learning and Development; not because of a lack of desire, but because tools and systems haven't made it easy to access and correlate data in order to measure.

This extract from the «Simple Guide to Improving NAPLAN Data» is a great starting point when preparing students for the NAPLAN Writing Test.

In their grade - level data teams, they created a simple action plan table that clarified who would be responsible for doing what and by when (see fig. 3).

Vertical Planning Task: Is a simple, yet powerful process for gathering data around what teachers and students are do when asked to use mathematics to model a real - life situation presented in the form of a word problem.

Many school leaders use a simple but highly effective yearly data calendar, which they display publicly and refer to constantly, so that everyone in the school community — including students and families — knows when important steps in the data cycle will take place.

This is becoming increasingly important as those using VAM - based data are using them to make causal claims, when only correlational (or in simpler terms relational) claims can and should be made.

A simple search reveals some disturbing information about the group that is doing the study that will produce the data that we all will be looking at when they release their Benchmark Study this month.

This is because they have an extra 5th processor which turns on when the phone is in standby to facilitate data syncing or even simple tasks like playing music.

MARC records are invisible to search engines, but when you transform them to linked data, users can find your library resources with a simple web search.

When we dug deeper, the data led us to a pretty simple answer, which we'll circle back to at the end of the report.

When the data was lost mainly because virus attack or human error it is very simple to recover data.

Designed to store data on up to one thousand (1000) clients, the new software comeswith an instant mortgage loan calculator, a debt ratio calculator, the ability to perform simple bookkeeping functions, a database designed to track letters and a built - in reminder to mortgage brokers when it's time to check up on their clients.

It makes it simple to quickly look over multiple quarters of data and I get more significance when I actually type the number instead of casually looking at it online in Mint or Personal Capital.

Designed to store data on up to one thousand (1000) clients, the new software comes with an instant mortgage loan calculator, a debt ratio calculator, the ability to perform simple bookkeeping functions, a database designed to track letters and a built - in reminder to mortgage brokers when it's time to check up on their clients.

Designed to store data on up to one hundred (1000) clients, the new software comes with an instant mortgage loan calculator, a debt ratio calculator, the ability to perform simple bookkeeping functions, a database designed to track letters and a built - in reminder to mortgage brokers when it's time to check up on their clients.

It's simple cherry picking your data, and a huge no - no when it comes to real science.

I know enough about time series with limited data to not read too much into periodicities, yet all when has to do is some simple comparisons on the residual temperature anomaly against noise models and one can see what role it plays.

Massaged isn't a word I would agree with, of course, but nevertheless the simple example I described showed how it was possible to measure temperature to a fraction of a degree even when the potential errors on the data points was as much as + / - 5 deg.

And whereas we might expect significant differences between different groups of people to show up when their responses to questions are averaged, in fact few differences between the groups emerge when the data from the surveys is analysed through simpler methods than those deployed by Lewandowsky.

I merely wanted to point to the basic data available on the Met office site (an organisation I visit frequently in order to use their archives) and ask those saying Rose was wrong to explain why, in simple terms, when the Met office graphs seemed to show he was basically correct.

Discussion of «pulsed stratospheric spraying» creating less noisy data for analysis of effects gave me a simple idea — If the input to the models assume that stratospheric spraying has been ongoing for decades, then plug into these models the temperature data accrued in the three days after 9/11 when, as reported at the time, the mean temperature over the US landmass «inexplicably» rose by 2 degrees C. in only three days while all aircraft were grounded, then the actual effect of stratospheric manipulations over US will emerge.

That data is created on a computer, and as many common «random» number generators will repeat when starting with the same seed, and with simple biases like the method of rounding numbers and the specific programming of math routines, patterns may be generated that do exist.

Report co-author Robert Fildes, a forecast researcher, developing a simple statistical model that delivers better results when compared with previous climate forecasts, i.e. by adding certain data he has been able to match his figures more accurately with a historic forecast.

PCA is confusing enough when you apply it to simple, low - dimensional data.

That's true when the data is as simple as one temperature time series is.

Ain't it nice when the simple physical models of the 1930s predict CO2 - driven AGW that is robustly affirmed by modern satellite data (as Dr.Benestad's analysis demonstrated)?

All is easy and simple, when the empirical data leads to a narrow enough distribution on the basis of the likelihood to make the influence of all plausible priors small, but in the opposite case we have the dilemma of the first paragraph.

All the GCM's are extremely simple when compared to actual climate, and they all have the same fundamental assumption that the fluctuation of CO2 is a major driver of climate change, even though we have very little real world data to support that claim.

General Introduction Two Main Goals Identifying Patterns in Time Series Data Systematic pattern and random noise Two general aspects of time series patterns Trend Analysis Analysis of Seasonality ARIMA (Box & Jenkins) and Autocorrelations General Introduction Two Common Processes ARIMA Methodology Identification Phase Parameter Estimation Evaluation of the Model Interrupted Time Series Exponential Smoothing General Introduction Simple Exponential Smoothing Choosing the Best Value for Parameter a (alpha) Indices of Lack of Fit (Error) Seasonal and Non-seasonal Models With or Without Trend Seasonal Decomposition (Census I) General Introduction Computations X-11 Census method II seasonal adjustment Seasonal Adjustment: Basic Ideas and Terms The Census II Method Results Tables Computed by the X-11 Method Specific Description of all Results Tables Computed by the X-11 Method Distributed Lags Analysis General Purpose General Model Almon Distributed Lag Single Spectrum (Fourier) Analysis Cross-spectrum Analysis General Introduction Basic Notation and Principles Results for Each Variable The Cross-periodogram, Cross-density, Quadrature - density, and Cross-amplitude Squared Coherency, Gain, and Phase Shift How the Example Data were Created Spectrum Analysis — Basic Notations and Principles Frequency and Period The General Structural Model A Simple Example Periodogram The Problem of Leakage Padding the Time Series Tapering Data Windows and Spectral Density Estimates Preparing the Data for Analysis Results when no Periodicity in the Series Exists Fast Fourier Transformations General Introduction Computation of FFT in Time Series

You were kind enough to provide a link to old stations for me when I asked the simple question as to what stations had been used in the BEST reconstruction to 1750, as I wanted to try and see if the data used was original or had been «adjusted.»

About the simplest means to a testable hypothesis about whether something external is changing the natural order is to develop a model using data from when we know that all was well with the world, and then look at how well that model fits with data observed when unnatural things were occurring.