Sentences with phrase «pattern fit their model»

Not exact matches

The inherited models by which men saw their lives as meaningful were breaking down simply because their lives were not fitting those patterns.

This mosaic pattern fits the prediction of the accretion model.

This chaining model fit the historical pattern of sense emergence better than alternative models.

The observations fit well with computer simulations, and can be used to refine models of how large - scale patterns, such as the distributions of galaxies and clusters of galaxies, came to be.

Results of the team's statistical analysis suggest that the long - verified, more - than - a-century-old model doesn't fit the pattern of seismicity seen on the New Madrid Seismic Zone in the past 2 centuries, the researchers report online today in Science.

A. 1) to diagnose the fit between model - simulated and observed patterns of zonal mean temperature change.

Then we can fit models to the data, giving us a rigorous statistical assessment of whether the expression pattern we observed supports the study hypothesis.

We also monitored wild bumble bee populations near greenhouses for evidence of pathogen spillover, and compared the fit of our model to patterns of C. bombi infection observed in the field.

BUST: Great for any cup size - fitted at bust so fuller busted women should consider sizing up WAIST: Fitted - ruffle detail and raised pattern conceals midsection HIPS: Fitted - for curvy women, size up one or two sizes for a better fit LENGTH: Mid-thigh to knee length -(on a 5» 5» fitted at bust so fuller busted women should consider sizing up WAIST: Fitted - ruffle detail and raised pattern conceals midsection HIPS: Fitted - for curvy women, size up one or two sizes for a better fit LENGTH: Mid-thigh to knee length -(on a 5» 5» Fitted - ruffle detail and raised pattern conceals midsection HIPS: Fitted - for curvy women, size up one or two sizes for a better fit LENGTH: Mid-thigh to knee length -(on a 5» 5» Fitted - for curvy women, size up one or two sizes for a better fit LENGTH: Mid-thigh to knee length -(on a 5» 5» model)

A hierarchical model, shown in diagram C of Figure 1, fits this pattern.

Or how well the pattern of students» answers fit the complex psychometric models used to estimate a student's proficiency.

While this investment came straight from SoftBank and not through Vision Fund, the Kabbage business model fits SoftBank's pattern of previous investments.

If a model comes along with low frequency variability that is less polar concentrated and fits the century, or half - century, trend pattern better, that would be news.

When I started working with climate models and saw how poorly they reproduce precipitation patterns, I was forced into the realization that the «science» was being fit to the models and that the models were not very realistic.

The pattern of answers a model gives you is no sure indication of whether or not you have the «fundamentals correct» A real skeptic would recognize that when the answer of a model does nt fit the data all you know is this: something is wrong.

It's not supposed to be physical model, it just summarises as repetitive pattern in the data which it is important to take note of when choosing the periods for over which to fit a trend.

I suspect that many, although perhaps not all, of the climate models are idiot - savant models, tuned to fit a hypothesized pattern that tells a particular story on past data, but rather lacking predictive skill.

This hardly seems to fit the IPCC description that «[m] odels reproduce observed continental - scale surface temperature patterns and trends over many decades» or is grounds for having «very high confidence» that the «model simulations show a trend in global - mean surface temperature from 1951 to 2012 that agrees with the observed trend.»

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 TimPatterns 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 Timpatterns 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

ly weren't able to re-run ensembles of these models with different parameter values, so instead, we just used a simple pattern - scaling approach to fit them to the data.

Professional Experience Client — XL Insurance (Hartford, CT) 6/2008 — Present Role — Business Intelligence Solutions Consultant — Insurance Data Warehouse • Participate in information - gathering sessions to determine and assess project requirements, identifying best - fit architecture solutions in line with enterprise data warehouse architectural standards • Work closely with the data modeler and the DBA in the design of the logical and physical data model • Create and maintain models for Cognos, performing extensive STAR Schema modeling to enable reporting decentralization and allow for user - driven ad - hoc reporting as well as drawing upon SSRS and OBIEE reporting solutions • Strategize with the ETL team to identify the best case design strategy for ETL - related activities including ETL design patterns determination, load strategies, load timing and frequency, and data retrieval expectations determination • Participate in providing Rough order of Magnitudes (ROM) estimates in and out of release projects, estimating resource requirements and managing within determined time constraints • Assist in the development of security tools in Cognos 8 using LDAP and Active directory while holding responsibility for maintaining run books and project documentation in Sharepoint

Path analysis revealed high fit between the theoretical model and empirical findings; moreover, the model's components revealed partially different patterns of relations for the two populations.

The significant model fit improvement by correlating the relationship - specific variables suggested that people have unique patterns of communicating stress with each type of relationship, depending on the specific perceived relationship quality.