Sentences with phrase «best fit index»

Confirmatory factor analyses revealed adequate to good fit indices for all three models, although the unitary factor model provided the most parsimonious summary of the data.

Not exact matches

Where investors can get confused and / or make mistakes, is that many total stock market index funds use the Wilshire 5000 Index or the Russell 3000 Index as the benchmark or, as Morningstar labels it, the «best - fit index.&rindex funds use the Wilshire 5000 Index or the Russell 3000 Index as the benchmark or, as Morningstar labels it, the «best - fit index.&rIndex or the Russell 3000 Index as the benchmark or, as Morningstar labels it, the «best - fit index.&rIndex as the benchmark or, as Morningstar labels it, the «best - fit index.&rindex.»

The idea was this: the returns of a manager are equal to his alpha versus a composite index that best fits his performance.

If you're a good fit for a role, one of the Index team will contact you and discuss next steps.

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Results indicated that both high - quality close friendships and a drive to fit in with peers in adolescence were associated with better health at age 27, even after taking other potentially influential variables such as household income, body mass index, and drug use into account.

To identify best fit models relating paleoclimate to both the lake index and hominin evolution, we used a the stepAIC function in R package MASS to select the best fit model [38], see Figure 2.

The above historical performance figures from Morningstar indicate that the fund had a higher volatility (expressed as a standard deviation of returns) and underperformed the S&P 500 ® index, its best - fit benchmark, on a risk - adjusted basis (Sharpe Ratio) in both the three - and five - year trailing periods.

The idea of indexing is to produce returns that are good enough; returns equal to a benchmark that fits an investor's risk tolerance.

Index fund managers will analyze and pick stocks trading on the S&P 500 that have performed well and fit into different industry groups like Consumer, Energy, Technology, and Financial.

Stock and bond index funds fit the bill nicely, although if you're investing within a 401 (k), you'll have to do the best with the investments in your plan's menu.

There are several good reasons why index investing might be the best route, and ultimately you need to look after your own money as you see fit.

While its stated benchmark is the S&P 500 ® index, in reality its best - fit benchmark is the Morningstar US Growth index.

The upshot: As tempting as it is to place a wager on one company or another, I think the best path to wealth is to stick with a set of simple, broad - market index funds in an allocation that fits your stage in life.

Fixed indexed annuities are one savings option that could fit into your three - pillar stool, as they ensure your earnings will never fall below zero, as well as provide growth potential.

As mentioned in the May 8 post, in the 401 (k), the S&P 500 index fund is the only low - cost index fund that fits well with the desired asset allocation.

We work with our clients to find the indexed annuities that best fit their needs, goals and timeline.

Instead, a new model is emerging that I like a lot better: paying for advice in a transparent way, for any part of your life that needs it, then figuring out the products to fit that advice separately, whether through DIY investing in low - cost index funds or using a robo - advisor to handle the investment management part.

It should be obvious that certain types are natural fits for technical analysis (for example, NTs, the intuitive thinkers) while others (such as SJs, the sensing judgers) are better served by safe / proven vehicles like index funds.

But, when those same mutual funds were compared to the index that fit them the best, the majority (Clements doesn't give a specific number) lagged the index.

Ask yourself — why does it fit your investment plan better than a plain vanilla index fund or other smart beta fund?

Choose Options to Best Fit Your Goals Shield is an index - linked annuity.

Basically, you'd send a portfolio (text is fine - all that's needed is the full name of all of the investments and dollar amounts), and a time frame, and you'll get a custom benchmark portfolio shell comprised of the best available fitting indices for each asset class back, with returns looking back over any time frame (as long as the data goes back).

What you're supposed to do is determine a mix of viable asset classes that fits an individual investor's life, and then either fund it with something very diversified like mutual funds, ETFs, or index funds (the CFA program likes index funds, as most advisers can't even pick open - ended mutual funds, or ETFs, well enough to beat an index fund).

ETFs are also more volatile than their best - fitting index mutual fund, but have similar covariances.

The update to the question about «nearing retirement age» makes a well structured ladder of inflation - indexed bonds sound like a good fit for someone with a low tolerance for volatility who is willing to sacrifice the possibility of growth in exchange for stability and predictability.

The idea was this: the returns of a manager are equal to his alpha versus a composite index that best fits his performance.

ETFdb staff has allocated each ETF in the ETF database, as well as each index, to a single «best - fit» ETFdb.com Category.

It is amazing how well the Southern Oscillation Index (from NCAR) fits to a temperature record such as GISS, and only shows deviations in recent years during the big volcanic disturbances.

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

Re the GISTEMP Land - Ocean Index graph: I should think that an 8 - year RUNNING MEAN would give an astonishingly - good fit to the data; one that will be statistically - sound as a regression.

Index Universal Life insurance may or may not be a good fit for your goals.

These plans may be a good fit for those who are wanting to choose from multiple strategies using global indices for optimizing their policy's index crediting potential.

One should possess sound knowledge in the various methods of production available for a component and the ability to make decisions to choose the one which best fits into the purpose with maximized profits and enhanced customer satisfaction index.

The most commonly used goodness - of - fit statistics were used in the present study (Byrne, 2016; Laveault & Grégoire, 2014), that is, the chi - square to its degrees of freedom (χ 2 / df; a χ 2 / df close to or less than 2.0 was considered to be indicative of a good model fit, and close to or less than 5.0 as indicative of a satisfying fit); the Root Mean Square Error of Approximation (RMSEA; good fit < 0.05, satisfying fit < 0.08); the Standardized Root Mean Square Residual (SRMR; good fit < 0.05, satisfying fit < 0.08); the Comparative Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit statistics were used in the present study (Byrne, 2016; Laveault & Grégoire, 2014), that is, the chi - square to its degrees of freedom (χ 2 / df; a χ 2 / df close to or less than 2.0 was considered to be indicative of a good model fit, and close to or less than 5.0 as indicative of a satisfying fit); the Root Mean Square Error of Approximation (RMSEA; good fit < 0.05, satisfying fit < 0.08); the Standardized Root Mean Square Residual (SRMR; good fit < 0.05, satisfying fit < 0.08); the Comparative Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit, and close to or less than 5.0 as indicative of a satisfying fit); the Root Mean Square Error of Approximation (RMSEA; good fit < 0.05, satisfying fit < 0.08); the Standardized Root Mean Square Residual (SRMR; good fit < 0.05, satisfying fit < 0.08); the Comparative Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit); the Root Mean Square Error of Approximation (RMSEA; good fit < 0.05, satisfying fit < 0.08); the Standardized Root Mean Square Residual (SRMR; good fit < 0.05, satisfying fit < 0.08); the Comparative Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit < 0.05, satisfying fit < 0.08); the Standardized Root Mean Square Residual (SRMR; good fit < 0.05, satisfying fit < 0.08); the Comparative Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit < 0.08); the Standardized Root Mean Square Residual (SRMR; good fit < 0.05, satisfying fit < 0.08); the Comparative Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit < 0.05, satisfying fit < 0.08); the Comparative Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit < 0.08); the Comparative Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199Fit Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 1Index (CFI; good fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit ≥ 0.95; satisfying fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit ≥ 0.90), and the adjusted goodness of fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 1index (AGFI; good fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit ≥ 0.95; satisfying fit ≥ 0.90)(Hu & Bentler, 199fit ≥ 0.90)(Hu & Bentler, 1999).

Goodness - of - fit indices for the six - factor model with 14 items indicate a well - adjusted fit to the data (χ2 / df = 1.427, RMSEA = 0.019, SRMR = 0.021, CFI = 0.992, AGFI = 0.982) which confirms study 1's findings.

Fit indices used to evaluate the model included a χ2 goodness - of - fit test (nonsignificant values indicate good fits), the comparative fit index (scores of > 0.95 indicate better fits), the root mean square error of approximation (values of < 0.05 indicate good fits), and the standardized root mean square residual (values of < 0.08 indicate good fits).43, 44 Missing values were imputed through multiple imputation by using functions in the missing data library in S - Plus (Insightful Corp, Seattle, WA).45, 46 The combined data for the cross - lagged / survival model converged more quickly with 15 imputed data sets than did the model that used a likelihood - based approach to missing daFit indices used to evaluate the model included a χ2 goodness - of - fit test (nonsignificant values indicate good fits), the comparative fit index (scores of > 0.95 indicate better fits), the root mean square error of approximation (values of < 0.05 indicate good fits), and the standardized root mean square residual (values of < 0.08 indicate good fits).43, 44 Missing values were imputed through multiple imputation by using functions in the missing data library in S - Plus (Insightful Corp, Seattle, WA).45, 46 The combined data for the cross - lagged / survival model converged more quickly with 15 imputed data sets than did the model that used a likelihood - based approach to missing dafit test (nonsignificant values indicate good fits), the comparative fit index (scores of > 0.95 indicate better fits), the root mean square error of approximation (values of < 0.05 indicate good fits), and the standardized root mean square residual (values of < 0.08 indicate good fits).43, 44 Missing values were imputed through multiple imputation by using functions in the missing data library in S - Plus (Insightful Corp, Seattle, WA).45, 46 The combined data for the cross - lagged / survival model converged more quickly with 15 imputed data sets than did the model that used a likelihood - based approach to missing dafit index (scores of > 0.95 indicate better fits), the root mean square error of approximation (values of < 0.05 indicate good fits), and the standardized root mean square residual (values of < 0.08 indicate good fits).43, 44 Missing values were imputed through multiple imputation by using functions in the missing data library in S - Plus (Insightful Corp, Seattle, WA).45, 46 The combined data for the cross - lagged / survival model converged more quickly with 15 imputed data sets than did the model that used a likelihood - based approach to missing data.

Model fit was evaluated using a chi - square test statistic as well as Comparative Fit Index (CFI), Tucker - Lewis Index (TLI) and root - mean - square error of approximation (RMSEfit was evaluated using a chi - square test statistic as well as Comparative Fit Index (CFI), Tucker - Lewis Index (TLI) and root - mean - square error of approximation (RMSEFit Index (CFI), Tucker - Lewis Index (TLI) and root - mean - square error of approximation (RMSEA).

At ages 1.5 and 3 the BIC and the BLRT indicated that five profiles resulted in better model fit than four profiles (fit indices are reported in Supplementary Table S1).

Confirmatory factor analysis suggested good to acceptable fit indexes.

Evaluation of model fit was based on multiple criteria, including the theoretical meaningfulness of the model, absolute - fit indices (how well a model fits the data, without comparing to a baseline model), incremental fit measures (how much better the model fits than a baseline model) and model cross-validation (how the model can be replicated with an independent sample).

Fit indexes were very good: S - B χ2 = 230.83, 214 df; RCFI =.97; RMSEA =.020, CI =.000 to.037.

Mplus v7.11 was used for all analyses.23 SDQ items were treated as ordinal, with weighted least - squares means and variance — adjusted estimation used.23 Given the χ2 statistic's propensity to reject good models when samples are large and / or complex, the comparative fit index (CFI) and root mean square error of approximation (RMSEA) were used to assess model fit.

The model chi - square is the only model fit statistic available when modeling growth in binary observed variables, and this index suggested that our model provided a good fit to the data, χ 2 (25) = 29.10, p > 0.05.

Confirmatory factor analyses indicated that while the hypothesized three - factor model fit significantly better than an alternative one - factor model, the fit indices associated with the three - factor model were below satisfactory cutoffs, thus tempering conclusions that the best fitting structure was found and highlighting the need for additional research.

Nevertheless, the Practice Environment Scale — Nursing Work Index (PES — NWI) seems to be one of the most promising instruments because of its appropriateness (content validity), its structure, which has a rather good fit (construct validity), its ability to discriminate magnet hospitals like other NWI derivates (discriminant validity), and it has also been associated in cross-sectional studies with health outcomes, especially nurses» self - assessed mental health but also with patients» health outcomes objectively assessed (concurrent validity).

Overall, this model fits the data extremely well (χ2 = 70.4, P <.001; comparative fit index = 0.99, Tucker - Lewis index = 0.96, root mean square error of approximation = 0.04, standardized root mean square residual = 0.02).