Sentences with phrase «mean variance analysis»

Behavioral finance has been the leading challenger to the efficient markets hypothesis, but the academics reply that behavioral anomalies are not an integrated theory that can explain everything, like the EMH, and its offspring like mean variance analysis, the capital asset pricing model, and their cousins.

The more traditional approach, which developed out of mean variance analysis some fifty years ago, tailors an individual's portfolio to his or her age, young investors should take more risk with stocks, and attitudes toward risk, conservative investors should hold more cash.

Not exact matches

One - way analysis of variance was used to compare the mean number of nutrients that were correctly identified for different labelling systems (continuous data), followed by Scheffe post hoc testing.

For variables with normal distribution, one way analysis of variance was used to compare mean values.

Exposing stats illusions Shalizi warns «causal - sounding phrases... encourage confusion» in many analyses of variance studies (where «due to,» «explained by,» «account for» don't have ordinary meanings).

Trenberth et al. (2005b) showed that the SAM is the leading mode in an EOF analysis of monthly mean global atmospheric mass, accounting for around 10 % of total global variance.

Assay, sample and side - to - side variation were quantified by analysis of variance and presumptively incorrect decisions related to high - risk disease were determined using the result from each MRD assay, the mean MRD in the patient as the measure of the true value, and each of 3 different MRD cut - off levels which have been used for making decisions on treatment.

For the purposes of our analysis, scores were standardized by subject, grade, and year to have mean zero and unit variance in the population of students attending Massachusetts public schools.

Principal Component Analysis is a translation of the data so the new origin is the mean and then a rotation so that the first axis is in the direction which explains the largest amount of variance, the second axis is the axis orthogonal to the first that explains the largest amount of the remaining variance, the third is the axis orthogonal to the first two that explains the largest amount of what remains and so on.

«A multiple linear regression analysis of global annual mean near - surface air temperature (1900 — 2012) using the known radiative forcing and the El Niño — Southern Oscillation index as explanatory variables account for 89 % of the observed temperature variance.

To describe the obtained data, frequency table, mean and standard deviation were applied and for analyzing the data, independent t - test and one - way analysis of variance were used.

One - way analyses of variance were used to test for significant overall mean differences by weight category groupings.

Using analysis of variance, the mean IGARs revealed significantly greater improvements for DOCC (versus EUC) at 6 -, 12 -, and 18 - month follow - ups (Table 7).

Continuous measures were analyzed by comparing means across the 3 conditions using analysis of variance procedures.

All analyses used the mean and variance adjusted weighted - least squares estimation method (WLSMV).

Doses of drugs over the trial were converted to mean daily equivalents of chlorpromazine and compared across groups by means of Kruskal - Wallis one way analysis of variance; this indicated no significant differences between treatment groups (medians of daily drugs in chlorpromazine equivalents: cognitive behaviour therapy 425, supportive counselling 517.75, routine care 450; χ = 0.963; P = 3D0.62).

We performed comparisons of means using analysis of variance, with planned post hoc analyses by the Tukey honestly significantly different test; effect sizes were obtained using mean differences with associated 95 % confidence intervals (CIs) and also calculated using Cohen d for purposes of comparability with related studies.

An analysis of variance demonstrated significant mean differences in egalitarianism between groups regarding reading and interpretation of religious scripture (p <.006) with the largest difference, as indicated by Tukeys HSD, between those who interpret scripture literally (least egalitarian), and those who do not read scripture at all (most egalitarian).

A one - way analysis of variance indicated significant mean differences in egalitarianism between groups on the DTAS (p <.0001).

We conducted negative binomial regression analysis instead of ordinary least squares regression analysis because accuracy of surrogate estimation was a count variable displaying overdispersion (i.e., its variance is larger than its mean).

This means that the sample size of a meta - analysis equals the number of included trials, often a few dozen, and that all within trial variance is ignored (Lipsey 2003).

A multi-group analysis was performed to compare four growth parameters (intercept mean, slope mean, intercept variance, and slope variance) between the Home - Start and the comparison group.

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.

As an additional check for facilitator effects, we conducted an analysis of variance to determine whether the mean number of intervention sessions attended differed significantly by facilitator.

The mean treatment duration was 275.49 days in usual care, 196.24 days in the standard treatment condition, and 210.15 days in the modular treatment condition; a fixed - effects analysis of variance showed that the groups were significantly different from each other (F2, 171 = 4.66, P =.011).

Three nested models with increased degrees of constraint were compared in multigroup analyses (fathers versus mothers): We specified a first model of configural invariance, in which the parameters (factor loadings, item intercepts, residual variances, factor variances, and covariance) were freely estimated in each group, whereas the factor means were constrained to zero in both groups.