Sentences with phrase «regression to the mean in»

Then there might be some protection on the downside with some undetermined probability, however slight, of regression to the mean in terms of earnings.

Not exact matches

With so many cheap stocks to choose from in 2009, even value managers who didn't want to buy financials could easily build a portfolio full of cheap stocks and wait for regression to the mean.

Snyder puts it this way: «Creative fidelity means to bear with their plateaus, regressions, imperfectness in such a way that these are transformed into new possibilities.

Jung has seen that psychologically this means that an overemphasis on either side of a polarity such as conscious - unconscious, or sacred - profane, will lead not to a dialectical coincidentia oppositorum but to a reinforcement or enantiodromia of the (untransfigured) other pole, that is, to an inundation or regression.17 It will be helpful to keep these Jungian motifs in mind as we explore the somewhat surprising parallels between Jung's notion of «individuation» and Altizer's idea of an ongoing kenotic incarnation.

Even factoring in regression to the mean from certain players, this team looks really fucking good.

Allowing so many average players to stay, which meant constant regression, leaving us in the position we're now in.

Betting experts wonder about regression to the mean as Texas Nick Martinez had a 0.35 ERA in his first four starts but 4.22 the last two.

This means that you will be less likely to see the sleep regression, and in case you have experienced this, you could settle your infant to sleep with hands - on suitable techniques if still swaddled.

We also estimated relative indices of inequality (RII) and slope indices of inequality (SII) as summary measures of relative and absolute inequalities of breastfeeding outcomes, respectively, across the entire distribution of maternal education.24 For child IQ, linear regression analyses using GEEs were performed to estimate mean IQ differences in lower maternal education from the reference category in each intervention group and compared between the groups.

Funnel plots, the Egger (weighted regression) test, and the Begg and Mazumdar (rank correlation) tests for funnel plot asymmetry were conducted to examine the relation between sample size and observed mean differences in blood pressure by infant feeding group (21).

In a meta - regression analysis, the mean differences between feeding groups observed in each study were unrelated to the mean total cholesterol concentrations in that study (P = 0.42In a meta - regression analysis, the mean differences between feeding groups observed in each study were unrelated to the mean total cholesterol concentrations in that study (P = 0.42in each study were unrelated to the mean total cholesterol concentrations in that study (P = 0.42in that study (P = 0.42).

Meta - regression was also used to establish whether mean concentrations of total cholesterol in each study had any effect on mean differences between feeding groups.

If a process is meant to improve the integrity of the electoral process, judgments that shoot it down can not advance the cause of our development; it means we have chosen a path of regression to the era where elections are decided by unsavory self - help in bloating the voter register with strange names on the part of politicians.

Progression / regression systems give us a simple, efficient means to put people in the best positions to train safely and develop strength.

These coefficients differ from the mean special education rates in Figure 1 because they refer to differences from the mean for whites, the excluded group in the regressions.

Even so, it seems unlikely that regression to the mean is the entire story, even in reading.

Thus in math there seems to be no effect from regression to the mean.

In reading, however, we found no difference in the test - score gains achieved by F schools and low - performing non-F schools, suggesting that regression to the mean could be influencing our results in readinIn reading, however, we found no difference in the test - score gains achieved by F schools and low - performing non-F schools, suggesting that regression to the mean could be influencing our results in readinin the test - score gains achieved by F schools and low - performing non-F schools, suggesting that regression to the mean could be influencing our results in readinin reading.

We also find that high - performing teachers» value - added dropped and low - performing teachers» value - added gained in the post-move years, primarily as a result of regression to the within - teacher mean and unrelated to school setting changes.

It is a book about why long - term investing serves you far better than short - term speculation; about the value of diversification; about the powerful role of investment costs; about the perils of relying a fund's past performance and ignoring the principle of reversion (or regression) to the mean (RTM) in investing; and about how financial markets work.

If mean reversion does occur in the years ahead, the regression will begin to show a strong relationship.

«Regression to the mean is the most powerful law in financial physics: Periods of above - average performance are inevitably followed by below - average returns, and bad times inevitably set the stage for surprisingly good performance.»

He was equally accurate in expressing that the greatest certainty over longer periods is regression to the mean.

Regression to the mean is nature's way of leveling the playing field, in almost every game, including investing.

Assuming that there is no relevant news, a hedge fund would buy the stock that is down and short the stock that is up and rely upon regression to the mean to fix this disparity in time.

If there is a reliable and helpful principle at works in our markets, my choice would be the ones the statisticians call «regression to the mean».

A linear regression says «let's allow our mean Y to depend on X in a linear fashion», instead of using a single mean Y to represent the data (independent of X).

Whether linear or curvilinear correlations, and regardless of the regression method, or outlier elimination process for values in a data set + / - 3 s.d.'s from the mean, error analysis and going back to raw data are essential procedures.

Their approach requires an estimate of the forced global mean temperature in a given year (excluding any natural variability), which are derived from Otto et al (2015), who employ a regression approach to reconstruct a prediction of global mean temperatures as a function of anthropogenic and natural forcing agents.

A recent paper by Loehle & Scafetta (L&S 2011) in a journal known as the «Bentham Open Atmospheric Science Journal «(also discussed at Skeptical Science) presents some analysis using regression to describe cycles in the global mean temperature, showing us many strange tricks one can do with curves and sinusoids, in something they call «empirical decomposition» (whatever that means).

Note that the sea ice extent is likely to be higher than the record September minimum in 2012 simply due to «regression to the mean», but it would not imply that there had been any «recovery».

Anyway, the change from the usual certainty level also means — I'm estimating here — that the coefficient of the regression associated with time is estimated at 0.5 W - 2 with a 95 % chance of being anywhere in the interval -(some - number) to one - point - something Wm - 2.

Then this means that the coefficient of the regression associated with time is estimated at 0.5 W - 2 with a 90 % chance of being anywhere in the interval 0.07 to 0.93 Wm - 2.

In our revision of the historical runoff model, we attempted to add several parameters of temperature — seasonal means, maximums, and minimums — as explanatory variables to the revised regression model but none were significant.

They draw a line on a graph showing the rate of warming from that unnatural peak in 1998 to now, and make it look like warming has continued at a steady pace, and not accelerated as expected (an argument that would fail any Statistics 101 class, as it ignores «regression to the mean»).

a Regressions of winter SLP and SAT trends upon the normalized leading PC of winter SLP trends in the CESM1 Large Ensemble, multiplied by two to correspond to a two standard deviation anomaly of the PC; b CESM1 ensemble - mean winter SLP and SAT trends; c b − a; d b + a. SAT in color shading (°C per 30 years) and SLP in contours (interval = 1 hPa per 30 years with negative values dashed).

A smaller multiyear ice area in the Arctic Ocean means that the sea ice summer extent is more sensitive to weather conditions during summer and thus more difficult to predict using statistical regression analysis since seasonal weather forecasts are not reliable.

«A strong warming and severe drought predicted on the basis of the ensemble mean of the CMIP climate models simulations is supported by our regression analysis only in a very unlikely case of the continually increasing AMO at a rate similar to its 1970 — 2010 increase» 7

We blended surface meteorological observations, remotely sensed (TRMM and NDVI) data, physiographic indices, and regression techniques to produce gridded maps of annual mean precipitation and temperature, as well as parameters for site - specific, daily weather generation for any location in Yemen.

In essence, all that's happened here is the rediscovery of the principle of regression to the mean.

For example, the fact that UAH / RSS slower warming in the 2000s has coincided with a solar minimum means that a regression is going to attribute the former to the latter, even though it might not be causal (although it probably is).

We also carried out analyses to examine the effect of potential influence of regression to the mean arising from the fact that high injury numbers might have been a factor in the decision to implement a 20 mph zone in some areas.

An exploratory analysis was to be performed by means of multiple regression with the dependent variable as the sum on the new scale and the independent variables as 14 personality disorder scales defined in DSM - 5, DSM - IV - TR, and DSM - III - R.

We implemented unadjusted and adjusted analyses (potential prognostic factors listed in table 2) of the outcomes (all quantitative) by using random effects linear regression models fitted by maximum likelihood estimation to allow for the correlation between the responses of participants from the same maternal and child health centre.29 We present means and standard deviations for each trial arm, along with the mean difference between arms, 95 % confidence intervals, and P values.

Our results might be partly attributable to regression to the mean and / or to improvement in scores resulting from expected maturational change.26 Such effects, however, would have been expected to be maximal during the first six months of the trial, and the further improvement in control group scores after six months was therefore unexpected.

Finally, it may be that the natural history of self - harm in adolescents is better then previously thought and the result represents a regression to the mean.

A decomposition methodology examined the contribution from different sources in explaining the SES gradient in early cognitive outcomes.34 Similar to the methodology used in the UK Millennium Cohort Study, we focus on the quintile 1 — quintile 5 (Q1 — Q5) and quintile 1 — quintile 3 (Q1 — Q3) gaps and calculate the percentile points and the percentage of the raw gaps explained by each candidate explanatory factor and each domain of factors.2 This was done by taking the product of the mean gap in each explanatory factor (mean difference between Q1 — Q5 and Q1 — Q3) by the β coefficients from linear regression models that predict reading and math ability from SES and all candidate explanatory factors.

Probit regression coefficients range from − 1 to 1: 1 - point increases in predictors equate to increases in the outcome z score (SDs above the mean) at the magnitude of the regression coefficient.