Sentences with phrase «in simple regression»

Tracts showing significant associations between callous — unemotional traits and FA, AD, RD and MD in simple regression models

In simple regression analyses examining the association between psychological distress, and demographic and disease variables, only the recipient of the questionnaire was statistically significant (B = 3.13, p = 0.03); partner recipient was significantly associated with higher psychological distress than patient recipient.

In step 1, age, sex, and variables that were shown to be statistically significant in simple regression analyses were simultaneously entered into the model as potential confounders.

Of course Eichengreen knows far more about the gold standard than most economists, and is far from being its harshest critic, so he'd undoubtedly be an outlier in the simple regression, y = α + β (x)(where y is vehemence of criticism of the gold standard and x is ignorance of the subject).

Not exact matches

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

The best of this work has taken advantage of the lottery - based admissions processes used by many school - choice programs, enabling researchers to draw far stronger conclusions about how schools affect student outcomes than the methods Coleman employed, which relied on simple regression techniques to adjust for differences in students» family background.

A simple regression of the average grades citizens assign to local schools in each state on NAEP and state proficiency rates simultaneously confirms that average grades (1) are strongly correlated with NAEP proficiency rates and (2) after controlling for NAEP proficiency rates, have no relationship whatsoever with proficiency rates on state tests.

Growth will be determined using a simple linear regression model in which current test scores are regressed on last year's test scores.

A simple regression would be inappropriate in these situations, since it would violate the independence assumption.

In the more complex case, however, even a simple linear regression may not work, and you would need to use methods of constrained regression and linear / quadratic programming to make this work (if it works at all).

In a more recent paper, our own Stefan Rahmstorf used a simple regression model to suggest that sea level rise (SLR) could reach 0.5 to 1.4 meters above 1990 levels by 2100, but this did not consider individual processes like dynamic ice sheet changes, being only based on how global sea level has been linked to global warming over the past 120 years.

Since our aim is not so much to produce a definitive analysis as to obtain some idea of the existence and magnitude of the effect, we will examine a variety of possibilities... we may plausibly expect... suggests that the area effect in Figs. 5a and 5b is likely to be underestimated... A potential problem here is that area may not be a reliable measure of cumulus activity... Figs 5c and 5d suggest that a simple linear regression may not be entirely appropriate.»

Would this be the same J. Bob who used FFT (seriously complex stats) to «prove» that long - term cycles in the temperature record are more significant than the linear trend revealed by linear regression (one of the simplest stats there is)?

In the end, I suspect that the F&R method and it's decedents are no more valid methods for estimating trend than simple OLS from linear regression.

1.5 C Projections: From my simple «CO > Temp» best - fit regression model (based on NASA temp set), I believe the equilibrium temperature will hit 1.5 C in 2025 (based on a baseline of 1955, and 2.5 ppm annual rise of CO2), and has already hit 1.5 C in 2017 if based on a baseline of 1880 - 1900 (adding 0.24 C to the 1955 baseline).

Tivy (University of Alaska Fairbanks); 5.7 Million Square Kilometers; Statistical This method is based on a simple regression where the predictor is the previous summer (May / June / July) sea surface temperature (SST) in the North Atlantic and North Pacific oceans near the marginal ice zone.

The predicted September sea ice area in the East Siberian and Laptev Seas, from a simple regression model using summer (Aug - Sep - Oct) sea surface temperatures in the North Atlantic as the predictor, is below normal but greater than in 2009.

Kapsch et al, 4.66 (± 0.59), Statistical For the prediction of the September sea - ice extent we use a simple linear regression model that is only based on the atmospheric water vapor in spring (April / May).

Kapsch et al., 4.1 (± 0.5), Statistical (same as June) For the prediction of the September sea - ice extent we use a simple linear regression model that is only based on the atmospheric water vapor in spring (April / May).

One approach is to estimate global temperature as a simple function of climate forcing and ENSO through a regression approach; perhaps the best - known example is Foster & Rahmstorf (2011), which found that when the impact of natural factors (volcanic eruptions, solar variations, and ENSO) is removed, the trend in global temperature has been remarkably steady since 1979 (when satellite observations of atmospheric temperature begin).

Kapsch et al, 4.75 (4.13 - 5.37), Statistical For the prediction of the September sea - ice extent we use a simple linear regression model that is only based on the atmospheric water vapor in spring (April / May).

According to the estimates made with a simple regression model, we can expect a seasonally ice ‐ free Arctic Ocean * as early as in the mid ‐ 2030s *.»

This is due to simple linear regression being ill - conditioned to errors in x variable.

While simple comparisons of observations with simulations by climate models have sometimes been used, the most commonly used approach is based on linear regression models (OLS), sometimes assuming error in the predictor (TLS or EIV).

In contrast, we show that simple regression methods used by several existing papers generally exaggerate positive feedbacks and even show positive feedbacks when actual feedbacks are negative.

With a simple regression model based on the four cycles (about 9.1, 10, 20 and 60 year period) plus an upward trend, that can be geometrically captured by a quadratic fit of the temperature, in the paper I have proved that all GCMs adopted by the IPCC fail to geometrically reproduce the detected temperature cycles at both decadal and multidecadal scale.

Many natural time series do show more persistent «memory» than a simple auto - regression (AR) process — in particularly (and classically) river outflows.

In fact, we performed a simple but powerful statistical test before drawing that conclusion: we calculated the linear - regression trends over successively longer periods to see whether the slope of the trend progressively increased (as it must if the curve is genuinely exponential); but, in recent years, the trend has ceased to increasIn fact, we performed a simple but powerful statistical test before drawing that conclusion: we calculated the linear - regression trends over successively longer periods to see whether the slope of the trend progressively increased (as it must if the curve is genuinely exponential); but, in recent years, the trend has ceased to increasin recent years, the trend has ceased to increase.

One set of their reconstructions uses the «Lasso» algorithm, while the other reconstruction methods use variations on a principal component (PC) decomposition and simple ordinary least squares (OLS) regressions among the PCs (varying the number of PCs retained in the proxies or the target temperatures).

In particular David's comment was that «Simple linear regression methods that don't take into the potential problems are certainly hazardous» so if these are what you are referring to as «traditional» methods (and they seem to be the tradition in a lot of papers in climate science) then they are problematiIn particular David's comment was that «Simple linear regression methods that don't take into the potential problems are certainly hazardous» so if these are what you are referring to as «traditional» methods (and they seem to be the tradition in a lot of papers in climate science) then they are problematiin a lot of papers in climate science) then they are problematiin climate science) then they are problematic.

To explain the moderating effect, according to the regression equation, respectively, take the campus pressure (U) positive and negative standard deviation to draw a simple effect analysis [13], as shown in Figure 2.

Finally, a simple linear regression was performed in order to examine whether self - compassion can significantly predict positive affect and the results are presented in Table 3 below.

In conclusion, this study was the first to adopt a linear regression approach in addition to the simple analysis of fMRI contrasts to directly investigate the neural mechanisms involved in economic decision making in individuals with varying psychopathic traitIn conclusion, this study was the first to adopt a linear regression approach in addition to the simple analysis of fMRI contrasts to directly investigate the neural mechanisms involved in economic decision making in individuals with varying psychopathic traitin addition to the simple analysis of fMRI contrasts to directly investigate the neural mechanisms involved in economic decision making in individuals with varying psychopathic traitin economic decision making in individuals with varying psychopathic traitin individuals with varying psychopathic traits.

We examined associations between variables in our main community sample using either simple logistic regression or multiple logistic regression (adjusted for sociodemographic and other variables) to generate odds ratios and Wald tests.

As shown in Figure 1, simple slopes tests of the plotted regression lines revealed diabetes conflict with mothers was associated with poorer adherence for Caucasians, but the slope was not significantly different from zero for the Latinos.