Sentences with phrase «of a linear trend model»

In addition, the regression of a linear trend model can produce a coefficient of determination, r ².

Not exact matches

Tests for trend with the use of simple linear regression analysis were performed by modeling the median values of each fiber category as a continuous variable.

Tests of linear trend across categories of coffee consumption were performed by assigning participants the midpoint of their coffee - consumption category and entering this new variable into a separate Cox proportional - hazards regression model.

The relationship between an athlete personal best in competition and back squat, bench press and power clean 1RM was determined via general linear model polynomial contrast analysis and regression for a group of 53 collegiate elite level throwers (24 males and 29 females); data analysis showed significant linear and quadratic trends for distance and 1RM power clean for both male (linear: p ≤ 0.001, quadratic: p ≤ 0.003) and female (linear: p ≤ 0.001, quadratic: p = 0.001) suggesting how the use of Olympic - style weightlifting movements — the clean, in this particular case, but more in general explosive, fast, athletic - like movements — can be a much better alternative for sport - specific testing for shot putters (Judge, et al, 2013).

A test for linear trend of effects across coffee consumption categories was performed by regressing each log RR on the ordered categorical variable for coffee in 5 levels using a random - effect meta - regression model.

While using a percent growth rate for free cash flows might be conventional, mathematically convenient and easier to convey to others, it is not as accurate or conservative as using an absolute rate of change from a linear trend model.

The model in F&R is elegantly simple and does a good job of showing that a linear trend due to CO2 + a few forcings that we know to be operant and important are sufficient to explain most of the variability in all of the temperature datasets.

One baseline model is a simple linear trend from the start of the century.

The DSLPA index computed from HadSLP2 shows a much more «trend - like» reduction than the datasets shown in the manuscript, in which the 1970s shift plays a less pivotal role; though the amplitude of slope of the linear trend is consistent with the model and observations.

I looked at eight CMIP 5 models whose output I had ready access to and calculated linear trends of potential intensity over the period 2006 - 2100 under the RCP 8.5 emissions pathway.

I went to the trouble of fitting a linear trend line to the A2 model input line from 2002 - 2009 and obtained a correlation coefficient (R2) of 0.99967.

Also, about 2/3 of the individual ensemble - members (46 out of 68) from all the model runs have linear trends that indicate at least a nominal weakening — this is significantly different from what one would be expected from a Binomial distribution with a 50 % probability.

Canadian Ice Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Over 1900 — 2005 the observed trend is substantially greater than the model expectation: a probability of 87 % for the linear trend compared with 78 % for the robust trend.

Over 1950 — 2005, the observed warming trend is slightly greater than the model expectation: a probability of 57 % for the linear trend compared with 61 % for the robust trend.

Because their model is insensitive to the small linear trends in GSL over the Common Era, the relative heights of the 300-1000 CE and 20th century peaks are not comparable.

The critical phrase seems to be «Because their model is insensitive to the small linear trends in GSL over the Common Era, the relative heights of the 300-1000 CE and 20th century peaks are not comparable.»

Box 9.2 Climate Models and the Hiatus in Global Mean Surface Warming of the Past 15 Years «The observed global mean surface temperature (GMST) has shown a much smaller increasing linear trend over the past 15 years than over the past 30 to 60 years (Section 2.4.3, Figure 2.20, Table 2.7; Figure 9.8; Box 9.2 Figure 1a, c).

Without a validated model there is no justification for fitting a linear trend and extrapolation way outside the range of the data.

Canadian Ice Service; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictors.

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

The models used in climate science are based not on extrapolated linear trends, but on expected consequences of all known physical forcings — which are not periodic.

The trends for all of the scenarios for the period 2000 - 2040 are effectively linear, similar to or lower than the trend 1997 - 2000 that informed the model start point, and on the scale of the predicted 0.2 C increase there is no variability to speak of in any of them.

The long periods found in dT / dt are different since a linear trend is accommodated by the constant rate of change «c» in the model.

The best fit linear trend lines (not shown) of the model mean and all datasets are set to zero at 1979, which is the first year of the satellite data.

A generalized nonlinear mixed model was used for modeling temporal trends of tree mortality and recruitment rates, and a linear mixed model was used for modeling temporal trends of tree growth rates (Methods).

Figure 1: Heat content smoothed with 1 -2-1 filter and overlaid with linear trend portion of best - fit model (slope = -0.35 x 1022 J / yr)

But the real L&S model failure is in the linear trend, since these mysterious astronomical cycles are simply oscillations on top of that trend.

The magnitude of each relative changepoint is calculated using the most appropriate two - phase regression model (e.g., a jump in mean with no trend in the series, a jump in mean within a general linear trend, etc.).

So this model will produce a linear warming trend with two natural oscillations superimposed on top of it.

Past climate models, as judged by the performance of the majority of Coupled Model Intercomparison Project 3 (CMIP3) simulations used in the IPCC Fourth Assessment Report, underestimated the observed linear trend in Arctic sea ice loss (Stroeve et al., 2007).

Absent an exponential temperature rise signaling forcing synergies in progress, seeing a linear trend for five decades leads to deep discounting of models that need to get to a degree per decade in order to make 2100 as hot as some have claimed over the last half century.

«In response to those who complained in my recent post that linear trends are not a good way to compare the models to observations (even though the modelers have claimed that it's the long - term behavior of the models we should focus on, not individual years), here are running 5 - year averages for the tropical tropospheric temperature, models versus observations...»

In fact, you can get a very good fit with actual temperature by modeling them as three functions: A 63 - year sine wave, a 0.4 C per century long - term linear trend (e.g. recovery from the little ice age) and a new trend starting in 1945 of an additional 0.35 C, possibly from manmade CO2.

The historical record of our climate is seems pretty clearly to follow a sin wave, yet all the models attempt to predict a dynamic, cyclical climate using linear trends.

Fit a linear model (preferably with ARMA (1,1) noise as the noise process is autocorrelated), the trend is the slope of that linear model (i.e. the coeffcient of the linear term of the model).

However, it is instructive to note that a simple model of a linear trend plus sine wave matches history so well, particularly since it assumes such a small contribution from CO2 (yet matches history well) and since in prior IPCC reports, the IPCC and most modelers simply refused to include cyclic functions like AMO and PDO in their models.

Trends in rates of child diagnoses by mother's response level in children with a baseline diagnosis and in rates of incidence or relapse in children without a baseline diagnoses were examined separately using the Cochran - Armitage test for trend.29 Low event rates precluded fitting regression models adjusting for potential confounders, such as age and sex of child, using generalized linear models with an identity - link function, to estimate parameters for adjusted trends.

Intervention effects will be assessed by conducting linear and logistic random effects models incorporating a time by group interaction or latent growth curve modelling to determine whether trends across the three data points within the course of the patients» treatment differ between the carer groups.39 The models will adjust for confounders and effect modifiers as necessary.

Conditional latent growth model results show that having adversity is positively associated with the intercept, but negatively associated with the linear trend of changes of depressive symptoms in adolescence (p <.01).