Sentences with phrase «tests of linear trend»

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.

Tests of linear trend across increasing quintiles of intake were conducted by assigning the medians of intakes in quintiles treated as a continuous variable.

Not exact matches

To assess whether the decline was greater at older ages with the test for linear trend, we reran the analysis using the categories of age as a continuous variable.

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.

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.

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.

But the task at hand was to test Dan H.'s claim that the Berkeley data reinforce the characterization of temperature change as «long - term linear trend» or «linear - plus - cyclic.»

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.

More to the point of William Briggs» article though, I can not see why a conclusion should be that one should not perform linear trend tests on data.

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.

Our calculations of the statistical significance of least - squares linear trends in timeseries are based on the two - sided t - test methodology and adjustment for autocorrelation reviewed and outlined by Santer et al. (2000).

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 increase.

If I limited the test to forecasting the trend only and use the linear trend from 1969 — 1988 (0.15 per decade) as the null hypothesis to compare with Hansen's trend the skill of Hansen is — 1.76 i.e. just sticking a line through the last 20 years is much more skillful.

Dhogaza and myself both explained why it's appropriate to use the period after 1975: That's when the GHG forcing is dominant and the expected trend in temperature would be roughly linear (and linearity was an assumption in the simplest form of the ADF tests, as I came to understand).

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.