Sentences with phrase «simple linear regression model»

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).

Raw model sea ice concentration data was processed using a simple linear regression model and satellite derived ice extent to produce bias corrected predictions.

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).

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).

A final simple linear regression model was fit and concluded that the number of searches a dog performs has a statistically significant effect on a dog's energy level (t = − 3.01, d.f. = 1, p - value = 0.0033).

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

Example of a simple linear regression model of climate change.

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.

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.

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 variance of a forecast using linear regression is often biased low, however, so a new record low is still plausible and perhaps outside the scope of this simple model.

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

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).