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Chiara Lepore, John T. Allen, and Michael K. Tippett

values of shear. We evaluate the coefficients of the multivariate regression in Eq. (2) to quantify the best set of regressors and their relative efficiency in describing the intensity process. The results of various variable combinations are presented in Fig. 10 . We first inspect the coefficients for ; this combination was also used in Lepore et al. (2015) , and our results, as in the univariate case, are in line with the previous analysis. The coefficients for CAPE are half of those found in

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Lucie A. Vincent, Xuebin Zhang, and Xiaolan L. Wang

, the well-known phase shift in the PDO around 1976/77 ( Agosta and Compagnucci 2008 ) may have resulted in a step in the regionally averaged temperature indices. To investigate if the trend identified in Vincent et al. (2005) could be due to this circulation change, we conducted a trend analysis with the PDO index explicitly considered in a regression model: where y i is the annual percentage of cold and warm nights, i is the year, x i is the annual mean of the PDO index, and e i is the

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Reynold J. Stone

Sen method, as the authors correctly pointed out, is based on Kendall’s rank correlation, but as McCuen (2003) advised, is designed to detect a monotonically increasing or decreasing trend in a data series rather than an abrupt change. It is of course well known that a statistically significant result in a linear regression analysis does not necessarily imply that the linear regression model is valid. For example, Wilks (1995) cautioned against this common statistical pitfall and advised as

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Sarah D. Ditchek, T. Connor Nelson, Michaela Rosenmayer, and Kristen L. Corbosiero

gradients of vertical mass flux, which are due to moist convection induced by the midlevel vortex. Here, the relationship between TCs at genesis and their maximum attained intensity is investigated for TCs within the Atlantic basin main development region (MDR) over a 37-yr period (1979–2015) through a multiple-parameter linear regression analysis of storm-centered composites. Composites are generated using the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA

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Xuebin Zhang, Bruce Cornuelle, and Dean Roemmich

time scales and can have impacts on individual ENSO events (e.g., Rasmusson and Carpenter 1982 ; Harrison 1989 ; Harrison and Larkin 1998 ; Vintzileos et al. 2005 ; Kessler and McPhaden 1995 ; McPhaden and Yu 1999 ). The empirical analysis by ZM06 pointed out that local wind variations can have nonnegligible effects on interannual SST variations in the eastern equatorial Pacific, mainly through modifying vertical upwelling velocity. Through regression analysis, they found that a spatially

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Alexander B. Rabinovich, Georgy V. Shevchenko, and Richard E. Thomson

regression analysis of two vector series is based on the complex functional relationship between input and output vector series (cf. Greenan and Prinsenberg 1998 ); specifically, where V = ( U , V ) is the input vector series (wind), u = ( u , υ ) is the output vector series (ice drift or current velocity; herein “drift velocity”) and α = a + ib is a complex coefficient determined using a least squares regressional fit for the entire suite of wind and drift velocity observations. In our

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Savin S. Chand and Kevin J. E. Walsh

standard multiple regression technique. Other studies, for example HH03 , PL86 , and S09 , used a linear discriminant analysis to classify systems into developing and nondeveloping groups through the linear combination of predictor variables. While these ordinary linear regression methods may work well for large sample sizes, their confidence is questionable for small sample sizes. Also, they are appropriate for modeling response data that are quantitative and continuous in nature and therefore may

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Keyan Fang, Xiaohua Gou, Fahu Chen, Edward Cook, Jinbao Li, Brendan Buckley, and Rosanne D’Arrigo

promax-based ( Hendrickson and White 1964 ) rotated principal component analysis (RPCA; Richman 1986 ) to consider the variable (meteorological station) and the observations (precipitation records of each station). 3. Results and discussion a. Precipitation reconstruction A number of experiments were conducted for each target station to generate the best possible regression model with different settings ( Table 2 ). The search spatial correlation coefficients normally did not drop below 0.4 to

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Xianhua Wu, Lei Zhou, Ji Guo, and Hui Liu

variables, and regression analysis is carried out based on the establishment of panel model. Specific models are shown below: In the models, Q it represents the unit employment 9 of region i in the period t ; PW it the per capita remuneration of region i in the period t ; TC it the dummy variable of typhoon, whose value is 1 when it is in experimental group where region i in the period t is hit by typhoons and 0 when it is in control group free from typhoons; GDP it the gross product of

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Leon S. Robertson

opportunity to estimate the correlation of these factors with CO 2 emissions. A least squares regression model was used to estimate the effect of differences in the hypothesized predictive factors on CO 2 emissions among the 48 contiguous U.S. states during the years 2000–14. Alaska and Hawaii were excluded because the data on temperatures in those states were not available. Weather stations are concentrated in more highly populated areas ( National Oceanic and Atmospheric Administration 2017b ). The

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