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This means this multiple regression fits the data (Table 6).
A slightly larger peak correlation is provided by the AL index (see Table 1, where the underlined solar wind quantities in the multiple regression fits are normalized to average values N = 5 cm−3, T = 105 K, V sw = 400 km/s, E kl = 1 mV/m, and AL = −100 nT).
All predictors assessed were validated in the multiple regression fits.
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The multiple regression fit has also enabled the relative roles of solar irradiance and geomagnetic processes in driving the NO cooling to be determined.
This result proves that this multiple regression model fits the data and 35.4% of the variance in the data is explained by this model.
For purposes of the multiple linear regression fits, we also compute the 60 day running means of the F10.7, Ap, and Dst indices.
The data will be analysed by multiple regression modelling, fitting baseline measures of outcomes as covariates.
To explore factors associated with the score we conducted univariate and multiple regression analyses fitting linear mixed effects models with age, gender, years of education (dichotomized to "less" and "more or equal" than 10 years), fear of bleeding, history of prior OAT related complications, self-rated knowledge as fixed effects, and practice as random effect.
Because our forebrain weight measurements were not taken from a population balanced for important covariates, we used multiple regression to fit effects of age, body weight, sex, and non-forebrain brain weight (weight of total brain after the weight of the forebrain was substracted).
For multiple linear regression, the procedure can be repeated many times, each time producing a new dataset with the same multiple linear regression fit as the original data.
Founder effects are the estimates derived from the multiple linear regression fit above.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com