Sentences with phrase «regression equation»

I determined regression equations for portfolios begun in 1923 - 1990.

In regression equations group (ASD vs control) and family functioning served as predictors for parental adjustment.

The resulting linear regression equations were then simply combined to calculate annual balance.

Point estimates illustrate values of externalizing behavior evaluated at 1 SD above and below the mean for conflict based on regression equations estimated for values of temperament 1 SD above and below the mean.

All four regression equations start at almost the exact same place at the lowest percentage earnings yields (i.e., at the highest valuations).

It has a section with the exact regression equations (in allocation increments of 10 %) for making comparisons.

In order to test this hypothesis, three first - order interaction terms involving two independent variables and neurological status were added to the above regression equation, and a multiple regression was conducted.

So, we just need to examine whether the powerless (mediating variables) had a significant influence on task performance (dependent variable) and whether task behavior (independent variable) was still significantly correlated with task performance (dependent variable) when powerless (mediating variables) was entered as a mediator in regression equation of task behavior (independent variable) and task performance (dependent variable).

I collected regression equations using 100E5 / P, 100E10 / P, 100D5 / P and 100D10 / P.

I have calculated a complete set of regression equations with 2 % TIPS.

I have calculated regression equations for SWAT and SwOptT portfolios.

I used Excel's plotting capability to determine regression equations versus the percentage earnings yield 100E10 / P.

Moderator and mediator variable models were specified via hierarchical multiple regression equations and path analyses, respectively.

The Value D regression equation is y = 0.2694 x +5.0841 plus 3 and minus 2, where x = 100E10 / P and y = the historical surviving withdrawal rate.

The Stock - Return Predictor is based on standard regression equations with no distinction as to the overall market direction.

Here are the new regression equations.

Here are the Half Constant Terminal Value Rate regression equations.

Here is the LHOptA regression equation of 1923 - 1975 30 - Year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P: y = 0.5763 x +2.6968 plus 3.0 % and minus 1.2 %.

Here is the LHOptE regression equation of 1923 - 1975 30 - Year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P: y = 0.5795 x +2.6739 plus 2.5 % and minus 1.0 %.

Here is the LHOptB regression equation of 1923 - 1980 30 - Year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P.

Here is the LHOptD regression equation of 1923 - 1975 30 - Year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P: y = 0.5776 x +2.6213 plus 3.0 % and minus 1.4 %.

Here is the LHOptC regression equation of 1923 - 1975 30 - Year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P: y = 0.6009 x +2.5881 plus 3.5 % and minus 1.4 %.

Here is the SwOptT2 regression equation of 1923 - 1975 30 - Year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P: y = 0.3874 x +3.6363 plus and minus 0.8 %.

Here is the SwAT2 regression equation of 1923 - 1975 30 - Year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P: y = 0.45 x +3.1172 plus 1.0 % and minus 0.7 %.

S&P 500 Returns S&P 500 Returns Statistics S&P 500 Regression Equations S&P 500 FIRST YEAR within a DECADE with returns above 7 %, 6 %, 5 % and 4 % S&P 500 Dividend Yields

We did use the original Baker - Kovner regression equation in this study to calculate total watershed runoff for forests in their current condition so that we could estimate the percentage increase in runoff associated with treatments.

The intercept and all first order regression coefficients then have their own regression equations at the species - level of the model: (2a) and (2b) where γ00 and γ0q are the intercepts for the species intercepts and the q in 1,..., Q first order regression coefficients (the four climate variables).

Non of these critical variables are measured in any of the VAM regression equations I have seen.

Regression equations retain the same proportional scaling behavior.

The next step will be to calculate some more HSWRxxT2 regression equations to see how well this simple curve fit performs.

Here is the HSwOptT2 regression equation of 1923 - 1975 30 - Year Half Constant Terminal Value Rates HCTVR versus the percentage earnings yield 100E10 / P: y = 0.373 x +2.9701 plus and minus 0.8 %.

Here is the CLHOptB regression equation of 1923 - 1975 30 - Year Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.5436 x +1.6339 plus 4.0 % and minus 2.0 %.

Here is the HLHOptF regression equation of 1923 - 1975 30 - Year Half Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.4663 x +2.3191 plus 1.4 % and minus 0.8 %.

Here is the CLHOptC regression equation of 1923 - 1975 30 - Year Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.5531 x +1.4915 plus 4.0 % and minus 2.0 %.

Here is the HLHOptC regression equation of 1923 - 1975 30 - Year Half Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.5772 x +2.0241 plus 3.5 % and minus 1.4 %.

Here is the HLHOptA regression equation of 1923 - 1975 30 - Year Half Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.5546 x +2.1411 plus 3.0 % and minus 1.2 %.

Here is the HLHOptD regression equation of 1923 - 1975 30 - Year Half Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.544 x +2.1606 plus 3.0 % and minus 1.4 %.

Here is the CLHOptG regression equation of 1923 - 1975 30 - Year Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.4922 x +1.5745 plus 2.0 % and minus 1.2 %.

Here is the LHOptG regression equation of 1923 - 1980 30 - Year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P: y = 0.459 x +3.2254 plus 2.0 % and minus 0.8 %.

Here is the CSwOptT2 regression equation of 1923 - 1975 30 - Year Constant Terminal Value Rates CTVR versus the percentage earnings yield 100E10 / P: y = 0.3645 x +2.2578 plus 1.2 % and minus 0.8 %.

Here is the HLHOptE regression equation of 1923 - 1975 30 - Year Half Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.557 x +2.1471 plus 2.5 % and minus 1.0 %.

Here is the CLHOptD regression equation of 1923 - 1975 30 - Year Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.518 x +1.6456 plus 3.5 % and minus 1.8 %.

Here is the CLHOptF regression equation of 1923 - 1975 30 - Year Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.4591 x +1.6232 plus 1.6 % and minus 1.0 %.

Here is the HSwAT2 regression equation of 1923 - 1975 30 - Year Half Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.4437 x +2.4023 plus 1.0 % and minus 0.7 %.

Here is the HLHOptG regression equation of 1923 - 1975 30 - Year Half Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.5018 x +2.2599 plus 2.0 % and minus 0.8 %.

Here is the CLHOptA regression equation of 1923 - 1975 30 - Year Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.5283 x +1.6283 plus 3.0 % and minus 1.6 %.

Here is the CSwAT2 regression equation of 1923 - 1975 30 - Year Constant Terminal Value Rates versus the percentage earnings yield 100E10 / P: y = 0.4379 x +1.6886 plus 1.2 % and minus 0.8 %.

Here is the LHOptF regression equation of 1923 - 1980 30 - Year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P: y = 0.4509 x +3.1199 plus 1.4 % and minus 0.8 %.