Sentences with phrase «regression equations using»

I determined the regression equations using 30 - year Historical Surviving Withdrawal Rates versus the percentage earnings yield 100E10 / P (or 100 / [P / E10]-RRB-.

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

She did, «finding that «beta weights» are the coefficients of the «predictors» in a regression equation used to find statistical correlations between variables.

The regression equation using the initial percentage earnings yield 100E10 / P for x is y = 904.6 x + 1344.7, where y is in real dollars (plus and minus $ 1200) and R - squared is 0.7448.

The regression equation using the year 10 percentage earnings yield 100E10 / P for x is y = -186.85 x + 9203.6, where y is in real dollars (plus $ 5000 and minus $ 4000) and R - squared is 0.0278.

Not exact matches

We used modified Poisson regression analysis with generalized estimating equations (GEEs) to estimate socioeconomic inequalities in discontinuing exclusive breastfeeding before 3 months and any breastfeeding before 12 months.

Some online dating sites offering compatibility matching methods use the word similarity as: «a proprietary Dyadic Adjustment Scale», others mean: «a proprietary multivariate linear regression equation», some say a mix of similarity and complementarity meaning: «a proprietary multivariate logistic regression equation», still others mix similarity and complementarity meaning: «a proprietary equation to calculate «compatibility» between prospective mates!»

The Pareto is summarized using a weighted least square expression as in equation (2), the regression line is termed the utopia line, and a quadratic expression, the utopia curve.

First we entered three measures of data use (principals» view of district data use, their own data use, and teachers» perceptions of principal data use), as a block, into a regression equation.

Standard regression equations were used to estimate the «effects» of LSE, LCE, and an aggregate measure of efficacy on leader behavior as well as school and classroom conditions.

Rather, Klees wrote, «[f] or proper specification of any form of regression analysis... All confounding variables must be in the equation, all must be measured correctly, and the correct functional form must be used.

I used Excel's plotting capability to determine regression equations (i.e., linear curve fits).

I used Excel plots to determine regression equations (i.e., linear curve fits) of balances versus the percentage earnings yield 100E10 / P.

I used Excel's curve fitting capability to fit straight lines to the data and to report the equations (i.e., regression equations) and goodness of fit (R - squared).

I use Excel to generate the regression equation between Historical Surviving Withdrawal Rates and valuations (100E10 / P).

This is the regression equation for the 10 - year stock return and the percentage earnings yield 100E10 / P (using 1923 - 1972 data): y = 1.5247x - 4.5509 where y is the annualized real return in percent and x is 100E10 / P or 100 / [P / E10].

This is from You Can't Count on 7 %: «This is the regression equation for the 20 - year stock return and the percentage earnings yield 100E10 / P (using 1923 - 1972 data): y = 1.0849x - 1.4488 where y is the annualized real return in percent and x is 100E10 / P or 100 / [P / E10].

This is the regression equation for the 30 - year stock return and the percentage earnings yield 100E10 / P (using 1923 - 1972 data): y = 0.4159 x +3.764 where y is the annualized real return in percent and x is 100E10 / P or 100 / [P / E10].

Here is the regression equation for the 30 - year stock return and the percentage earnings yield 100E10 / P (using 1923 - 1972 data): y = 0.4159 x +3.764 where y is the annualized real return in percent and x is 100E10 / P or 100 / [P / E10].

I used Excel's curve fitting capability to fit straight lines to the data and report the equations (i.e., regression equations) and goodness of fit (R - squared).

I used Excel to determine regression equations (i.e., straight - line, linear curve fits).

This exponential growth equation can be transformed into a linear form so it can be modeled using linear regression.

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

I used Excel's plotting function to calculate regression equations (i.e., linear, straight - line curve fits) of the dividend amount at Year 10 and at Year 20 versus the percentage earnings yield 100E10 / P.

I used Excel's charting capability to calculate (linear) regression equations.

I used Excel to determine regression equations and plot Historical Surviving Withdrawal Rates versus the Percentage Earnings Yield 100Ex / P for E1, E5, E10, E15, E20, E25 and E30.

For a climate model that has some correlation with the past data the model estimates should be converted into a recalibrated estimate using the regression equation.

If the regression equation is then used to reconstruct temperatures for another period during which the proxies are statistically similar to those in the calibration period, it would be expected to capture a similar fraction of the variance.

Participated in the development of a multiple linear regression equation modeling process that used Geostatistical Analyst and Spatial Analyst extensions of the ArcGIS software

The study involved administering all 3 sets of scales to a general population sample who were then interviewed by clinical interviewers blinded to screening scales scores and classified as having or not having SMI based on 12 - month prevalences of DSM - IV disorders, as assessed by the Structured Clinical Interview (SCID) for DSM - IV16 and scores on the GAF.1 Logistic regression analyses were then carried out to estimate the strength of associations between the screening scales and SMI using linear and nonlinear prediction equations that assumed either additive or multiplicative associations among the different screening scales.

Its clinical utility is somewhat limited owing to the scoring requirement of using a logistic regression equation.

Marginal logistic regression models were fitted for repeated - measures data (eg, well - child visits) using generalized estimating equations with working - independence covariance structures.28

Because of substantial missing data on 2 direct parenting measures (29 %), multiple imputation via chained equations was used to handle missing covariate data.30 This approach uses regression models to predict missing data from available variables with 20 imputation iterations selected.

She has technical expertise in a wide range of statistical techniques used in the social sciences, including structural equation modeling, confirmatory factor analysis and MIMIC approaches to measurement, path modeling, regression analysis (e.g., linear, logistic, Poisson), latent class analysis, hierarchical linear models (including growth curve modeling), latent transition analysis, mixture modeling, item response theory, as well as more commonly used techniques drawing from classical test theory (e.g., reliability analysis through Cronbach's alpha, exploratory factor analysis, uni - and multivariate regression, correlation, ANOVA, etc).

Regression and structural equation modelling techniques are used to identify practices constituting good and harsh parenting, factors associated with these parenting behaviours and child and adolescent outcomes.

Moreover, although integrative models were tested by using structural - equation modelling or hierarchical regressions to demonstrate the predictive effect of positive youth development on problem behaviour (Jessor et al. 2003; Lent et al. 2005), these cross-sectional studies did not examine the reverse predictive effect of problem behaviour on positive youth development.

Structural equation models and regression analyses accounting for age and sex contributions revealed that emotion dysregulation mediated associations between sociodemographic risk and internalizing symptoms, externalizing problem behavior, and drug use severity, and moderated links between psychosocial risk and internalizing symptoms and externalizing problem behavior.