Sentences with phrase «regression line values»

Although the linear regression line values are quite different, the error margins mean that there is considerable overlap between the 95 % confidence limits so the two data sets are in fact in statistical agreement.

Not exact matches

The blue and yellow dots on the regression lines correspond to the relative valuation of the value factor, equal to 0.13 in March 2016, comfortably in the bottom decile of historical relative valuation.

The dashed line shows the regression fit between the two; a lower starting valuation for value relative to growth strongly correlates with higher subsequent five - year performance.

Perhaps it's my age (I remember when I had do do linear regressions with a pencil and paper for the sums, and a slide rule to help with the squares and square roots), but a fundamental principle of a linear least squares regression is that the best fit line passes through the point represented by the mean X and mean Y values.

They then regress ALST on TEX86 as shown in Figure S4, for which the regression residuals have a standard error of 2.1 dC, and then use this line to «forecast» temperature from past values of TEX86 at various depths down the core (not tabulated anywhere, unfortunately).

nobody would suggest doing a regression on that line to arrive at an estimate of the true value.

Or are you testing which of the values (representing regressions) in the population of values is different from a «zero» that is actually the slope of the regression line for the whole period?

A least - squares fit regression line for the simulations (solid line) and the observed seasonal cycle Δαs / ΔTs value based on ISCCP and ERA40 reanalysis (dashed vertical line) are also shown.

For example, the correlation between the variables in Figure 9 - 1 is 0.88, which means that the regression line explains 100 × 0.882 = 77.4 percent of the variability in the temperature values.

Linear regressions are provided (green lines) together with value of the slope and goodness - of - fit (R2).

That value is also in line with F2xCO2 of 4.53 W / m2 estimated from a Gregory - plot regression over the 35 years following an abrupt quadrupling of CO2.

With the effects of other covariates and the classification effects subsumed in the intercept of the equations, the vertical distance between the regression lines represents the estimated mean difference at a given covariate value on the abscissa.

Preliminary analyses using statistical analysis system (SAS Institute, Cary, NC) were carried out to detect any missing values or outliers with large influences on the regression lines.

Note: Lines depict the simple regression line for values of child temperament 1 SD above and below the mean.