regression equation

These interactions can be shown in the regression equation as illustrated by the example below.

Of course, he did this analysis without the benefit of a single regression equation.

I measured the long bones, fed the data into the computer, and ran the regression equations.

In Japan the only - but interesting - clues to technical success come from the regression equation.

Variables were entered into or removed from the logistic regression equation one at a time.

This set of variables can then be expressed by the following regression equation:

Given the form of the regression equation, we can interpret the coefficients as elasticities.

Other regression equations on different data sets are said to be less satisfactory or less powerful if their is lower.

This batch calculation is based on the common linear regression equation:

Another approach has been to include gestational age in a multiple regression equation along with other potential predictors of birth weight.

Then the following regression equation is postulated as a description of the process that generated the data:

This equation can be statistically estimated using the following regression equation:

The research proposed ways to estimate 'happiness' and job-satisfaction regression equations.

In our categorical case we would use a simple regression equation for each group to investigate the simple slopes.

Suppression variables increase the predictive validity of another variable by its inclusion into a regression equation.

Using the past to predict the present: Confidence intervals for regression equations in phylogenetic comparative methods.

The stepwise multiple regression equations, with the reservations already mentioned, are as follows:

The regression equation that is estimated is.

Lucas's study used data from 18 countries, and the regression equation (6.5) was estimated for each of them using annual data over the period 1952-67.

As with many exercise test equations, there have been many regression equations developed that may give varying results.

The relationship between the subcutaneous adipose layer and total body fat in a person is often modelled by using regression equations.

The 'conglomerate' regression equation could be assumed to apply to Panaragua.

The overall regression equation, calibrated by ordinary least squares, relating population to the carrier variables is Table 5.3 gives further details.

The external diameter of the abdominal aorta at 100 mmHg pressure was also estimated from the individual regression equations.

This variation may be addressed in MLM by adding "age" into the regression equation.