Rejoinder: An Ancillarity Paradox which Appears in Multiple Linear Regression

Some Modifications to Calculate Regression Coefficients in Multiple Linear Regression

In a multiple linear regression model, there are instances where one has to update the regression parameters. In such models as new data become available, by adding one row to the design matrix, the least-squares estimates for the parameters must be updated to reflect the impact of the new data. We will modify two existing methods of calculating regression coefficients in multiple linear regres...

Linear Regression Under Multiple

This dissertation studies the least squares estimator of a trend parameter in a simple linear regression model with multiple changepoints when the changepoint times are known. The error component in the model is allowed to be autocorrelated. The least squares estimator of the trend and the variance of the trend estimator are derived. Consistency and asymptotic normality of the trend estimator a...

Multiple linear regression as an analytical tool in cephalometric studies.

When the effect is studied of a factor like 'orthodontic therapy' on linear craniofacial growth, the concomitant consequence of age and gender on size cannot be ignored. The methodologically correct solution is division of the study group into smaller units, each of which is homogeneous with respect to age, gender, and therapy, and to compare these with matched controls. Yet, apart from matched...

Solving the Paradox of Multiple IRR\'s in Engineering Economic Problems by Choosing an Optimal -cut

Until now single values of IRR are traditionally used to estimate the time value of cash flows. Since uncertainty exists in estimating cost data, the resulting decision may not be reliable. The most commonly cited drawbacks to using the internal rate of return in evaluatton of deterministic cash flow streams is the possibility of multiple conflicting internal rates of return. In this paper we p...

Part II Multiple Linear Regression