Lines through the origin—is NO INT the answer?

The fitting of a straight line that must pass through (0, 0) is a frequently encountered application of linear regression. Many computer packages provide a least-squares option sometimes identified as NO INT, or even NOINT, or as ‘suppressing the intercept’. Often these procedures are invalid or do not correspond to the most suitable model. This paper questions the least-squares-line approach and suggests some alternatives. Two measures are proposed for comparing the fit of alternative models.     

State of the unions: Assessment by elite American labor leaders

One hundred and sixty-four elite union leaders in the United States completed a survey to assess the condition of the American labor movement and the factors affecting it. The respondents included high-level international union and state federation officers, central labor presidents, and 58 shop stewards. This study compares the responses of these labor officials and finds that they have similar, negative assessments of the condition of the American labor movement. The study also finds that these leaders agree that the most important factors affecting the labor movement are collective bargaining rights, union leadership, union member solidarity, and the NLRB.     

Assessment of influence in non-linear measurement error models

We consider the assessment of outliers and influential observation in non-linear measurement error models. Residuals, leverage measures and case-deletiondiagonostics are examined. The method of local influence is also applied to the models. In particular, the perturbation of measurement error variances has been found useful in assessing the adequancy of the model assumptions. A numerical example is given to illustrate the application of the diagonostics.     

A puzzlement: health-related organisations in the Nonprofit Almanac

Conclusion Data systems created with one purpose inevitably have shortcomings when used for other purposes, so it is not surprising that a database compiled from informational tax returns has serious flaws when used to compile a statistical picture of the non-profit sector. The two-digit NTEE level of aggregation that is published in theAlmanac conceals many problems that appear when the IRS-based data are broken down to the three-digit level.Identification of the problems in the NTEE and in the use of the IRS data is a necessary step toward their correction. At this point, it is clear that the IRS-based numbers in theAlmanac should be regarded, at best, as rough approximations and as a starting point for additional work. My assessment of theAlmanac suggests that, at least as far as health care is concerned, the NTEE needs revision. Correcting the problems with the IRS data will be much, much more difficult.I am grateful for assistance from Steve Noga, Chris Toppe and Virginia Hodgkinson at Independent Sector.