There is a set of points in the plane whose elements correspond to the observations that are used to generate a simple least-squares regression line. Each value of the independent variable in the observations matches up with one of these points, which are called pivot or fixed points. The coordinates of the fixed points are derived, and the properties of the points are explored. All points in the plane that yield each of the fixed points are found. The role that fixed points play in regression diagnostics is investigated. A new mechanical device that uses linkages to model the role of fixed points is described. A numerical example is presented.
References
[1]
Lutzer, C.V. and Farnsworth, D.L. (2021) Pivot Points in Bivariate Linear Regression. Open Journal of Statistics, 11, 393-399. https://doi.org/10.4236/ojs.2021.113023
Kutner, M.H., Nachtsheim, C.J., Neter, J. and Li, W. (2005) Applied Linear Statistical Models. 5th Edition, McGraw-Hill/Irwin. https://doi.org/10.2307/1269508
[4]
Hoaglin, D.C. (1988) Using Leverage and Influence to Introduce Regression Diagnostics. The College Mathematics Journal, 19, 387-416. https://doi.org/10.1080/07468342.1988.11973146
[5]
Chatterjee, S. and Hadi, A.S. (1986) Influential Observations, High Leverage Points, and Outliers in Linear Regression. Statistical Science, 1, 379-393. https://doi.org/10.1214/ss/1177013622
[6]
Chatterjee, S. and Hadi, A.S. (1988). Sensitivity Analysis in Linear Regression. Wiley. https://doi.org/10.1002/9780470316764
[7]
Carpenter J.R. and Kenward, M.G. (2015) Development of Methods for the Analysis of Partially Observed Data and Critique of Ad Hoc Methods. In: Molenberghs, G., Fitzmaurice, G., Kenward, M.G., Tsiatis, A. and Verbeke, G., Eds., Handbook of Missing Data Methodology, Chapman & Hall/CRC Press, 23-46. https://doi.org/10.1201/b17622
[8]
Little, R. and Rubin, D. (2019) Statistical Analysis with Missing Data. 3rd Edition, Wiley. https://doi.org/10.1002/9781119482260
[9]
David L. Farnsworth, (2024) Analysis of Data Containing Outliers. Journal of Probability and Statistical Science, 22, 99-105. https://doi.org/10.37119/jpss2024.v22i1.794
[10]
Harrell, Jr. (2015) Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd Edition, Springer. https://doi.org/10.1007/978-3-319-19425-7
[11]
Lutzer, C.V. (2017) A Curious Feature of Regression. The College Mathematics Journal, 48, 189-198. https://doi.org/10.4169/college.math.j.48.3.189