Apparently you have to set some dummy variables =0/1 … Some alternatives are discussed, including ad-hoc bootstrap. For each person in my sample, I have one independent variable (age) and two dependent variables (self rating and peer rating). The other predictor, mental composite score, is continuous and measures one’s mental well-being. The intersection point of two lines is: If the P value for comparing slopes is greater than 0.05. One of the main objectives in linear regression analysis is to test hypotheses about the slope and inter cept of the regression equation. I ran a linear modelregressing “physical composite score” on education and “mental composite score”. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Statistical comparison of the slopes of two regression lines: A tutorial. Copyright © 2014 Elsevier B.V. All rights reserved. Dr. Graciela Estévez-Pérez is a Senior Lecturer of Statistics, Department of Mathematics, University of A Coruña since 1998. Comparing the slopes of two regression lines is an almost daily task in analytical laboratories. Thanks. More recently she created some statistical packages for R Statistical Software and applied them to the treatment of Ecological data. Copyright © 2020 Elsevier B.V. or its licensors or contributors. The hypotheses and a priori assumptions about the intercepts or slopes, or both, will define the full model. Dr. Jose Andrade is a Professor of Analytical Chemistry at the University of A Coruña (Spain) since 2011. Accepted Answer: Oleg Komarov. Such an analysis, when done by a school psychologist, is commonly referred to as a Potthoff (1966) analysis. Presents the fundamentals of the Student’s t-test, with several associated problems. In this work fundamental concepts on the use of the Student’s test were reviewed and Monte Carlo … ... For two independent groups, let (X 1j, Y 1j), ... the difference between the slopes, when there is complete heteroscedasticity. 2. Major emphasis was placed on comparing two slopes. General rules are given to avoid common misunderstandings. Comparing the slopes of two independent regression lines when there is complete heteroscedasticity. This difference in allometric growth should manifest itself as a different slope in both regression lines. As shown below, we usually plot the data values of our dependent variable on the y-axis. In fact, note that the hypothesis of normality of the residuals in the regression models guaranties that b1−b2∈N(β1−β2,σ12/n1sx12+σ22/n1sx22), which yields the statistic … However, SPSS omits the group coded as one. He gives this interesting example. Sample size for line 1: In 2001, she received her Ph.D. at the University of Santiago de Compostela. They are diffrent. between the two slopes divided by the standard error of the difference between the slopes, that is, 1 2 1 2 s b b b b z If your regression program gives you the standard error of the slope (both SAS and SPSS do), the standard error of the difference between the two slopes is most easily computed as .08140 .095942.1258 1 2 1 2 s b b s b s b . Therefore, when you compare the output from the different packages, the results seem to be different. Major emphasis was placed on comparing two slopes. The outcome variable, physical composite score, is a measurement of one’s physical well-being. linearity: each predictor has a linear relation with our outcome variable; r = b sr/s. Linear regression is a commonly used procedure in statistical analysis. Dr. Jose Andrade is a Professor of Analytical Chemistry at the University of A Coruña (Spain) since 2011. These two models were then compared with respect to slopes, intercepts, and scatter about the regression line. 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