Related Experiment Video
Updated: Oct 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Regression with repeated measures in the experimental units
1Universidad de Buenos Aires, Argentina.
Abstract:
The most satisfactory solution to the problem of modeling a family of regressions with repeated measures in the experimental units is multivariate in nature. However, multivariate methods are difficult to follow and implement. Furthermore, by keeping the focus on the experimental unit, a family of simple univariate linear models will often parallel both the investigator's intuitive grasp of the statistical task at hand. We present two examples based on data from a study of the suckling stimulus during breastfeeding in newborn infants. We show how a family of regression lines can provide useful, if approximate, answers to the questions of interest. One example involves a regression setting proper and the other a typical case of correlation. We discuss alternative univariate models that may be useful for this type of problems.
Related Concept Videos
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Crossover Experiments
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Group Design
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

