Related Experiment Video
Updated: Jun 14, 2026
14:14
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
Published on: May 13, 2022
Linear regression analysis for comparing two measurers or methods of measurement: but which regression?
1The University of Melbourne, Parkville, Victoria, Australia. ludbrook@bigpond.net.au
Clinical and Experimental Pharmacology & Physiology
|March 27, 2010
Summary
Comparing measurement methods requires specialized regression techniques beyond ordinary least squares (OLS). Ordinary least products regression offers a versatile solution for calibration and bias detection in scientific research.
Area of Science:
- Biostatistics
- Method comparison studies
- Measurement error analysis
Background:
- Comparing measurement methods is crucial for calibration and bias detection.
- Ordinary least squares (OLS) regression is often inappropriate due to measurement error in both variables.
- Various regression techniques are favored across different scientific disciplines.
Purpose of the Study:
- To evaluate the suitability of different regression methods for comparing measurement techniques.
- To identify an appropriate regression method for pharmacologists and physiologists.
- To provide guidance on selecting regression models for method comparison.
Main Methods:
- Review of linear regression techniques for method comparison.
- Discussion of limitations of ordinary least squares (OLS) regression.
- Introduction of ordinary least products (OLP) regression as a recommended alternative.
Main Results:
- OLS regression is unsuitable when both variables are subject to error.
- Existing simulation studies on regression techniques are difficult to interpret.
- Ordinary least products (OLP) regression is versatile for calibration and bias detection.
Conclusions:
- Pharmacologists and physiologists should consider ordinary least products (OLP) regression.
- OLP regression is adaptable for both calibration and bias detection.
- OLP regression is accessible via calculators and statistical software.
Related Concept Videos
00:53Correlation and Regression
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
01:25Multiple Regression
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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...
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...
01:11Regression Analysis
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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:
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:
01:21Friedman Two-way Analysis of Variance by Ranks
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
00:54Ratio Level of Measurement
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For...
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For...
01:11Calculating and Interpreting the Linear Correlation Coefficient
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation: