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Related Concept Videos

Regression Analysis01:11

Regression 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:
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
Multiple Regression01:25

Multiple 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...
Variation01:19

Variation

An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
Correlation and Regression00:53

Correlation 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...
Residual Plots01:07

Residual Plots

A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...

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Understanding regression analysis: what is inside the box?

Fatih Aktoz1, Nikolaos P Polyzos2, Christophe Blockeel3

  • 1Department of Obstetrics and Gynecology, Memorial Bahcelievler Hospital, Istanbul, Turkey.

Reproductive Biomedicine Online
|July 9, 2026
PubMed
Summary

Multivariable regression in IVF research helps compare treatments and account for patient differences. This guide clarifies complex statistical methods for clinicians, improving understanding of adjusted results in reproductive medicine.

Keywords:
AdjustmentConfoundingIVFOdds ratioRegression analysis

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Area of Science:

  • Reproductive Medicine
  • Biostatistics

Background:

  • Advanced statistical analyses, particularly multivariable regression, are increasingly used in reproductive medicine to compare in vitro fertilization (IVF) treatments.
  • These methods allow for adjustment of baseline differences between patients, improving the ability to compare treatment strategies and analyze complex data.

Purpose of the Study:

  • To demystify regression analysis in IVF research for clinicians.
  • To clarify the meaning of 'adjusted' results and the capabilities and limitations of regression models.
  • To enhance the interpretation of statistical findings in daily clinical practice.

Main Methods:

  • The manuscript explains the underlying logic of regression analysis as applied to IVF research.
  • It defines and discusses key statistical terms like covariates, confounders, mediators, and colliders.
  • Focus is on conceptual understanding rather than complex mathematical derivations.

Main Results:

  • Regression analysis is crucial for understanding IVF outcomes influenced by multiple factors (e.g., age, ovarian reserve, embryo quality).
  • Understanding regression helps clinicians interpret studies reporting adjusted results.
  • Clarification of statistical concepts bridges the gap between research and clinical application.

Conclusions:

  • Regression analysis provides valuable insights into IVF treatment effectiveness by controlling for confounding variables.
  • Clinicians need a clear understanding of these statistical methods to accurately interpret research and apply findings.
  • This work aims to empower clinicians to use and interpret regression-adjusted data with confidence and caution.