Which of the following situations describes a multiple regression?
In the field of statistics, multiple regression is a powerful tool used to analyze the relationship between a dependent variable and two or more independent variables. This technique allows researchers to understand how changes in multiple factors can collectively influence the outcome of interest. Identifying which of the following situations best fits the description of multiple regression can help clarify its application and significance in various research areas.
The correct answer is:
A. A study that examines the effect of income, education level, and gender on salary.
In this scenario, multiple regression is employed to determine how income, education level, and gender collectively impact an individual’s salary. By analyzing these three independent variables, researchers can gain insights into the complex interplay between them and the dependent variable, salary. This example highlights the versatility of multiple regression in accounting for the influence of multiple factors on a single outcome.
Other situations that do not describe multiple regression include:
B. A study that investigates the effect of temperature on the sales of ice cream.
This situation involves a single independent variable (temperature) and a single dependent variable (sales of ice cream). While this is a valuable analysis, it does not fit the definition of multiple regression, which requires the consideration of multiple independent variables.
C. A study that examines the relationship between the number of hours studied and exam scores.
Similar to situation B, this example involves a single independent variable (hours studied) and a single dependent variable (exam scores). Although this relationship is important to understand, it does not demonstrate the use of multiple regression.
D. A study that investigates the impact of age, height, and weight on the risk of developing a particular disease.
This situation involves multiple independent variables (age, height, and weight) and a single dependent variable (risk of developing a disease). While this is a relevant analysis, it does not meet the criteria for multiple regression since the focus is on the combined effect of these variables on the dependent variable, rather than their individual contributions.
In conclusion, multiple regression is a valuable statistical technique for analyzing the complex relationships between multiple independent variables and a dependent variable. By recognizing which situations involve multiple regression, researchers can effectively apply this method to gain a deeper understanding of the factors influencing their outcomes of interest.
