Final answer:
Both Analyst 1 and Analyst 2 are correct. The dependent variable in linear regression is the variable that the regression model tries to explain, while the independent variables are the variables that a regression model uses to explain the dependent variable.
Step-by-step explanation:
Both Analyst 1 and Analyst 2 are correct. In linear regression, the dependent variable is the variable that the regression model tries to explain, while the independent variables are the variables that a regression model uses to explain the dependent variable. The dependent variable is also sometimes referred to as the response variable, while the independent variables are also called predictor variables or explanatory variables.
For example, if you are studying the relationship between study time and exam scores, the dependent variable would be the exam score and the independent variable would be the study time.
To illustrate this, let's consider a simple linear regression model. Suppose we want to predict a student's test score based on the number of hours they studied. In this case, the dependent variable would be the test score, and the independent variable would be the number of hours studied. The regression model would then try to find the line of best fit that represents the relationship between the test score (dependent variable) and the number of hours studied (independent variable).