Final answer:
Simple linear regression analysis allows you to predict a value of the dependent variable given a value of the independent variable, unlike correlation analysis which only assesses the strength of the relationship. The regression process includes creating a scatter plot, calculating a least-squares line, assessing the significance of the correlation coefficient, and making predictions using the regression equation.
Step-by-step explanation:
Simple linear regression analysis allows you to do several tasks that a correlation analysis does not provide. Firstly, it enables you to predict a value of the response variable given a value of the predictor variable. In contrast, correlation analysis only measures the strength and direction of the linear relationship between two variables.
To perform a linear regression analysis, you generally follow these steps:
- Decide which variable is the independent variable and which is the dependent variable.
- Draw a scatter plot to visualize the data.
- Look at the scatter plot to determine if there appears to be a linear relationship between the variables.
- Calculate the least-squares line and put the equation in the form 'ý = a + bx'.
- Find the correlation coefficient and assess its significance to understand the strength of the linear relationship.
- Use the regression equation to predict values of the dependent variable for given values of the independent variable.
The slope and y-intercept of the regression line provide insight into how changes in the predictor variable affect the response variable. The correlation coefficient, while related to regression, doesn't offer predictive capabilities.
Therefore answer is a. predict a value of the response given a value of the predictor.