Final answer:
The output of linear regression analysis can be interpreted by considering the coefficient of determination, slope of the regression equation, line of best fit, and outliers.
These hints provide insights into the relationship between variables and how well the regression line fits the data.
Step-by-step explanation:
Here are some hints on how to interpret the output of a linear regression analysis which provides multiple numbers that can be interpreted to gain insights into the relationship between variables:
- Coefficient of determination: The coefficient of determination, also known as R-squared, measures the proportion of the variance in the dependent variable that can be explained by the independent variable. It ranges from 0 to 1, where a value closer to 1 indicates a stronger relationship.
- Slope of the regression equation: The slope represents the rate of change in the dependent variable for each unit increase in the independent variable. It tells us the direction and magnitude of the relationship between the variables.
- Line of best fit: The line of best fit can be used to estimate values for specific input values. By plugging in the input values into the regression equation, you can obtain estimates for the dependent variable.
- Outliers: Outliers are data points that deviate significantly from the general pattern of the data. They can have a large impact on the regression analysis results and should be investigated further.