Final answer:
To assess the bivariate relationship between continuous predictor and target variables, identify the independent and dependent variables, draw a scatter plot, calculate the least-squares line, find and interpret the correlation coefficient, and assess if a linear relationship and model fit is appropriate.
Step-by-step explanation:
When assessing the bivariate relationship between a Continuous predictor variable and a Continuous target variable, several steps are followed to understand the nature and strength of their relationship:
- Identify independent and dependent variables: The independent variable (predictor) is the one believed to influence or predict the outcome, while the dependent variable (target) is the outcome we are trying to explain or predict.
- Draw a scatter plot: This visual representation helps in preliminary assessment of the nature of the relationship between variables.
- Inspect the scatter plot to gauge whether there's an apparent relationship between the variables.
- Calculate the least-squares line: This provides the best fit line through the data, represented by the equation ý = a + bx, where 'a' is the y-intercept and 'bx' represents the slope.
- Find the correlation coefficient (r): It quantifies the degree of linear relationship between the variables. A higher absolute value indicates a stronger relationship.
- Interpret the significance of the correlation coefficient: Statistical tests (like a t-test) are used to determine if the coefficient is significantly different from zero, indicating a significant relationship.
- Assess if the linear model is appropriate: Sometimes, data shows a non-linear relationship or suggests the presence of outliers which can influence the overall fit of the model.