1. The problem asks to identify the independent and dependent variables, determine the strength of the relationship, and discuss the results. 2. **Independent variable**: This is the variable that is controlled or manipulated in an experiment or analysis. It is the input or cause. 3. **Dependent variable**: This is the variable that is measured or observed. It depends on the independent variable and is the output or effect. 4. To determine the strength of the relationship between variables, we typically use correlation coefficients or regression analysis. 5. The strength of the relationship is measured by the correlation coefficient $r$, which ranges from $-1$ to $1$: - $r = 1$ means a perfect positive linear relationship. - $r = -1$ means a perfect negative linear relationship. - $r = 0$ means no linear relationship. 6. The closer $|r|$ is to $1$, the stronger the relationship. 7. Without specific data or context, we cannot calculate $r$ or discuss the exact strength. 8. In general, if the dependent variable changes consistently with the independent variable, the relationship is strong. 9. The results should be interpreted in context, considering the variables' nature and the data's variability. 10. Summary: - Independent variable: the cause or input. - Dependent variable: the effect or output. - Strength of relationship: measured by correlation coefficient $r$. - Discussion depends on data and context. Final answer: Independent variable is the input or cause; dependent variable is the output or effect; strength of relationship is measured by correlation coefficient $r$; interpretation depends on data and context.