Main Answer:
(a) Developing a scatter diagram with weight as the independent variable reveals the relationship.
(b) There appears to be a positive correlation between weight and the dependent variable.
Step-by-step explanation:
In creating a scatter diagram with weight as the independent variable, we plot each data point, representing the weight and the corresponding dependent variable. The resulting visualization provides a quick overview of the data distribution. In this case, with weight as the independent variable, the scatter plot suggests a positive correlation. This means that as weight increases, the dependent variable also tends to increase. The points on the diagram tend to form an upward trend, indicating a potential relationship between weight and the dependent variable.
Understanding the scatter diagram's patterns is crucial for making informed interpretations about the relationship between variables. In this context, the positive correlation implies that changes in weight are associated with changes in the dependent variable in the same direction. It's important to note that while a correlation is observed, it doesn't necessarily imply causation. Further analysis and statistical tests would be needed to establish the strength and significance of this relationship.
In summary, the scatter diagram visually illustrates a positive correlation between weight and the dependent variable. This initial observation prompts a closer examination of the data to draw more definitive conclusions about the nature and strength of the relationship.