Which analysis would be most appropriate in a study examining both weight and blood pressure?

In a study examining both weight and blood pressure, factorial analysis is the most appropriate choice because it allows researchers to analyze the relationship between two or more independent variables and their effect on dependent variables. In this context, weight and blood pressure can be treated as dependent variables that might be influenced by various factors, such as lifestyle or medication.

Factorial analysis is particularly useful when researchers want to understand interactions between multiple factors. For example, it could reveal how different weight categories (e.g., underweight, normal weight, overweight, obese) might influence blood pressure readings more distinctly. This type of analysis can accommodate complex relationships, providing richer insights compared to other methods.

In contrast, options like the t-test and ANOVA are typically utilized for comparing means across groups. A t-test assesses differences between two groups, while ANOVA extends this to three or more groups but does not inherently analyze relationships between two continuous dependent variables. Similarly, the chi-square test is used primarily for categorical data analysis and would not be suitable for continuous variables such as weight and blood pressure. Thus, factorial analysis stands out as the most fitting method for this study's objectives.