Final answer:
To explore the relationship between age and glucose levels in the 'pima' dataset using ggplot2, one must first decide on the variables, create a scatter plot, determine if there's a relationship, calculate the best-fit line, find the correlation coefficient, make predictions, and evaluate the fit of the model.
Step-by-step explanation:
To visualize the association between age categories and the glucose (glu) field from the 'pima' dataset using ggplot2, follow these steps:
- Decide the independent and dependent variables: Age will be the independent variable (x-axis) and glucose levels will be the dependent variable (y-axis).
- Draw a scatter plot: Use ggplot2 to create a scatter plot with age on the x-axis and glucose levels on the y-axis.
- C: Inspect the plot to determine if there is an apparent relationship between the variables.
- Calculate the least-squares line: Find the best-fit line using the least-squares method and put the equation in the form ý = a + bx.
- Find the correlation coefficient: Calculate the correlation coefficient to determine the strength and direction of the relationship.
- F: Use the equation of the line to estimate the average glucose level for specific age categories.
- G: Assess if a linear model is appropriate for the data based on the scatter plot and correlation coefficient.