Final answer:
The correlation coefficient is 0.48. To predict the ear circumference for a coworker who is 35 years old, we use the regression line equation. The slope represents the change in ear circumference for each unit increase in age. The data point (65, 175) is an outlier in the scatterplot.
Step-by-step explanation:
The correlation coefficient measures the strength and direction of the linear relationship between two variables. In this case, the correlation coefficient can be determined by multiplying the slope by the standard deviation of the x variable and dividing it by the standard deviation of the y variable. So, the correlation coefficient is 0.48.
To predict the ear circumference for a coworker who is 35 years old, we can use the regression line equation. The equation is y = mx + b, where y is the ear circumference, x is the age, m is the slope, and b is the y-intercept. Plugging in the values, we get y = 0.48 * 35 + b. Since we don't have the y-intercept value, we cannot calculate the exact predicted ear circumference.
The slope in this context represents the change in ear circumference for each unit increase in age. In other words, for every year older a coworker is, their ear circumference is expected to increase by approximately 0.48 millimeters.
To identify any outliers in the scatterplot, we can look for data points that are far away from the general trend of the data. These are points that do not follow the linear relationship between age and ear circumference. In the given scatterplot, it appears that the data point (65, 175) is an outlier as it deviates significantly from the overall pattern of the data.