assessing Nomality Part 2 of 4 Points: 0 of 1 Save Use the given data values (a sample of female arm circumferences in centimeters) to identify the corresponding z scores that are used for a normal quantile plot, then identify the coordinates of each point in the normal quantile plot. Construct the normal quantile plot, then determine whether the data appear to be from a population with a normal distribution. \[ 323,44.9,40.5,34.2,38.1 \] List the \( z \) scores for the normal quantile plot. \[ -1.28,-0.52,0,0.52,1.28 \] (Round to two decimal places as needed. Use ascending order.) Identify the coordinanss of each point in the normal quantile plot. Use ordered pairs of the form ( \( \mathrm{x}, \mathrm{y} \) ), where x is the sorted arm circumferences, and y is the corresponding z score. \( \square \) \( \square \) \( \square \) \( \square \) ]. ]. \( \square \) (Type ordered pairs. Round to two decimal places as needed. Use ascending order.)

To construct the normal quantile plot with the given arm circumferences and their corresponding z-scores, we first sort the arm circumference data: Sorted arm circumferences: \[ 34.2, 38.1, 40.5, 44.9, 323 \] Matching them with the z-scores: - For \( 34.2 \): it's the lowest value, corresponding to the z-score of \( -1.28 \) - For \( 38.1 \): the second value, corresponding to the z-score of \( -0.52 \) - For \( 40.5 \): the third value, corresponding to the z-score of \( 0.00 \) - For \( 44.9 \): the fourth value, corresponding to the z-score of \( 0.52 \) - For \( 323 \): the highest value, corresponding to the z-score of \( 1.28 \) Thus, the coordinates for the normal quantile plot are: - \( (34.2, -1.28) \) - \( (38.1, -0.52) \) - \( (40.5, 0.00) \) - \( (44.9, 0.52) \) - \( (323, 1.28) \) Formatted as ordered pairs, they are: (34.2, -1.28), (38.1, -0.52), (40.5, 0.00), (44.9, 0.52), (323, 1.28) To determine if the data appears to be from a population with a normal distribution, you would typically look for a linear pattern in the normal quantile plot. If the points closely follow a straight line, then the data can be considered normally distributed. Any significant deviations from linearity would suggest that the data may not come from a normal distribution.