(a) The sketches (described here with ASCII representations for clarity, not to scale) illustrate the relationships:

(i) Negative Skew: The distribution has a longer tail on the left. The mode is to the right of the median, which is to the right of the mean (mean < median < mode).

(ii) Positive Skew: The distribution has a longer tail on the right. The mode is to the left of the median, which is to the left of the mean (mode < median < mean).

(iii) Symmetric: The distribution is bell-shaped with no skew. Mean, median, and mode coincide at the center.

(b) The data values are: 0, 0, 19.99, 32.98, 19.99, 19.99, 48.20, 32.98, 0, 19.99, 0, 32.98, 19.99, 0, 24.50, 0, 32.98, 24.50

(i) Mean: Sum = (6 × 0) + (5 × 19.99) + (2 × 24.50) + (4 × 32.98) + (1 × 48.20) = 0 + 99.95 + 49.00 + 131.92 + 48.20 = 329.07
Mean = 329.07 / 18 ≈ 18.28 GHS
To arrive at the solution: Add all values and divide by the number of observations (18).

(ii) Median: Sorted data: 0, 0, 0, 0, 0, 0, 19.99, 19.99, 19.99, 19.99, 19.99, 24.50, 24.50, 32.98, 32.98, 32.98, 32.98, 48.20
For even number (18), median is average of 9th and 10th values: (19.99 + 19.99)/2 = 19.99 GHS
To arrive at the solution: Sort the data in ascending order and find the middle value(s).

(iii) Mode: The value 0 appears 6 times (most frequent).
To arrive at the solution: Identify the value with the highest frequency.

(c) The calculated measures are mean ≈ 18.28 GHS, median = 19.99 GHS, and mode = 0 GHS. Comparing to part (a), the relationship is mode (0) < mean (18.28) < median (19.99), which does not perfectly match the standard patterns but is closest to a positive skew where mode < median < mean (though here mean < median, the slight discrepancy arises from the clustering and outlier). The skewness coefficient is approximately 0.06 (slightly positive), indicating a mild positive skew with a longer right tail due to the outlier at 48.20 GHS pulling the mean slightly, but the many low values at 0 create a pull leftward. In practical banking terms, such as analyzing customer spending patterns at a student-focused microfinance unit in a Ghanaian bank like GCB Bank, this distribution suggests most students spend little or nothing on textbooks (possibly relying on digital or shared resources), with a few higher spenders. This insight could inform targeted financial products, like low-interest loans for educational materials, ensuring compliance with BoG’s consumer protection directives under Act 930 for fair lending practices.