In the case of a normal probability curve, the graph/curve is plotted for:

Correct option is C

In a normal probability curve, the graph is plotted for scores and their respective frequencies. This means the x-axis represents the scores or data points, while the y-axis represents the frequency of occurrence of each score. The normal probability curve, also known as the Gaussian curve or bell curve, is symmetrical, unimodal, and depicts the distribution of a dataset where most data points cluster around the mean.

Key features of the normal probability curve include:

1. Symmetry about the mean.
2. The mean, median, and mode coincide at the central peak.
3. Tails of the curve extend infinitely but never touch the x-axis.
4. The area under the curve represents probabilities and equals 1.
5. Standard deviation determines the spread or width of the curve.
6. It is used extensively in inferential statistics to make predictions.

Information Booster:

1. The normal curve is integral to understanding standard deviations and z-scores.
2. Z-scores indicate how many standard deviations a value is from the mean.
3. Nearly 68% of the data lies within one standard deviation from the mean in a normal distribution.
4. Normal distributions model many natural phenomena, such as IQ scores, heights, and exam marks.
5. Kurtosis and skewness measure deviations from the normal curve's shape.
6. The curve helps in hypothesis testing and calculating probabilities.

Additional Information:

• Option (a):Scores and respective frequencies form the basis of the normal curve; most datasets naturally form this pattern.
• Option (b): Scores and deviations relate to measures of variability but not to the graph itself.
• Option (c): Z-scores and respective relative frequencies are used to standardize scores, but they do not define the normal curve directly.
• Option (d): Z-scores and standard deviations help compare different datasets but are not what the curve directly plots.