Computing and Interpreting z-Scores In Exercises 39 and 40, (a) find the z-score that corresponds to each value and (b) determine whether any of the values are unusual.
Stanford-Binet IQ Scores The test scores for the Stanford-Binet Intelligence Scale are normally distributed with a mean score of 100 and a standard deviation of 16. The test scores of four students selected at random are 98, 65, 106, and 124.
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Step 1: Recall the formula for calculating a z-score: , where is the data value, is the mean, and is the standard deviation.
Step 2: Substitute the given values into the formula for each test score. The mean is 100, and the standard deviation is 16. For example, for the first score of 98, calculate .
Step 3: Repeat the calculation for the other scores: 65, 106, and 124. For each score, use the formula and substitute the respective values.
Step 4: Determine whether any of the z-scores are unusual. A z-score is considered unusual if it is less than -2 or greater than 2. Compare each calculated z-score to this threshold.
Step 5: Interpret the results. For any z-scores that are unusual, explain what this means in the context of the problem (e.g., the corresponding test score is significantly different from the mean).
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
z-Score
A z-score is a statistical measurement that describes a value's relationship to the mean of a group of values. It is calculated by subtracting the mean from the value and then dividing by the standard deviation. A z-score indicates how many standard deviations an element is from the mean, allowing for comparison across different datasets.
Z-Scores From Given Probability - TI-84 (CE) Calculator
Normal Distribution
Normal distribution is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In a normal distribution, approximately 68% of the data falls within one standard deviation of the mean, 95% within two, and 99.7% within three, which is known as the empirical rule.
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Unusual Values
In statistics, a value is considered unusual if it lies more than two standard deviations away from the mean in a normal distribution. This threshold helps identify outliers or extreme values that may warrant further investigation, as they can significantly impact the results and interpretations of the data.