Chebyshev’s Theorem How can you determine whether a distribution is approximately normal? A statistical theorem called Chebyshev’s theorem states that the minimum percent of data between plus and minus K standard deviations from the mean ( K > 1) in any distribution can be found by the formula Minimum percent = 1 − 1 K 2 Thus, for example, between ±2 standard deviations from the mean there will always be a minimum of 75% of data. This minimum percent applies to any distribution For K = 2, Minimum percent = 1 − 1 − 2 = 1 − 1 4 = 3 4 , or 75% Likewise, between ±3 standard deviations from the mean there will always be a minimum of 89% of the data. For K = 3, Minimum percent = 1 − 1 3 2 = 1 − 1 9 = 8 9 , or 89% The following table lists the minimum percent of data in any distribution and the actual percent of data in the normal distribution between ±1.1, ± 1.5, ± 2.0, and ±2.5 standard deviations from the mean. The minimum percents of data m any distribution were calculated by using Chebyshev’s theorem. The actual percents of data for the normal distribution were calculated by using the area given in the standard normal, or z , table. K = 1.1 K = 1.5 K = 2 K = 2.5 Minimum (for any distribution) 17.4% 55.6% 75% 84% Normal distribution 72.9% 86.6% 95.4% 98.8% Given distribution The third row of the chart has been left blank for you to fill in the percents when you reach part (e). Consider the following 30 pieces of data obtained from a quiz. a. Determine the mean of the set of scores. b. Determine the standard deviation of the set of scores. c. Determine the values that correspond to 1.1, 1.5, 2, and 2.5 standard deviations above the mean. Then determine the values that correspond to 1.1, 1.5, 2, and 2. 5 standard deviations below the mean. d. By observing the 30 pieces of data, determine the actual percent of quiz scores between ±1.1 standard deviations from the mean. ±1.5 standard deviations from the mean. ±2 tandard deviations from the mean. ±2.5 standard deviations from the mean. e. Place the percents found in part (d) in the third row of the chart. f. Compare the percents in the third row of the chart with the minimum percents in the first row and the normal percents in the second row, and then make a judgment as to whether this set of 30 scores is approximately normally distributed .
Chebyshev’s Theorem How can you determine whether a distribution is approximately normal? A statistical theorem called Chebyshev’s theorem states that the minimum percent of data between plus and minus K standard deviations from the mean ( K > 1) in any distribution can be found by the formula Minimum percent = 1 − 1 K 2 Thus, for example, between ±2 standard deviations from the mean there will always be a minimum of 75% of data. This minimum percent applies to any distribution For K = 2, Minimum percent = 1 − 1 − 2 = 1 − 1 4 = 3 4 , or 75% Likewise, between ±3 standard deviations from the mean there will always be a minimum of 89% of the data. For K = 3, Minimum percent = 1 − 1 3 2 = 1 − 1 9 = 8 9 , or 89% The following table lists the minimum percent of data in any distribution and the actual percent of data in the normal distribution between ±1.1, ± 1.5, ± 2.0, and ±2.5 standard deviations from the mean. The minimum percents of data m any distribution were calculated by using Chebyshev’s theorem. The actual percents of data for the normal distribution were calculated by using the area given in the standard normal, or z , table. K = 1.1 K = 1.5 K = 2 K = 2.5 Minimum (for any distribution) 17.4% 55.6% 75% 84% Normal distribution 72.9% 86.6% 95.4% 98.8% Given distribution The third row of the chart has been left blank for you to fill in the percents when you reach part (e). Consider the following 30 pieces of data obtained from a quiz. a. Determine the mean of the set of scores. b. Determine the standard deviation of the set of scores. c. Determine the values that correspond to 1.1, 1.5, 2, and 2.5 standard deviations above the mean. Then determine the values that correspond to 1.1, 1.5, 2, and 2. 5 standard deviations below the mean. d. By observing the 30 pieces of data, determine the actual percent of quiz scores between ±1.1 standard deviations from the mean. ±1.5 standard deviations from the mean. ±2 tandard deviations from the mean. ±2.5 standard deviations from the mean. e. Place the percents found in part (d) in the third row of the chart. f. Compare the percents in the third row of the chart with the minimum percents in the first row and the normal percents in the second row, and then make a judgment as to whether this set of 30 scores is approximately normally distributed .
Solution Summary: The author explains how the mean for the 30 pieces of data is 5.33. The standard deviation is s=3.00.
Chebyshev’s Theorem How can you determine whether a distribution is approximately normal? A statistical theorem called Chebyshev’s theorem states that the minimum percent of data between plus and minus K standard deviations from the mean (K > 1) in any distribution can be found by the formula
Minimum percent =
1
−
1
K
2
Thus, for example, between ±2 standard deviations from the mean there will always be a minimum of 75% of data. This minimum percent applies to any distribution For K = 2,
The following table lists the minimum percent of data in any distribution and the actual percent of data in the normal distribution between ±1.1, ± 1.5, ± 2.0, and ±2.5 standard deviations from the mean. The minimum percents of data m any distribution were calculated by using Chebyshev’s theorem. The actual percents of data for the normal distribution were calculated by using the area given in the standard normal, or z, table.
K = 1.1
K = 1.5
K = 2
K = 2.5
Minimum (for any distribution)
17.4%
55.6%
75%
84%
Normal distribution
72.9%
86.6%
95.4%
98.8%
Given distribution
The third row of the chart has been left blank for you to fill in the percents when you reach part (e).
Consider the following 30 pieces of data obtained from a quiz.
a. Determine the mean of the set of scores.
b. Determine the standard deviation of the set of scores.
c. Determine the values that correspond to 1.1, 1.5, 2, and 2.5 standard deviations above the mean. Then determine the values that correspond to 1.1, 1.5, 2, and 2. 5 standard deviations below the mean.
d. By observing the 30 pieces of data, determine the actual percent of quiz scores between
±1.1 standard deviations from the mean.
±1.5 standard deviations from the mean.
±2 tandard deviations from the mean.
±2.5 standard deviations from the mean.
e. Place the percents found in part (d) in the third row of the chart.
f. Compare the percents in the third row of the chart with the minimum percents in the first row and the normal percents in the second row, and then make a judgment as to whether this set of 30 scores is approximately normally distributed.
Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, subject and related others by exploring similar questions and additional content below.
Hypothesis Testing using Confidence Interval Approach; Author: BUM2413 Applied Statistics UMP;https://www.youtube.com/watch?v=Hq1l3e9pLyY;License: Standard YouTube License, CC-BY
Hypothesis Testing - Difference of Two Means - Student's -Distribution & Normal Distribution; Author: The Organic Chemistry Tutor;https://www.youtube.com/watch?v=UcZwyzwWU7o;License: Standard Youtube License