The answer is about 16%
Using the z-score formula(z = (x-u)/sd) the z score is 1.
This means that we want the percentage above 1 standard deviation. We know from the 68-95-99.7 rule that 68 percent of all the data fall between -1 and 1 standard deviation so there must be about 16% that falls above 1 standard deviation.
To determine the percentage of scores between 61 and 82, you would need to know the distribution of the scores (e.g., normal distribution) and the total number of scores. If the data is normally distributed, you can use the mean and standard deviation to find the percentage of scores in that range using a z-score table. Without specific data, it isn't possible to provide an exact percentage.
To determine the percentage of scores between 63 and 90, you would need the complete dataset or a statistical summary (like a frequency distribution or histogram) of the scores. By counting the number of scores within that range and dividing by the total number of scores, then multiplying by 100, you can calculate the percentage. Without specific data, it's impossible to provide an exact percentage.
To determine the percentage of students who score between 400 and 600, you would typically need access to the specific data set or distribution of scores for the students in question. If this data follows a normal distribution, you could use statistical methods to find the percentage based on the mean and standard deviation. Without that data, it's impossible to provide an accurate percentage.
To find the mean percentage score, first add all the individual percentage scores together to get the total score. Then, divide this total by the number of scores to calculate the average percentage. The formula can be expressed as: Mean Percentage Score = (Sum of Percentage Scores) / (Number of Scores). This gives you the average percentage across all the scores.
You can't do this without knowing the distribution of scores.
To determine the percentage of scores between 61 and 82, you would need to know the distribution of the scores (e.g., normal distribution) and the total number of scores. If the data is normally distributed, you can use the mean and standard deviation to find the percentage of scores in that range using a z-score table. Without specific data, it isn't possible to provide an exact percentage.
To determine the percentage of scores between 63 and 90, you would need the complete dataset or a statistical summary (like a frequency distribution or histogram) of the scores. By counting the number of scores within that range and dividing by the total number of scores, then multiplying by 100, you can calculate the percentage. Without specific data, it's impossible to provide an exact percentage.
2
The mean of a distribution of scores is the average.
100%. And that is true for any probability distribution.
99.7% of scores fall within -3 and plus 3 standard deviations around the mean in a normal distribution.
To determine the percentage of students who score between 400 and 600, you would typically need access to the specific data set or distribution of scores for the students in question. If this data follows a normal distribution, you could use statistical methods to find the percentage based on the mean and standard deviation. Without that data, it's impossible to provide an accurate percentage.
3
Credit scores are used to determine loan percentages when a person applies for a loan. If a person has a low credit score, the percentages of interest are higher, whereas higher credit scores result in lower loan percentage rates.
To find the mean percentage score, first add all the individual percentage scores together to get the total score. Then, divide this total by the number of scores to calculate the average percentage. The formula can be expressed as: Mean Percentage Score = (Sum of Percentage Scores) / (Number of Scores). This gives you the average percentage across all the scores.
You can't do this without knowing the distribution of scores.
Tests are curved by adjusting scores to a predetermined distribution, such as a bell curve, to determine final grades. This helps account for variations in difficulty and ensures fairness in grading.