What are the Percentiles and Z-Scores for Bowling Scores?

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In summary, the conversation discusses questions involving percentiles and the use of Z-scores to calculate probabilities. The first question asks about Adrian's bowling scores, with an average of 174 and a standard deviation of 35. The second question involves the top 10% of bowlers in Adrian's league and their average score needed to play in an all-star game. The conversation concludes with the suggestion to use a Z-score table in the back of the textbook to calculate probabilities.
  • #1
majinknight
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Hi i am having trouble with a couple questions involving percentiles, here is the question:

1) Adrian's average bowling score is 174, with a standard deviation of 35.
a) In what percent of games does Adrian score less than 200 points? More than 200 points?
b) The top 10% of bowlers in Adrian's league get to play in an all-star game. If the league average is 170, with a standard deviation of 11 points, what average score does Adrian need to have to obtain a spot in the all-star game?

Thank you.
 
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  • #2
Use the definition of Z-score:

[tex]Z = \frac {x - \bar x}{\sigma}[/tex]

and use your textbook tables for cumulative probability (or words to that effect depending on the authors).
 
  • #3
I figured it out now, you had to use this Z-score table in the back of the textbook which i did not know how to do. Thanks for the help though.
 

1. What is the difference between percentiles and z-scores?

Percentiles and z-scores are both measures of relative position in a data set. However, percentiles are based on the actual values in the data set, while z-scores are standardized values based on the mean and standard deviation of the data.

2. How are percentiles and z-scores calculated?

Percentiles are calculated by finding the value in the data set that corresponds to a certain percentage of the data. For example, the 50th percentile would be the median of the data set. Z-scores are calculated by subtracting the mean from a data point and dividing by the standard deviation.

3. What is the importance of using z-scores in data analysis?

Z-scores allow for easier comparison between different data sets, as they are all standardized to the same scale. This makes it easier to identify outliers and understand the distribution of the data.

4. How do percentiles and z-scores relate to the normal distribution?

Percentiles and z-scores are both based on the normal distribution, which is a bell-shaped curve that represents the distribution of many natural phenomena. Percentiles correspond to specific areas under the curve, while z-scores represent the number of standard deviations a data point is from the mean.

5. Why is it important to understand percentiles and z-scores in statistics?

Percentiles and z-scores are important statistical concepts because they allow us to understand the relative position of a data point within a data set. This can help us make comparisons, identify outliers, and understand the distribution of the data. They are also commonly used in hypothesis testing and confidence intervals in data analysis.

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