Calculating Probability for Test Scores: Mean 2.725, SD 1.329, 61 Students

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In summary, the conversation is about a student seeking help with a statistics problem involving finding the probability of achieving an average score of at least 3 on a test with a mean of 2.725 and a standard deviation of 1.329. The solution involves using the Central Limit Theorem and Chebyshev's Inequality to calculate the sampling distribution and probability of achieving this average score. The question asks about the chance of the average score, not the percentage of individual scores that are a certain size.
  • #1
monacom08
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Statistics Need Help !1

I am doing review for a test and i still can not get this problem..HELP Please

The scores on a test had a mean of 2.725 and a standard deviation of 1.329. A teacher had 61 students take the test. Although the students were not random sample, the teacher considered the students to be typical of all the national students. what us the probability that the students achieved an average score of at least 3?

Please help!
 
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  • #2


I'm going to assume that the distribution is a normal distribution.

Have you studied how to find probabilities in a normal distribution? Have you learned about finding probabilities in a standardized normal distribution (ie mean = 0, std dev = 1)? Do you know how to convert your distribution to a standardized normal distribution?
 
  • #3


The point of monacom08's problem is to use the CLT to calculate

[tex]
P(\overline X \ge 3) = 1 - P(\overline X < 3)
[/tex]

so the first step is to find the sampling distribution of [tex] \overline X [/tex].
 
  • #4


Try setting up a solution using Chebycheff's Inequality:

P(|Y - µ| >= kσ) <= 1/k^2

We know that µ is the mean, which equals 2.725, and that σ is the square root of variance (i.e., the standard deviation, which is 1.329).
 
  • #5


Look at this part of the problem: "what us the probability that the students achieved an average score of at least 3? "

The question asks about the chance the average score is a certain size, not the percentage of individual scores that are a certain size.
 
  • #6


statdad said:
The question asks about the chance the average score is a certain size, not the percentage of individual scores that are a certain size.

Is this in regards to what I wrote? I am very much a neophyte of statistics, especially compared to you, but how does the number of students and the idea of a percentage achieving an average located above a certain number change the problem? Is Chebysheff's Inequality a viable means to a solution?
 

Related to Calculating Probability for Test Scores: Mean 2.725, SD 1.329, 61 Students

What is statistics?

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It involves a systematic approach to understanding data and making inferences and predictions based on that data.

Why is statistics important?

Statistics is important because it helps us make sense of large amounts of data and draw meaningful conclusions. It also helps us make informed decisions and predictions based on data, and is essential in fields such as science, medicine, business, and social sciences.

What are the main types of statistics?

The main types of statistics are descriptive statistics, which involves summarizing and describing data, and inferential statistics, which involves making predictions and inferences about a larger population based on a sample of data.

What is the difference between a population and a sample?

A population is the entire group of individuals or subjects that we are interested in studying, while a sample is a smaller subset of the population that is selected for study. Statistics is often used to make inferences about a population based on data from a sample.

How can statistics be misused?

Statistics can be misused in a variety of ways, such as manipulating data to support a certain argument or using biased samples to draw false conclusions. It is important to use statistics carefully and ethically, and to always consider the limitations and potential biases in data and analysis methods.

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