Statistics Normal Distribution

In summary, Eleanor's standardized score is approximately 1.565. Gerald's standardized score is approximately 1.471. Assuming that both tests measure the same kind of ability, Gerald has the higher score.
  • #1
Junior1
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Eleanor scores 680 on the mathematics part of the SAT. The distribution of SAT math scores in recent years has been Normal with mean 547 and standard deviation 85.
Gerald takes the ACT Assessment mathematics test and scores 27. ACT math scores are Normally distributed with mean 21.3 and standard deviation 3.4.

What is Elanor's standardized score? ______ Round to 3 decimal places.
What is Gerald's standardized score?________ Round to 3 decimal places.
Assuming that both tests measure the same kind of ability, who has the higher score?
___Gerald.
___Elanor.
___They both did equally well.
 
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  • #2
Junior said:
Eleanor scores 680 on the mathematics part of the SAT. The distribution of SAT math scores in recent years has been Normal with mean 547 and standard deviation 85.
Gerald takes the ACT Assessment mathematics test and scores 27. ACT math scores are Normally distributed with mean 21.3 and standard deviation 3.4.

What is Elanor's standardized score? ______ Round to 3 decimal places.
What is Gerald's standardized score?________ Round to 3 decimal places.
Assuming that both tests measure the same kind of ability, who has the higher score?
___Gerald.
___Elanor.
___They both did equally well.

Hi Junior! Welcome to MHB! ;)

A standardized score is calculated by taking the score, subtract the mean, and divide by the standard deviation.
What would we get as standardized scores? (Wondering)
 
  • #3
Equivalently, the 'standard score' is the number of standard deviations the score is above or below the mean. Eleanor scored 680 and the mean is 547 so she scored 680- 547= 133 above the mean. If the standard deviation is 85, how many "standard deviations" is that?
 

FAQ: Statistics Normal Distribution

What is the Normal Distribution?

The Normal Distribution, also known as the Gaussian Distribution, is a statistical concept that describes the distribution of a set of data that follows a bell-shaped curve. It is a continuous probability distribution that is symmetrical around the mean and has a predictable pattern of how the data is spread out.

What are the characteristics of the Normal Distribution?

The Normal Distribution has three main characteristics: 1) It is symmetrical around the mean, meaning that the mean, median, and mode of the data are all equal. 2) It follows the 68-95-99.7 rule, which states that approximately 68% of the data falls within one standard deviation of the mean, 95% falls within two standard deviations, and 99.7% falls within three standard deviations. 3) It has a bell-shaped curve, with the majority of the data falling in the middle and fewer data points in the tails.

Why is the Normal Distribution important in statistics?

The Normal Distribution is important in statistics because it is a common pattern that many natural phenomena, such as human height and test scores, follow. It allows us to make predictions and draw conclusions about a population based on a sample of data. It is also used in many statistical tests and models, including the Central Limit Theorem and Regression Analysis.

How do you calculate the probability in a Normal Distribution?

The probability in a Normal Distribution can be calculated using the formula for the Standard Normal Distribution, where the z-score (the number of standard deviations from the mean) is used to find the corresponding probability on a standard normal table. Alternatively, the probability can be calculated using statistical software or an online calculator.

What are some real-world applications of the Normal Distribution?

The Normal Distribution is used in various fields, including finance, biology, psychology, and quality control. Some examples of its real-world applications are: predicting stock market returns, analyzing blood pressure measurements, evaluating student test scores, and monitoring manufacturing processes for consistency and quality. It is also used in decision-making, such as determining cutoff scores for college admissions or setting standards for medical diagnoses.

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