Salary Probability and Normal Distribution

Carlea McAvoy explains the empirical rule for finding probabilities, focusing on standard deviations and their percentages. She uses a mean salary example to demonstrate how to calculate probabilities for different salary ranges, emphasizing the areas under the normal curve. The video concludes with a summary of key points.

What is the Empirical Rule primarily used for?

• Calculating the mean of a dataset

• Finding probabilities in a normal distribution

• Determining the mode of a dataset

• Calculating the median of a dataset

What percentage of data falls within one standard deviation in a normal distribution?

• 50%

• 99.7%

• 95%

• 68%

In a normal distribution, what percentage of data falls within two standard deviations?

• 68%

• 95%

• 99.7%

• 50%

What percentage of data falls within three standard deviations in a normal distribution?

• 68%

• 95%

• 99.7%

• 100%

What is the mean salary used in the example?

• $100,000

• $175,000

• $150,000

• $125,000

What is the standard deviation of the salaries in the example?

• $30,000

• $10,000

• $20,000

• $25,000

What percentage of salaries fall between $125,000 and $175,000?

• 50%

• 68%

• 99.7%

• 95%

What is the probability of a salary being more than $175,000?

• 16%

• 32%

• 50%

• 68%

How do you calculate the probability of a salary being more than one standard deviation above the mean?

• Subtract 50% from 100% and divide by 2

• Subtract 68% from 100% and divide by 2

• Subtract 99.7% from 100% and divide by 2

• Subtract 95% from 100% and divide by 2

What percentage of salaries are $100,000 or less?

• 2.5%

• 16%

• 32%

• 5%