Understanding Sample Mean Distribution with TI-84

This video tutorial demonstrates how to use the TI-84 calculator to determine probabilities and percentiles of a sample mean distribution. It covers calculating the mean and standard deviation of sample means, understanding the distribution characteristics using the central limit theorem, and finding probabilities and percentiles using the normal cdf and inverse norm functions on the TI-84.

What tool is used in this example to determine the probability and percentile of a sample mean distribution?

• R Programming

• TI-84 Calculator

• Excel Spreadsheet

• Python Script

What is the mean age of tablet users in the study?

• 25 years

• 40 years

• 34 years

• 30 years

How is the standard deviation of the sample means calculated?

• By adding the standard deviation of the population to the sample size

• By dividing the standard deviation of the population by the square root of the sample size

• By multiplying the standard deviation of the population by the sample size

• By subtracting the standard deviation of the population from the sample size

According to the central limit theorem, what does the distribution of sample means tend to?

• A uniform distribution

• A bimodal distribution

• A skewed distribution

• A normal distribution

What function is used on the TI-84 to determine the probability that the sample mean age is more than 30 years?

• normalpdf

• normalcdf

• binomcdf

• invNorm

What is the lower bound used in the normalcdf function for calculating the probability that the sample mean age is more than 30 years?

• 34

• 9999

• 30

• 0

What is the approximate probability that the sample mean age is more than 30 years?

• 0.9962

• 0.5000

• 0.2500

• 0.7500

What is the 95th percentile for the sample mean age?

• 37.0 years

• 34.0 years

• 35.5 years

• 36.5 years

Which function is used on the TI-84 to find the 95th percentile?

• binomcdf

• normalcdf

• normalpdf

• invNorm

What is the area to the left of k for the 95th percentile?

• 0.05

• 0.95

• 0.50

• 1.00