This module contains practice problems for the Central Limit Theorem from Collaborative Statistics by Susan Dean and Dr. Barbara Illowsky. The problems relating to Central Limit Theorem for averages have not been changed in this module. The problems relating to Central Limit Theorem for sums have been removed from this module.
Student learning outcomes
The student will explore the properties of data through the Central Limit Theorem.
Given
Yoonie is a personnel manager in a large corporation. Each month she must review 16 of the employees. From past experience, she has found that the reviews take her approximately 4 hours each to do with a population standard deviation of 1.2 hours. Let
$X$ be the random variable representing the time it takes her to complete one review. Assume
$X$ is normally distributed. Let
$\overline{X}$ be the random variable representing the average time to complete the 16 reviews. Let
$\mathrm{\Sigma X}$ be the total time it takes Yoonie to complete all of the month’s reviews.
Distribution
Complete the distributions.
$X$ ~
$\overline{X}$ ~
Graphing probability
For each problem below:
Sketch the graph. Label and scale the horizontal axis. Shade the region corresponding to the probability.
Calculate the value.
Find the probability that
one review will take Yoonie from 3.5 to 4.25 hours.
Find the probability that the
average of a month’s reviews will take Yoonie from 3.5 to 4.25 hrs.
$P\text{( )}=$ _______
0.7499
When calculating the standard deviation for the mean using the Central Limit Theorem in this problem, the sample size is n = 16, the number of reviews she completes in a month.
Find the 95th percentile for the
average time to complete one month’s reviews.
The 95th Percentile=
4.49 hours
Discussion question
What causes the probabilities in
[link] and
[link] to differ?
At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
Source:
OpenStax, Collaborative statistics: custom version modified by r. bloom. OpenStax CNX. Nov 15, 2010 Download for free at http://legacy.cnx.org/content/col10617/1.4
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