MONDAY:

The Central Limit Theorem: regardless of the distribution shape of the raw data, the mean of all possible sample means is equal to the population mean.
Because of the Central Limit Theorem, we can predict the probability of a sample mean. And those sample means will be a good predictor of the population mean.

Class notes: (linked here)
(repeat of Friday)

Practice Problems during class

Exercises from the Rice textbook: #1 - 5, 7, 9, 10, 11, 13, 16, 17

Readings for Central Limit Theorem

(Again, if you haven't read already)

Stats Stuff:
Central Limit -a little heavy, but good background
https://www.usu.edu/math/schneit/StatsStuff/Probability/CLT.html
(You can skip the section on Normal Approximations)
Inference -makes the connection to predicting from samples
https://www.usu.edu/math/schneit/StatsStuff/Inference/inference.html

Read up through Sampling Distributions
this is where the applet demonstrated in class originated
https://www.usu.edu/math/schneit/StatsStuff/Inference/samplingdistributions.html

Rice Textbook:
https://onlinestatbook.com/2/sampling_distributions/intro_samp_dist.html

https://onlinestatbook.com/2/sampling_distributions/samp_dist_mean.html

------------------------
WEDNESDAY:

OPTIONAL: Office Hours during class time.

If you are missing any work or have any misunderstandings, come for class time.

-----------------------
FRIDAY:

Confidence Intervals for the Mean

Class notes (linked here)

Readings:

Stats Stuff:
confidence intervals https://www.usu.edu/math/schneit/StatsStuff/Inference/confidenceintervals.html
NOTE: this link includes the applet that we used in class to demonstrate "levels of confidence"

Rice textbook:
confidence intervals
https://onlinestatbook.com/2/estimation/confidence_ov.html

video: https://onlinestatbook.com/2/estimation/meanM.html

Study Questions started in class (linked here)

Online calculator for CI mean (mu)
Math is Fun (advanced) confidence interval generator