This week we continue working with the Normal Distribution and develop ideas essential to Inferential Statistics.

Using the Normal Distribution for estimating probability

Class exercises:
Use either the online applet (linked here) or Excel to calculate probabilities for the worksheet distributed during class (also linked here)

Readings: (repeat from last week)

Stats Stuff: Read the Normal Distribution section up through Empirical rule.
https://www.usu.edu/math/schneit/StatsStuff/Probability/probModels9.html

Rice textbook: Read up through VII section 2: Introduction to Normal Distribution
https://onlinestatbook.com/2/normal_distribution/normal_distribution.html

Using z-score for comparing values within and between datasets; application of Normal Distribution

Class Exercises (linked here) -paper copy distributed during class
link to the normal applet (again)

Readings (same as Monday)

Link to problems from class (3 graded questions)

The Central Limit Theorem: normally distributed datasets are not necessary for predicting means!

Readings:

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