9: Normal Distribution
- Page ID
- 113191
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- 9.1: The Normal Distribution
- When graphing the data from each of the examples in the introduction, the distributions from each of these situations would be mound-shaped and mostly symmetric. A normal distribution is a perfectly symmetric, mound-shaped distribution. It is commonly referred to the as a normal curve, or bell curve. Because so many real data sets closely approximate a normal distribution, we can use the idealized normal curve to learn a great deal about such data.
- 9.2: The Standard Normal Distribution
- A standard normal random variable \(Z\) is a normally distributed random variable with mean \(\mu =0\) and standard deviation \(\sigma =1\).
- 9.3: Probability Computations for General Normal Distributions
- Probabilities for a general normal random variable are computed after converting \(x\)-values to \(z\)-scores.
- 9.4: Applications of Normal Distributions
- The normal distribution is the foundation for statistical inference and will be an essential part of many of those topics in later chapters. In the meantime, this section will cover some of the types of questions that can be answered using the properties of a normal distribution. The first examples deal with more theoretical questions that will help you master basic understandings and computational skills, while the later problems will provide examples with real data, or at least a real context.
- 9.7: Exercises
- This page contains 14 exercise problems related to the material from Chapter 11.
- 9.E: Continuous Random Variables (Exercises)
- hese are homework exercises to accompany the Textmap created for "Introductory Statistics" by Shafer and Zhang.