This Concept introduces students to the standard normal probability distribution.
This video gives more detail about the mathematical principles presented in Normal Distributions.
This video shows how to work step-by-step through one or more of the examples in Normal Distributions.
Explains how the normal distribution relates to standard deviation and z-scores.
Explains how to use the empirical rule, also known as the 68-95-99.7 rule, to calculate certain normal distribution probabilities. That's right! You don't always need a calculator!
Walks through the steps to solve problems involving the normal distribution with use of the empirical rule and z-scores.
Explains how to tell which real world example would follow a normal distribution.
Explains how to find a z-score for a given data point when you know the mean and standard deviation of the entire normal distribution.
Adjusts the empirical rule to determine the area to left or right of certain points on the normal distribution curve.
This lesson plan covers Normal Distributions and includes Teaching Tips, Common Errors, Differentiated Instruction, Enrichment, and Problem Solving.
A list of student-submitted discussion questions for Normal Distributions.
To stress understanding of a concept by summarizing the main idea and applying that understanding to create visual aids and generate questions and comments using a Concept Matrix.
To activate prior knowledge, make personal connections, reflect on key concepts, encourage critical thinking, and assess student knowledge on the topic prior to reading using a Quickwrite.
Students will investigate why and how the SAT is scored to correspond with a normal distribution curve.
Students will investigate why and how the SAT is scored to correspond with a normal distribution curve. Answer Key.
This study guide looks at characteristics of a normal distribution curve, the empirical rule, z-score, standardizing normal curve, and approximating binomial distribution.
These flashcards help you study important terms and vocabulary from Normal Distributions.