A normal distribution is a symmetric bell-shaped probability distribution characterized by its mean and standard deviation. It follows a specific mathematical formula called Gaussian distribution.
Imagine you are measuring the heights of all the students in your school. If you were to plot a graph with height on the x-axis and the number of students on the y-axis, it would form a bell-shaped curve. This is similar to a normal distribution because most students' heights will be around the average, with fewer students being significantly taller or shorter.
Standard Deviation: A measure of how spread out or dispersed data points are from the mean in a distribution.
Z-Score: A standardized value that represents how many standard deviations an individual data point is away from the mean in a normal distribution.
Central Limit Theorem: States that when independent random variables are added together, their sum tends to follow a normal distribution, regardless of the shape of their original distributions.
AP Statistics - 1.10 The Normal Distribution
AP Statistics - 4.7 Introduction to Random Variables and Probability Distributions
AP Statistics - 5.2 The Normal Distribution, Revisited
AP Statistics - 5.3 The Central Limit Theorem
AP Statistics - 5.5 Sampling Distributions for Sample Proportions
AP Statistics - 5.6 Sampling Distributions for Differences in Sample Proportions
AP Statistics - 5.7 Sampling Distributions for Sample Means
AP Statistics - 5.8 Sampling Distributions for Differences in Sample Means
AP Statistics - 6.1 Introducing Statistics: Why Be Normal?
AP Statistics - 7.2 Constructing a Confidence Interval for a Population Mean
AP Statistics - 7.3 Justifying a Claim About a Population Mean Based on a Confidence Interval
AP Statistics - 7.8 Setting Up a Test for the Difference of Two Population Means
AP Statistics - 7.10 Skills Focus: Selecting, Implementing, and Communicating Inference Procedures
AP Statistics - 9.4 Setting Up a Test for the Slope of a Regression Model
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