Aly Moosa

Kanya Shah

The main concepts you need to grasp from this section are the basics of probability and the complement rule. Make sure you understand these key ideas 🔐!

A **probability model** is a description of some chance process that is made up of two portions: a list of all possible outcomes and the probability for each outcome. The list of all possible outcomes is called a **sample space.**

- If all outcomes in a sample space are equally likely, the probability that event A occurs is...
**P(A) =***number of outcomes in event A/ total number of outcomes in the sample space.*

- The probability of any event is a number between 0 and 1 (proportion).
- All possible outcomes
**MUST**add up to**1**. - The probability that an event does not occur is 1 minus the probability that it does occur. In other words, this refers to the
**complement rule**which says that P(AC) = 1- P(A) where P(AC) is the complement of event A or the probability that event A doesn’t occur. (*Understand how to solve problems using this rule given a scenario mentioning “at least one.”)

🎥**Watch: AP Stats - ****Probability Rules and Random Variables**

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Unit 1: Exploring One-Variable Data

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Unit 2: Exploring Two-Variable Data

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Unit 3: Collecting Data

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Unit 4: Probability, Random Variables, and Probability Distributions

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Unit 5: Sampling Distributions

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Unit 6: Inference for Categorical Data: Proportions

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Unit 7: Inference for Qualitative Data: Means

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Unit 8: Inference for Categorical Data: Chi-Square

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Unit 9: Inference for Quantitative Data: Slopes

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