About This Chapter
Overview of Probability in Calculus - Chapter Summary
This fun chapter explores probability as it's used in calculus. These engaging lessons teach you about the process of calculating simple conditional probabilities and the formulas and rules associated with basic probability theory. You'll also review Poisson's distribution and discover how to apply its formula to practice problems. After you have finished this chapter, you should be able to:
- Use the 'At Least Once' rule to solve probability problems
- Calculate probabilities for independent and dependent events
- Differentiate between continuous and discrete variables
- Graph random distributions with random variables
Our professional instructors have created these text and video lessons to provide you with an enjoyable learning experience. All of our materials are accessible on any mobile device, computer or tablet any time of day. Use the printable transcripts as helpful offline study guides. If you need assistance with the lessons, please contact an instructor for help.
1. How to Calculate Simple Conditional Probabilities
Conditional probability, just like it sounds, is a probability that happens on the condition of a previous event occurring. To calculate conditional probabilities, we must first consider the effects of the previous event on the current event.
2. Basic Probability Theory: Rules & Formulas
This lesson contains probability basics and rules, as well as the fundamental law of total probability and Bayes' theorem. Explore these important concepts and then see if you can answer the questions in the follow-up quiz.
3. Probability of Independent Events: The 'At Least One' Rule
Occasionally when calculating independent events, it is only important that the event happens once. This is referred to as the 'At Least One' Rule. To calculate this type of problem, we will use the process of complementary events to find the probability of our event occurring at least once.
4. Probability of Independent and Dependent Events
Sometimes probabilities need to be calculated when more than one event occurs. These types of compound events are called independent and dependent events. Through this lesson, we will look at some real-world examples of how to calculate these probabilities.
5. Graphing Probability Distributions Associated with Random Variables
What's a random variable? Does it have anything to do with gambling? What's the difference between a continuous and a discrete variable? This lesson explains the difference and how to graph each one.
6. Poisson Distribution: Definition, Formula & Examples
In this lesson, we'll use a hypothetical road trip and some other real-life examples to show you how to use the Poisson distribution, a formula for calculating the probability of events. Then, test what you learned with the quiz questions.
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Other chapters within the BITSAT Exam - Math: Study Guide & Test Prep course
- Expressions & Reasoning in Math
- Complex Numbers & Polynomials in Algebra
- Introduction to Quadratics
- Working with Quadratic Functions Overview
- Mathematical Sequences & Series Overview
- Series & Sequences Application
- Exponential Functions & Logarithmic Equations
- Binomial Theorem Overview
- Overview of Matrices & Determinants
- Sets & Relations in Math
- Introduction to Trigonometric Functions
- Trigonometric Identities Overview
- Real World Trigonometric Applications
- Operating with Functions
- Solving Inequalities
- Two-Dimensional Coordinate Geometry
- Straight Lines & Angles in Coordinate Geometry
- Circular Arc & Circles in Coordinate Geometry
- Conic Sections in Coordinate Geometry
- Three-Dimensional Coordinate Geometry
- Limits & Continuity in Differential Calculus
- Calculating Derivatives
- Differentiable Functions & Min-Max Problems
- Differentiability in Integral Calculus
- Vectors & Scalars in Math
- Dispersion & Frequency Distributions in Statistics
- Linear Programming & Systems of Equations
- Definite Integrals
- Permutation & Combination
- Mathematical Modeling Overview
- BITSAT Exam - Math Flashcards