'Complement rule' presentation slideshows

Random Variable Overview

Random Variable Overview. What are random variables? Intro to probability distributions Discrete Continuous Linear transformations of RVs Combinations of RVs. What are random variables?. Let X represent a quantitative variable that is measured or observed in an experiment.

797 views • 44 slides

A Survey of Probability Concepts

A Survey of Probability Concepts. Chapter 5. GOALS. Define probability . Describe the classical, empirical, and subjective approaches to probability. Explain the terms experiment, event, outcome, permutations, and combinations .

670 views • 41 slides

Section 7.1 Discrete and Continuous Random Variables

Section 7.1 Discrete and Continuous Random Variables. AP Statistics NPHS Miss Rose. Some Review:. In order to be a VALID probability distribution, the probabilities of each event must add up to ____. Every probability is a number between __ and ___.

537 views • 41 slides

Probability and Sampling Distributions

Probability and Sampling Distributions. Randomness. There are 117,000,000 households in America (amazing what I learn when I read!). If a sample of 57,000 households is undertaken and household income is obtained from each, then we could calculate the sample mean income level.

486 views • 40 slides

From Randomness to Probability

596 views • 31 slides

Introduction to Probability

Introduction to Probability. Ginger Holmes Rowell, Middle TN State University MSP Workshop June 2007. Objectives. Understand the concepts of sample space and probability distribution and construct sample spaces and distributions in simple cases

405 views • 31 slides

Probability Models

Probability Models. Section 6.2. Probability Models. The sample space S of a random phenomenon is the set of all possible outcomes. The event is any outcome or a set of outcomes of a random phenomenon. That is, an event is a subset of the sample space.

410 views • 26 slides

Intro to Probability

368 views • 24 slides

Random Variables and Probability Distributions Chapter 4

518 views • 18 slides

II. BINOMIAL DISTRIBUTIONS A. Binomial Experiments

II. BINOMIAL DISTRIBUTIONS A. Binomial Experiments 1. A binomial experiment is a probability experiment that satisfies the following conditions: a. The experiment is repeated for a fixed number of independent trials.

344 views • 16 slides

4.3-4.4: Binomial Distributions

CHS Statistics. 4.3-4.4: Binomial Distributions. Objective : To solve multistep probability tasks with the concept of binomial distribution. Warm-Up.

511 views • 16 slides

Probability and Sample space

Probability and Sample space. We call a phenomenon random if individual outcomes are uncertain but there is a regular distribution of outcomes in a large number of repetitions.

283 views • 15 slides

A Survey of Probability Concepts

A Survey of Probability Concepts. Chapter 5. GOALS. Define probability . Describe the classical, empirical , and subjective approaches to probability. Explain the terms experiment, event, outcome, permutations , and combinations .

1.46k views • 14 slides

Section 5.2 The Addition Rule and Complements

Section 5.2 The Addition Rule and Complements. KEY IDEA All disjoint events in a sample space sum to 1. Two events are disjoint if they have no outcomes in common. Disjoint events are also called mutually exclusive events. Example: Toss a coin. H and T are disjoint.

327 views • 13 slides