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Poisson Distribution: Examples, Uses, and Applications

Statistics: Master Data Analysis & Probability

Set Theory

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Set notation

Understanding How to Use Set Notation

Set builder notation

Intersection and union of 2 sets

Intersection and Union of 2 Sets

Intersection and union of 3 sets

Intersection and Union of 3 Sets

Basic Concepts

Classification of data

Characteristics of Quantitative Data

Classify the Level of Measurement

Identify the Level of Measurement

Sampling methods

Sampling Methods

Census and bias

Census and Bias

Classifying Bias

Data Representation

Frequency distribution and histograms

Stem and leaf plots

The Leaf and Stem of Numbers

Recognizing the Leaf and Stem of Numbers

Recognizing The Leaf And Stem of Numbers Leaf

Recognizing The Leaf And Stem of Numbers Stem

Reading Stem and Leaf Plots — Median

Reading Stem and Leaf Plots — Range

stem and leaf plots Range

Reading Stem and Leaf Plots

Reading Stem and Leaf Plots — Mean

Reading Stem and Leaf Plots — Mode

Frequency polygons

Shapes of distributions

Data Interpretation

Center of a data set: mean, median, mode

Spread of a data set - standard deviation & variance

Measures of relative standing - z-score, quartiles, percentiles

Bivariate, scatter plots and correlation

Regression analysis

Equation of the best fit line

Discrete Probabilities

Probability distribution - histogram, mean, variance & standard deviation

Binomial distribution

Binomial Distribution

Mean and standard deviation of binomial distribution

Interpreting Mean and Standard Deviation of Binomial

Poisson distribution

Poisson Distribution

Geometric distribution

Negative binomial distribution

Negative Binomial Distribution

Determining the Negative Binomial Distribution

Determining the Cumulative Negative Binomial Distribution

Hypergeometric distribution

Determining the Cumulative Hypergeometric Distribution

Hypergeometric Distribution

Properties of expectation

Properties of Expectation and Variance

Properties of Expectation

Normal Distribution and Z-score

Introduction to normal distribution

The Normal Distribution and the 68-95-99.7 Rule

Using "invNorm" to Solve Normal Distribution Problems

Normal distribution and continuous random variable

Z-scores and random continuous variables

Sampling distributions

Understanding Proportions and Sample Size

Central limit theorem

Central Limit Theorem

Applying the Central Limit Theorem

Rare event rule

The Rare Event Rule for the Central Limit Theorem

Confidence Intervals

Point estimates

Confidence levels and critical values

Margin of error

Margin of Error

Making a confidence interval

Chi-Squared confidence intervals

Chi-Squared Confidence Intervals

Confidence intervals to estimate population mean

Student's t-distribution

Student"s t-distribution

Determining a Confidence Interval for a Population Mean using t-distributions

Combinatorics

Fundamental counting principle

Fundamental Counting Principle Involving Restrictions

Factorial notation

Permutations

Combinations

Problems involving both permutations and combinations

Permutation vs. Combination

Probability

Addition rule for 

Addition Rule for "OR"

Addition rule for "OR"

Multiplication rule for 

Multiplication rule for "AND"

Multiplication Rule for "AND"

Conditional probability

Probability with permutations and combinations

Probability involving permutations and combinations

Law of total probability

Law of Total Probability

Probability of a randomly selected employee meeting a sales target

Probability of a randomly selected driver being involved in an accident

Probability of a randomly selected customer making a purchase

Probability of a randomly selected person being overweight

Bayes' rule

Bayes" Rule

Bayes rule

Bayes" rule

Bayes' Rule

Probability with Venn diagrams

Hypothesis Testing

Null hypothesis and alternative hypothesis

Proving claims

Confidence levels, significance levels and critical values

Test statistics

Traditional hypothesis testing

Traditional Mean Hypothesis Testing

P-value hypothesis testing

Proportion Hypothesis Testing

Chi-Squared hypothesis testing

Variance Hypothesis Testing

Chi-Squared Hypothesis Testing

Mean hypothesis testing with t-distribution

Hypothesis Testing Mean Claims without Knowing <katex> \sigma </katex>

Analysis of variance (ANOVA)

Chi-square goodness of fit test

Type 1 and type 2 errors

Type 1 and Type 2 Errors

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math

<h2>What are statistics?</h2>
<p>Statistics &ndash; the math and science of data collection, analysis, visualization, and interpretation. Typically paired with the study of probability, the study of likelihoods for a particular event, statistics can also be described as the study of events given a series of probabilities. A statistic on the other hand, is the result that is yielded after data is analysed. A statistic can be described as descriptive or inferential. Descriptive statistics provide general summaries such as the distribution, spread and centre of a dataset. Inferential statistics involves data analysis on just a sample of the population to make inferences on the larger population as a whole.</p>
<figure class="quote">
<q>Statistical thinking will one day be as necessary a qualification for efficient citizenship as the ability to read and write.</q>
<figcaption>H.G. Wells</figcaption>
</figure>
<p><center><i></i></center></p>
<p align="right"></p>
<p>As suggested by Wells, Statistics math and probability math are both important topics you will likely encounter or require further on in life. At the very least, statistics are paramount in helping make major decisions that will have some effect on you. Governments are frequently tasked with collecting statistics from the population to inform them on the status of issues and how they can better serve the public. Academically speaking, be it in this course, an introduction to statistics course, probability and statistics or introduction to probability class, the purpose and study of statistics is to equip with you with strong mathematical induction and deduction skills when faced with data. Furthermore through exposure of the best practices in data collection, data analysis, data representation, and interpretation, the study of statistics aims to improve your analytical and problem-solving skills.</p>

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Mastering Poisson Distribution: Examples and Applications

What You'll Learn

Recognize the Poisson distribution as an approximation of the binomial distribution

Calculate lambda (λ) as the mean using n times p for any Poisson problem

Apply the Poisson formula to find probabilities for specific numbers of events

Use calculator commands (Poisson PDF and CDF) to solve distribution problems efficiently

Determine when Poisson distribution is appropriate for modeling rare events over time or space

What You'll Practice

Finding probabilities for exact numbers of successes using the Poisson formula

Calculating cumulative probabilities with Poisson CDF for ranges of outcomes

Using TI-84 calculator Poisson PDF and CDF functions

Comparing Poisson approximations with exact binomial distribution results

Why This Matters

This Unit Includes

10 Video lessons

Practice exercises

Learning resources

Skills

Poisson Distribution

Lambda

Probability

Binomial Approximation

PDF and CDF

Calculator Functions

Statistical Modeling