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George Ingersoll

This workshop is designed to help you make sense of basic probability and statistics with easy-to-understand explanations of all the subject's most important concepts. Whether you are starting from scratch or if you are in a statistics class and struggling with your assigned textbook or lecture material, this workshop was built with you in mind.

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What's inside

Learning objectives

  • By the end of this workshop you should be able to pass any introductory statistics course
  • This workshop will teach you probability, sampling, regression, and decision analysis

Syllabus

Basic Probability and Terminology

Basic introduction to probability. Examples using the fundamental probability equation.

Comprehension Check 1.1
Read more

Continuing the discussion of basic probability we define complements ("not A") and examine how to find the probability of the complement of an event.

Comprehension Check 1.2

More on basic probability. How to find the probability of two or more events occurring when we use the terms "and" and "or." For instance, how to find the probability of events "A and B" / "A or B".

Comprehension Check 1.3
Or (Union)
Comprehension Check 1.4
Population vs. Sample
Measures of Central Tendency
Comprehension Check 1.5
Variance & Standard Deviation
Comprehension Check 1.6
Expected Value Introduction
Comprehension Check 1.7
Problem Set 1
Problem Set 1 Walkthrough
Joint and Conditional Probability

How to find the probability of multiple events all taking place when we know the probability of each event.

Comprehension Check 2.1

Introduction to conditional probability and how to solve using the fundamental probability equation.

Comprehension Check 2.2

Three examples of conditional probability questions solved.

Comprehension Check 2.3

How to calculate the intersection of several events. More examples using decision trees to calculate probabilities.

Comprehension Check 2.4
Problem Set 2
Problem Set 2 Walkthrough
Bayes' Rule & Random Variables
Permutations and Combinations

Bayes' Theorem and how to solve conditional probability questions using decision trees.

Comprehension Check 3.1

Putting it all together with Conditional Probability with a look ahead at Expected Value.

Definition and terms related to random variables and examples of probability distributions, including an explanation of cumulative probability.

Comprehension Check 3.2

Explanation and examples of expected value and its relationship to probability and statistics. Includes a refresher on weighted averages.

Comprehension Check 3.3
Problem Set 3
Problem Set 3 Walkthrough
Probability Distributions
Binomial Distributions
Comprehension Check 4.1

How to calculate the Expected Value and Standard Deviation of a function when it contains a Random Variable.

Comprehension Check 4.2

Graphing probability distributions in an X-Y coordinate plane. Calculating probabilities by measuring the area under a curve. Includes explanations of Histograms and the Uniform Distribution.

Problem Set 4
Problem Set 4 Walkthrough
The Normal Distribution

Introduction to the Normal Distribution and Z Scores. Explanation of how the number of standard deviations from the mean is related to probability.

Popular real estate website Wozill has developed an algorithm for predicting the eventual sales price of any house before it goes on the market.  Sometimes the estimate provided by the algorithm is high, and sometimes it is low, but overall the expected difference between the prediction given by the algorithm and the actual sales price of the home is zero--meaning that the averages of all predictions and recorded sales are the same.

Unfortunately, the standard deviation of the difference between the algorithm's predictions and the actual sales prices of the homes is rather large: $100k, normally distributed around $0.  Approximately what percentage of estimates provide by the Wozill algorithm will be $200k or more below the actual sales price of the home?

How Z Scores (# of standard deviations from the mean of a normal distribution) can be converted to cumulative probabilities. How to use the Standard Normal (Z) Table.

Comprehension Check 5.2

In this video we solve several problems related to probabilities and the Normal Distribution. Includes solving for observed values, expected values, standard deviations, and cumulative probabilities.

Comprehension Check 5.3
Problem Set 5
Problem Set 5 Walkthrough
Joint Random Variables

How to calculate confidence intervals using the Normal Distribution and Z Scores.

Comprehension Check 6.1

Definitions, examples, and how to calculate covariances and correlations for two random variables.

Comprehension Check 6.2

Portfolio Analysis has to do with how to calculate the joint variance (and standard deviation) of multiple random variables. This video includes the equation to calculate joint variances when there may be multiple instances of two random variable and the variables may be correlated.

Comprehension Check 6.3

An example illustrating the concepts of Portfolio Analysis as well as correlation and variance of Joint Random Variables.

Problem Set 6
Problem Set 6 Walkthrough
Sampling

Introduction to Sampling and the Central Limit Theorem. Also how the size of a sample relates to the accuracy of a prediction for a population parameter.

More on Sampling and the Central Limit Theorem. How to calculate the probability of observing a sample mean using the standard deviation of the sample.

Comprehension Check 7.1

How to apply the principles of Sampling and the Central Limit Theorem to proportions. Includes how to calculate a proportion sample standard deviation.

Comprehension Check 7.2

Definition of the t-distribution an how to perform sampling calculations when the standard deviation of the population is unknown. Also how to use the t-Table.

Comprehension Check 7.3

Several examples demonstrating calculations pertaining to Z values, sampling, confidence intervals, proportion sampling, and t-distributions. All related to the previous four videos: Stats 24-27.

Comprehension Check 7.4
Problem Set 7
Problem Set 7 Walkthrough
Hypothesis Testing

Introduction to Hypothesis Testing and its relationship to Sampling. How to select null and alternative hypotheses and how to determine whether to use a one-tailed or two-tailed test.

Comprehension Check 8.1
Problem Set 8
Problem Set 8 Walkthrough
Simple Linear Regression

Introduction to linear regression. Definitions of independent and dependent variables, scatterplots, best-fit lines, residuals, the least-squares method, and the prediction equation.

More on simple linear regression including how to analyze the output of regression analysis using example data. Definitions of R-squared, coefficients, and standard errors. Also how to test the significance of the relationship between an independent and dependent variable using hypothesis testing.

A grab bag of additional regression concepts including how to calculate confidence intervals for predicted changes to a dependent variable based on a change to an independent variable, degrees of freedom with multiple independent variables, standardized coefficients, and the F-statistic.

How to calculate confidence intervals for point predictions and population averages using regression.

Overview of the four main assumptions of linear regression: linearity, independence of errors, homoscedasticity, and normality of residual distribution.

Problem Set 9
Problem Set 9 Walkthrough
Multiple Regression

Overview of multiple regression including the selection of predictor variables, multicollinearity, adjusted R-squared, and dummy variables.

Employing dummy variables and time-lagged variables to come up with a better predictive model for your multiple regression analysis.

This video provides a very brief overview of some ways that you can transform your data so that it takes the form of a linear function and can then be used in a regression. Includes exponential and logarithmic transformations.

An example illustrating the iterative process used to select predictor variables for a multiple regression model.

A quick introduction to ANOVA, including examples of one-way and two-way analysis of variance.

Problem Set 10
Problem Set 10 Walkthrough

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Suitable for those who need to gain a thorough grounding in probability and statistics
Beginners to statistics will benefit considerably from this course
Those in statistics classes who struggle with textbooks and lectures may find this course invaluable
This course covers a wide range of topics, making it suitable for those seeking a comprehensive understanding of probability and statistics

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Reviews summary

Useful examples and breakdowns

learners say that this workshop course is great, especially because the instructor provides useful examples and breaks down concepts in a way that makes them easy to understand. Students wish they had more opportunities to practice.
The examples are useful.
"especially the examples"
"and how he breaks it down"
The instructor is great.
"The course and instructor are great"
Learners wish they had more opportunities to practice.
"just need to practice more"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Workshop in Probability and Statistics with these activities:
Review Z-Scores and Probability Functions
Review Z-Scores and Probability Functions to solidify foundational prequisites and refresh comprehension of these key statistical concepts.
Browse courses on Statistics
Show steps
  • Go to Z-Score and Probability Function resource materials from previous statistics course.
  • Complete practice problems to apply Z-Score / Probability Function concepts.
  • Take an online quiz or test on Z-Scores / Probability Functions.
Work through Probability Theory Tutorial
Working through the Probability Theory tutorial will help you gain a deeper understanding of the fundamental principles of probability.
Browse courses on Probability Theory
Show steps
  • Find a reputable Probability Theory tutorial online or in a textbook.
  • Read through the tutorial and make notes on the key concepts.
  • Complete any practice problems or exercises that are included in the tutorial.
Solve Probability Practice Problems
Solving probability practice problems will help you improve your problem-solving skills and reinforce your understanding of the concepts.
Browse courses on Probability
Show steps
  • Find a set of probability practice problems online or in a textbook.
  • Attempt to solve the problems on your own.
  • Check your answers and review the solutions to identify any areas where you need to improve your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a Probability Workshop
A Probability Workshop can provide you with structured, in-person guidance and allow you to learn from others in a collaborative environment
Browse courses on Probability
Show steps
  • Find a reputable Probability workshop.
  • Register for the workshop and attend all sessions.
  • Participate actively in the discussions and exercises.
Write a Blog Post or Article on a Probability Topic
Creating a blog post or an article on a probability topic will allow you to synthesize your understanding and share your knowledge with others.
Browse courses on Probability
Show steps
  • Choose a topic that you are interested in and that you have a good understanding of.
  • Research the topic thoroughly and gather credible sources to support your claims.
  • Write a clear and concise article that explains the topic in a way that is easy to understand.
Tutor Fellow Students in Elementary Probability Concepts
Mentoring others will help you strengthen your understanding of probability and reinforce your own learning.
Browse courses on Probability
Show steps
  • Identify fellow students who would benefit from your help.
  • Offer to tutor them and commit to providing them with regular support.
  • Prepare lessons and activities that will help them understand probability concepts.
  • Meet with your students regularly to work through problems and answer their questions.
Contribute to the Probability and Statistics Open Source Project
Contributing to a probability and statistics open source project will allow you to apply your skills in a real-world setting and contribute to the broader community.
Browse courses on Probability
Show steps
  • Find a reputable open source project that is related to probability or statistics.
  • Join the project and familiarize yourself with its codebase.
  • Identify areas where you can contribute your skills and make a meaningful contribution.

Career center

Learners who complete Workshop in Probability and Statistics will develop knowledge and skills that may be useful to these careers:
Actuary
An Actuary is a financial professional who uses mathematics and statistics to assess the financial risks of insurance companies. They develop and use models to calculate the probability of events such as death, disability, and property damage. This course can help you develop the skills you need to be a successful Actuary by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Quantitative Analyst
A Quantitative Analyst is a financial professional who uses mathematics and statistics to analyze financial data. They develop and use models to predict the performance of stocks, bonds, and other financial instruments. This course can help you develop the skills you need to be a successful Quantitative Analyst by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Machine Learning Engineer
A Machine Learning Engineer is a professional who develops and deploys machine learning models. They work with data scientists to identify business problems that can be solved using machine learning, and then develop and implement models that can solve those problems. This course can help you develop the skills you need to be a successful Machine Learning Engineer by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Risk Analyst
A Risk Analyst is a professional who identifies, assesses, and manages risks. They work in a variety of industries, including finance, insurance, and healthcare. This course can help you develop the skills you need to be a successful Risk Analyst by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Data Scientist
A Data Scientist is a professional who uses data to solve business problems. They use a variety of techniques, including machine learning and artificial intelligence, to analyze data and identify patterns. This course can help you develop the skills you need to be a successful Data Scientist by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Market Researcher
A Market Researcher is a professional who conducts research to gather information about consumer behavior and market trends. They use a variety of research methods, including surveys, interviews, and experiments, to collect data that can help businesses make informed decisions. This course can help you develop the skills you need to be a successful Market Researcher by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Operations Research Analyst
An Operations Research Analyst is a professional who uses mathematics and statistics to improve the efficiency of business operations. They develop and use models to optimize processes, reduce costs, and improve customer satisfaction. This course can help you develop the skills you need to be a successful Operations Research Analyst by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Statistician
A Statistician is a scientist who uses mathematics to collect, analyze, interpret, and present data. They work in a variety of industries, including healthcare, finance, and government. This course can help you develop the skills you need to be a successful Statistician by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Database Administrator
A Database Administrator is a professional who manages and maintains databases. They work with a variety of database technologies to ensure that data is stored and processed efficiently and securely. This course can help you develop the skills you need to be a successful Database Administrator by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis. This knowledge can be helpful for developing databases that are efficient, reliable, and scalable.
Financial Analyst
A Financial Analyst is a professional who provides financial advice to individuals and businesses. They use a variety of analytical techniques to evaluate financial data and make recommendations for investment decisions. This course can help you develop the skills you need to be a successful Financial Analyst by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Data Engineer
A Data Engineer is a professional who builds and maintains the infrastructure that is used to store and process data. They work with a variety of technologies to ensure that data is available to users when and where they need it. This course can help you develop the skills you need to be a successful Data Engineer by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis. This knowledge can be helpful for developing data pipelines that are efficient, reliable, and scalable.
Business Analyst
A Business Analyst is a professional who uses data to help businesses make better decisions. They work with stakeholders to identify business needs, collect and analyze data, and develop recommendations for improvement. This course can help you develop the skills you need to be a successful Business Analyst by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis.
Data Analyst
A Data Analyst is a business professional who uses data to help businesses make informed decisions. They collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills you need to be a successful Data Analyst by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis. This course may also be useful for Data Analysts who want to learn more about advanced statistical techniques.
Software Engineer
A Software Engineer is a professional who designs, develops, and maintains software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course can help you develop the skills you need to be a successful Software Engineer by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis. This knowledge can be helpful for developing software that is efficient, reliable, and user-friendly.
Web Developer
A Web Developer is a professional who designs and develops websites. They work with a variety of programming languages and technologies to create websites that are visually appealing, functional, and user-friendly. This course can help you develop the skills you need to be a successful Web Developer by providing you with a strong foundation in probability and statistics. You will learn how to collect, clean, and analyze data, as well as how to interpret the results of your analysis. This knowledge can be helpful for developing websites that are efficient, reliable, and user-friendly.

Reading list

We've selected 15 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Workshop in Probability and Statistics.
In-depth reference on Bayesian data analysis, providing comprehensive coverage of the topic for those interested in further exploration.
Comprehensive and concise introduction to probability theory, recommended as a reference for deeper understanding of core probability concepts.
This introductory textbook covers basic to intermediate probability and statistics concepts with a focus on applications, suitable as a more in-depth reference.
Modern statistical learning methods and algorithms, may provide additional depth on the course's statistical modeling concepts.
For those new to probability, this book is an approachable introduction, requiring no advanced math skills. Suitable for beginners.
A comprehensive treatment of statistics as a whole, suitable as a supplemental reference or for additional background knowledge.
Econometrics textbook, relevant for the course's materials on regression and may be useful as a reference for those interested in applications in economics.
Regression models in actuarial science and finance contexts, which may provide practical insights for the course's material on regression.
Introductory text on causal inference, providing a foundation for understanding causality in statistics, which may supplement the course's materials.
Additional reading on concepts like decision making under uncertainty and risk assessment, which may supplement the course's materials.

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