Workshop in Probability and Statistics
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Workshop in Probability and Statistics

This workshop will teach you the fundamentals of statistics in order to give you a leg up at work or in school.
Best Seller
4.5 (1,178 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
13,783 students enrolled
Created by George Ingersoll
Last updated 6/2017
Current price: $12 Original price: $40 Discount: 70% off
4 days left at this price!
30-Day Money-Back Guarantee
  • 21.5 hours on-demand video
  • 1 Article
  • 14 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?
  • 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
View Curriculum
  • Knowledge of basic algebra
  • Microsoft Excel (recommended)

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.

Who is the target audience?
  • Current students who are (or will soon be) taking a course in introductory statistics with their home institutions
  • People in business who want a better grasp of probability and statistics.
Compare to Other Probability Courses
Curriculum For This Course
71 Lectures
Basic Probability and Terminology
7 Lectures 01:38:13

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

Fundamentals of Probability

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

Events and Complements

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".

And & Or (Intersection and Union)

This video provides a brief overview of basic statistical concepts and terms. Defined terms include population vs. sample, mean, median, mode, percentiles, quartiles, geometric mean, variance, standard deviation, Z-scores, and expected values.

Descriptive Statistics

Problem Set 1
2 pages

Problem Set 1 Walkthrough
Joint and Conditional Probability
6 Lectures 01:31:07

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

Preview 18:58

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

Preview 15:41

Three examples of conditional probability questions solved.

Conditional Probability II

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

Joint and Marginal Probabilities

Problem Set 2
3 pages

Problem Set 2 Walkthrough
Bayes' Rule & Random Variables
7 Lectures 01:35:52
Permutations and Combinations

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

Bayes' Theorem

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

Conditional Probability Challenge Question

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

Random Variables and Probability Distributions

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

Expected Value

Problem Set 3
3 pages

Problem Set 3 Walkthrough
Probability Distributions
5 Lectures 01:23:47
Binomial Distributions

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

Functions of Random Variables

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.

Graphing Probability Distributions

Problem Set 4
3 pages

Problem Set 4 Walkthrough
The Normal Distribution
5 Lectures 01:57:06

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

The Normal Distribution

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.


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.

Z-Score and Normal Distribution Examples

Problem Set 5
3 pages

Problem Set 5 Walkthrough
Joint Random Variables
6 Lectures 01:43:13

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

Confidence Intervals

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

Covariance and Correlation of Joint Random Variables

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.

Portfolio Analysis

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

Variance in Joint Random Variables Example

Problem Set 6
3 pages

Problem Set 6 Walkthrough
7 Lectures 02:14:55

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.

Preview 20:27

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.

Sampling Distributions

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

Proportion Sampling

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.


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.

Sampling and Confidence Intervals Examples

Problem Set 7
3 pages

Problem Set 7 Walkthrough
Hypothesis Testing
3 Lectures 01:18:53

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.

Hypothesis Testing

Problem Set 8
3 pages

Problem Set 8 Walkthrough
Simple Linear Regression
7 Lectures 02:13:09

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

Simple Linear Regression

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.

Analyzing Regression Output

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.

Additional Regression Concepts

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

Prediction and Confidence Intervals in Regression

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

Preview 12:31

Problem Set 9
3 pages

Problem Set 9 Walkthrough
Multiple Regression
7 Lectures 02:07:42

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

Multiple Regression (32:23)

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

Dummy and Time-Lagged Variables

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.

Multiple Regression Case Study

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

Analysis of Variance (ANOVA)

Problem Set 10
5 pages

Problem Set 10 Walkthrough
2 More Sections
About the Instructor
George Ingersoll
4.5 Average rating
1,176 Reviews
13,783 Students
1 Course

Associate Dean of Executive MBA Programs at the UCLA Anderson School of Management

I believe that quantitative subjects can be explained in ways that make the material much more accessible than the approaches that are typically taken by most college textbooks and courses. I hope that you will find my explanations easy to understand and easier still to put into practice.