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.
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  • Lectures 71
  • Length 22.5 hours
  • Skill Level All Levels
  • Languages English, captions
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 1/2014 English Closed captions available

Course Description

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.

What are the requirements?

  • Knowledge of basic algebra
  • Microsoft Excel (recommended)

What am I going to get from this course?

  • 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

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.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Basic Probability and Terminology
Course Welcome/ Introductory Lecture

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


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


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".


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.

Problem Set 1
2 pages
Problem Set 1 Walkthrough
Section 2: Joint and Conditional Probability

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


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


Three examples of conditional probability questions solved.


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

Problem Set 2
3 pages
Problem Set 2 Walkthrough
Section 3: Bayes' Rule & Random Variables
Permutations and Combinations

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


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.


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

Problem Set 3
3 pages
Problem Set 3 Walkthrough
Section 4: Probability Distributions

Introduction to Binomial Distributions. How to find binomial probabilities using equations, Excel, and Binomial Tables.

Correction (29:45-29:52): Using the binomial table you would subtract the value for x<=329 (not 330)


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


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
3 pages
Problem Set 4 Walkthrough
Section 5: 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.


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.

Problem Set 5
3 pages
Problem Set 5 Walkthrough
Section 6: Joint Random Variables

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


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


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

Problem Set 6
3 pages
Problem Set 6 Walkthrough
Section 7: 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.


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


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.

Problem Set 7
3 pages
Problem Set 7 Walkthrough
Section 8: Hypothesis Testing

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.


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.

Problem Set 8
3 pages
Problem Set 8 Walkthrough
Section 9: 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
3 pages
Problem Set 9 Walkthrough
Section 10: 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
5 pages
Problem Set 10 Walkthrough
Section 11: Decision Analysis

Steps for creating complex decision trees to aid in decision analysis using chance nodes and decision nodes.


More on decision analysis including how to calculate the value of information.

Problem Set 11
5 pages
Problem Set 11 Walkthrough
Concluding Lecture
Section 12: Section 12: Summary Assessment
Summary Assessment
Summary Assessment Walkthrough Qs 1-4
Summary Assessment Walkthrough Qs 5-8
Summary Assessment Walkthrough Qs 9-12
Summary Assessment Walkthrough Qs 13-16
Summary Assessment Walkthrough Qs 17-20

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Instructor Biography

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.

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