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This workshop is designed to help you make sense of basic probability and statistics with easytounderstand 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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Section 1: Basic Probability and Terminology  

Lecture 1 
Course Welcome/ Introductory Lecture
Preview

09:49  
Lecture 2  07:52  
Basic introduction to probability. Examples using the fundamental probability equation. 

Lecture 3  07:55  
Continuing the discussion of basic probability we define complements ("not A") and examine how to find the probability of the complement of an event. 

Lecture 4  18:19  
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". 

Lecture 5  29:17  
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, Zscores, and expected values. 

Lecture 6 
Problem Set 1

2 pages  
Lecture 7 
Problem Set 1 Walkthrough

25:01  
Section 2: Joint and Conditional Probability  
Lecture 8  18:58  
How to find the probability of multiple events all taking place when we know the probability of each event. 

Lecture 9  15:41  
Introduction to conditional probability and how to solve using the fundamental probability equation. 

Lecture 10  15:53  
Three examples of conditional probability questions solved. 

Lecture 11  23:01  
How to calculate the intersection of several events. More examples using decision trees to calculate probabilities. 

Lecture 12 
Problem Set 2

3 pages  
Lecture 13 
Problem Set 2 Walkthrough

17:34  
Section 3: Bayes' Rule & Random Variables  
Lecture 14 
Permutations and Combinations

12:00  
Lecture 15  12:49  
Bayes' Theorem and how to solve conditional probability questions using decision trees. 

Lecture 16  17:36  
Putting it all together with Conditional Probability with a look ahead at Expected Value. 

Lecture 17  10:16  
Definition and terms related to random variables and examples of probability distributions, including an explanation of cumulative probability. 

Lecture 18  15:44  
Explanation and examples of expected value and its relationship to probability and statistics. Includes a refresher on weighted averages. 

Lecture 19 
Problem Set 3

3 pages  
Lecture 20 
Problem Set 3 Walkthrough

27:27  
Section 4: Probability Distributions  
Lecture 21  31:42  
Introduction to Binomial Distributions. How to find binomial probabilities using equations, Excel, and Binomial Tables. Correction (29:4529:52): Using the binomial table you would subtract the value for x<=329 (not 330) 

Lecture 22  13:32  
How to calculate the Expected Value and Standard Deviation of a function when it contains a Random Variable. 

Lecture 23  09:27  
Graphing probability distributions in an XY coordinate plane. Calculating probabilities by measuring the area under a curve. Includes explanations of Histograms and the Uniform Distribution. 

Lecture 24 
Problem Set 4

3 pages  
Lecture 25 
Problem Set 4 Walkthrough

29:06  
Section 5: The Normal Distribution  
Lecture 26  29:27  
Introduction to the Normal Distribution and Z Scores. Explanation of how the number of standard deviations from the mean is related to probability. 

Lecture 27  27:21  
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. 

Lecture 28  25:15  
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. 

Lecture 29 
Problem Set 5

3 pages  
Lecture 30 
Problem Set 5 Walkthrough

35:03  
Section 6: Joint Random Variables  
Lecture 31  14:03  
How to calculate confidence intervals using the Normal Distribution and Z Scores. 

Lecture 32  24:55  
Definitions, examples, and how to calculate covariances and correlations for two random variables. 

Lecture 33  16:18  
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. 

Lecture 34  18:38  
An example illustrating the concepts of Portfolio Analysis as well as correlation and variance of Joint Random Variables. 

Lecture 35 
Problem Set 6

3 pages  
Lecture 36 
Problem Set 6 Walkthrough

29:19  
Section 7: Sampling  
Lecture 37  20:27  
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. 

Lecture 38  18:59  
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. 

Lecture 39  12:11  
How to apply the principles of Sampling and the Central Limit Theorem to proportions. Includes how to calculate a proportion sample standard deviation. 

Lecture 40  20:09  
Definition of the tdistribution an how to perform sampling calculations when the standard deviation of the population is unknown. Also how to use the tTable. 

Lecture 41 
Problem Set 7

3 pages  
Lecture 42 
Problem Set 7 Walkthrough

43:15  
Section 8: Hypothesis Testing  
Lecture 43  19:54  
Several examples demonstrating calculations pertaining to Z values, sampling, confidence intervals, proportion sampling, and tdistributions. All related to the previous four videos: Stats 2427. 

Lecture 44  38:55  
Introduction to Hypothesis Testing and its relationship to Sampling. How to select null and alternative hypotheses and how to determine whether to use a onetailed or twotailed test. 

Lecture 45 
Problem Set 8

3 pages  
Lecture 46 
Problem Set 8 Walkthrough

39:58  
Section 9: Simple Linear Regression  
Lecture 47  18:59  
Introduction to linear regression. Definitions of independent and dependent variables, scatterplots, bestfit lines, residuals, the leastsquares method, and the prediction equation. 

Lecture 48  34:48  
More on simple linear regression including how to analyze the output of regression analysis using example data. Definitions of Rsquared, coefficients, and standard errors. Also how to test the significance of the relationship between an independent and dependent variable using hypothesis testing. 

Lecture 49  15:45  
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 Fstatistic. 

Lecture 50  17:13  
How to calculate confidence intervals for point predictions and population averages using regression. 

Lecture 51  12:31  
Overview of the four main assumptions of linear regression: linearity, independence of errors, homoscedasticity, and normality of residual distribution. 

Lecture 52 
Problem Set 9

3 pages  
Lecture 53 
Problem Set 9 Walkthrough

33:53  
Section 10: Multiple Regression  
Lecture 54  32:23  
Overview of multiple regression including the selection of predictor variables, multicollinearity, adjusted Rsquared, and dummy variables. 

Lecture 55  13:45  
Employing dummy variables and timelagged variables to come up with a better predictive model for your multiple regression analysis. 

Lecture 56  09:39  
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. 

Lecture 57  23:39  
An example illustrating the iterative process used to select predictor variables for a multiple regression model. 

Lecture 58  19:19  
A quick introduction to ANOVA, including examples of oneway and twoway analysis of variance. 

Lecture 59 
Problem Set 10

5 pages  
Lecture 60 
Problem Set 10 Walkthrough

28:57  
Section 11: Decision Analysis  
Lecture 61  18:37  
Steps for creating complex decision trees to aid in decision analysis using chance nodes and decision nodes. 

Lecture 62  17:15  
More on decision analysis including how to calculate the value of information. 

Lecture 63 
Problem Set 11

5 pages  
Lecture 64 
Problem Set 11 Walkthrough

01:25:16  
Lecture 65 
Concluding Lecture
Preview

17:38  
Section 12: Section 12: Summary Assessment  
Lecture 66 
Summary Assessment

Article  
Lecture 67 
Summary Assessment Walkthrough Qs 14

13:53  
Lecture 68 
Summary Assessment Walkthrough Qs 58

17:42  
Lecture 69 
Summary Assessment Walkthrough Qs 912

11:08  
Lecture 70 
Summary Assessment Walkthrough Qs 1316

19:42  
Lecture 71 
Summary Assessment Walkthrough Qs 1720

33:54 
Associate Dean of Hybrid Learning at the UCLA Anderson School of Management
Despite working in traditional academia, I strongly believe that quantitative subjects can be explained in ways that will make the material much more accessible to the student than the approaches that are usually 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.