Workshop in Probability and Statistics
4.6 (3,529 ratings)
25,708 students enrolled

# 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.
4.6 (3,529 ratings)
25,708 students enrolled
Created by George Ingersoll
Last updated 4/2020
English
English, French [Auto], 7 more
Current price: \$27.99 Original price: \$39.99 Discount: 30% off
5 hours left at this price!
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This course includes
• 21.5 hours on-demand video
• 1 article
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll 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
Requirements
• Knowledge of basic algebra
• Microsoft Excel (recommended)
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.

Who this course is for:
• 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.
Course content
Expand all 74 lectures 22:13:13
+ Basic Probability and Terminology
10 lectures 01:36:12

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

Preview 08:47
Comprehension Check 1.1
1 question

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
07:21
Comprehension Check 1.2
1 question

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 (Intersection)
07:15
Comprehension Check 1.3
1 question
Or (Union)
08:49
Comprehension Check 1.4
1 question
Population vs. Sample
06:44
Measures of Central Tendency
12:32
Comprehension Check 1.5
1 question
Variance & Standard Deviation
12:48
Comprehension Check 1.6
1 question
Expected Value Introduction
06:55
Comprehension Check 1.7
1 question
Problem Set 1
2 pages
Problem Set 1 Walkthrough
25:01
+ 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
Comprehension Check 2.1
1 question

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

Preview 15:41
Comprehension Check 2.2
1 question

Three examples of conditional probability questions solved.

Conditional Probability II
15:53
Comprehension Check 2.3
1 question

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

Joint and Marginal Probabilities
23:01
Comprehension Check 2.4
1 question
Problem Set 2
3 pages
Problem Set 2 Walkthrough
17:34
+ Bayes' Rule & Random Variables
7 lectures 01:35:52
Permutations and Combinations
12:00

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

Bayes' Theorem
12:49
Comprehension Check 3.1
1 question

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

Conditional Probability Challenge Question
17:36

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

Random Variables and Probability Distributions
10:16
Comprehension Check 3.2
1 question

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

Expected Value
15:44
Comprehension Check 3.3
1 question
Problem Set 3
3 pages
Problem Set 3 Walkthrough
27:27
+ Probability Distributions
5 lectures 01:23:47
Binomial Distributions
31:42
Comprehension Check 4.1
1 question

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

Functions of Random Variables
13:32
Comprehension Check 4.2
1 question

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
09:27
Problem Set 4
3 pages
Problem Set 4 Walkthrough
29:06
+ 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
29:27

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?

Comprehension Check 5.1
1 question

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.

Z-Scores
27:21
Comprehension Check 5.2
1 question

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
25:15
Comprehension Check 5.3
1 question
Problem Set 5
3 pages
Problem Set 5 Walkthrough
35:03
+ Joint Random Variables
6 lectures 01:43:13

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

Confidence Intervals
14:03
Comprehension Check 6.1
1 question

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

Covariance and Correlation of Joint Random Variables
24:55
Comprehension Check 6.2
1 question

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
16:18
Comprehension Check 6.3
1 question

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

Variance in Joint Random Variables Example
18:38
Problem Set 6
3 pages
Problem Set 6 Walkthrough
29:19
+ Sampling
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
18:59
Comprehension Check 7.1
1 question

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
12:11
Comprehension Check 7.2
1 question

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.

t-Distributions
20:09
Comprehension Check 7.3
1 question

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
19:54
Comprehension Check 7.4
1 question
Problem Set 7
3 pages
Problem Set 7 Walkthrough
43:15
+ 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
38:55
Comprehension Check 8.1
1 question
Problem Set 8
3 pages
Problem Set 8 Walkthrough
39:58
+ 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
18:59

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
34:48

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.

15:45

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

Prediction and Confidence Intervals in Regression
17:13

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
33:53
+ 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)
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
13:45

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.

Transformations
09:39

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

Multiple Regression Case Study
23:39

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

Analysis of Variance (ANOVA)
19:19
Problem Set 10
5 pages
Problem Set 10 Walkthrough
28:57