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)
13,783 students enrolled
Created by George Ingersoll
Last updated 6/2017
English
English
Current price: \$12 Original price: \$40 Discount: 70% off
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Includes:
• 21.5 hours on-demand video
• 1 Article
• 14 Supplemental Resources
• Access on mobile and TV
• Certificate of Completion

### Training 5 or more people?

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
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 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
22:15:14
+
Basic Probability and Terminology
7 Lectures 01:38:13
Preview 09:49

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

Fundamentals of Probability
07:52

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
07:55

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)
18:19

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
29:17

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

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
15:53

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

Joint and Marginal Probabilities
23:01

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

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

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

Expected Value
15:44

Problem Set 3
3 pages

Problem Set 3 Walkthrough
27:27
+
Probability Distributions
5 Lectures 01:23:47
Binomial Distributions
31:42

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

Functions of Random Variables
13:32

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

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

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

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

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

Covariance and Correlation of Joint Random Variables
24:55

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

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

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

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

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

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

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
2 More Sections