# Top 8 Recommended Statistics Self-Study Materials! [March 2024]

This page introduces the best in educational materials for beginners who are trying to learn **Statistics** on their own.

## Table of Contents:

## 1. Description of this page

We introduce 8 recommended video courses on various platforms for those who want to learn Statistics on their own.

What is Statistics?

Statistics is an essential subject in the field of data science. In recent years, big data analysis has achieved significant results in various fields such as management, marketing, healthcare, education, and system performance analysis. As a result, the importance of statistics has been increasing every year, not only for data scientists but also for professionals in a wide range of areas.

Our site, "Outlecture," evaluates courses using our proprietary algorithm that balances course rating, freshness of information, number of purchasers and viewers, and recent rate of increase, in order to extract only the most suitable courses for users.

In addition, we will explain the features of each video platform and provide use cases such as "this is better for people in this situation."

We hope this will be a reference for everyone who is going to learn Statistics.

## 2. Top 5 Recommended Udemy Courses

Here are Outlecture's top 5 recommended Udemy courses, carefully selected for you.

Title | Ratings | Subscribers | Subscribers last month (February 2024) | Level | Video Duration | Created | Last updated | Price |
---|---|---|---|---|---|---|---|---|

Probability and Statistics: Complete Course 2024 | 4.79 | 1,510 | 147 | all | 16 hours 18 minutes | Mar 5th, 2023 | Jan 2nd, 2024 | $94.99 |

Statistics for Data Science and Business Analysis | 4.58 | 195,361 | 2,046 | all | 4 hours 48 minutes | Jul 20th, 2017 | Feb 21st, 2024 | $99.99 |

Essential Statistics for Data Analysis | 4.62 | 7,003 | 331 | all | 7 hours 48 minutes | Nov 15th, 2022 | Dec 7th, 2023 | $99.99 |

Become a Probability & Statistics Master | 4.73 | 88,448 | 945 | all | 15 hours 4 minutes | Feb 3rd, 2018 | Feb 16th, 2024 | $169.99 |

Master statistics & machine learning: intuition, math, code | 4.66 | 24,942 | 467 | all | 38 hours 19 minutes | May 23rd, 2020 | Jan 31st, 2024 | $99.99 |

Udemy, Inc. is an education technology company that provides the world's largest online learning and teaching platform.

The features of Udemy include:

- Over 155,000 course
- Instructors who are leading experts in their fields
- Affordable prices range from tens to hundreds of dollars per course, with discounts of up to 70-90% during campaigns
- Courses can be viewed without expiration after purchase, and come with a 30-day money-back guarantee
- Courses can be taken at the student's own pace, with playback speeds of 0.5 to 2 times normal speed, and can be viewed offline on a smartphone with a dedicated app
- Students can ask questions directly to the instructor on the course discussion board, allowing them to resolve any doubts and receive support for self-study

These are some of the benefits of using Udemy.

The management team at Outlecture consists of active software engineers, creators, and web designers. We often catch up on learning new programming languages and products by taking courses on Udemy.

As for our experience, we find that Udemy offers courses of very high quality. The instructors are all leading figures in their fields, and they teach cutting-edge knowledge and practical know-how in a clear and detailed manner. You can acquire the knowledge and skills that are actually used in the field and in practical projects, rather than just knowledge for exams.

We highly recommend Udemy courses, especially for those who want to apply what they learn in practical situations or for those who want to start self-studying. Once you purchase a course, you can take it without a time limit, and there is a 30-day money-back guarantee, so you can start learning with peace of mind.

Recommended for

- Planning to use Statistics in actual projects
- Wanting to learn the know-how of professionals who are active in the world's cutting-edge fields
- Hesitant to use a subscription service
- Having basic IT knowledge

The details of each course are as follows:

Learn the Probability and Statistics You Need to Succeed in Data Science and Business Analytics

- Ratings
- 4.79
- Subscribers
- 1,510
- Subscribers last month

(February 2024) - 147
- Level
- all
- Video Duration
- 16 hours 18 minutes
- Created
- Mar 5th, 2023
- Last updated
- Jan 2nd, 2024
- Price
- $94.99

This is course designed to take you from beginner to expert in probability and statistics. It is designed to be **practical**, **hands on** and suitable for anyone who wants to use statistics in **data science**, **business analytics** or any other field to make better informed decisions.

Videos packed with worked examples and explanations so you never get lost, and every technique covered is implemented in **Microsoft Excel** so that you can put it to use immediately.

Key concepts taught in the course are:

Descriptive Statistics: Averages, measures of spread, correlation and much more.

Cleaning Data: Identifying and removing outliers

Visualization of Data: All standard techniques for visualizing data, embedded in Excel.

Probability: Independent Events, conditional probability and Bayesian statistics.

Discrete Distributions: Binomial, Poisson, expectation and variance and approximations.

Continuous Distributions: The Normal distribution, the central limit theorem and continuous random variables.

Hypothesis Tests: Using binomial, Poisson and normal distributions, T-tests and confidence intervals.

Regression: Linear regression analysis, correlation, testing for correlation, non-linear regression models.

Quality of Tests: Type I and Type II errors, power and size, p-hacking.

Chi-Squared Tests: The chi-squared distribution and how to use it to test for association and goodness of fit.

Much, much more!

It requires no prior knowledge, with the exception of 2 optional videos at the end of the continuous distribution chapter, in which knowledge of calculus is required).

- Introduction
- Introduction
- Course Overview
- Descriptive Statistics
- Data for this chapter
- The Mean Average
- The Mean Average - Quiz
- The Median Average
- The Median Average - Quiz
- The Modal Average
- The Modal Average - Quiz
- Comparing Averages
- Comparing Averages - Quiz
- Quantiles, Range and Inter-Quartile Range
- Quantiles, Range and Inter-Quartile Range - Data
- Quantiles, Range and Inter-Quartile Range - Quiz
- Standard Deviation and Variance
- Standard Deviation and Variance - Data
- Standard Deviation and Variance - Quiz
- The Coefficient of Variation
- The Coefficient of Variation - Data
- The Coefficient of Variation - Quiz
- Skew
- Skew - data
- Skew - Quiz
- Kurtosis
- Kurtosis - Quiz
- Correlation Coefficients
- Correlation Coefficients - Data
- Correlation Coefficients - Quiz
- Slides
- Cleaning Data
- Anomalies and Outliers
- Anomalies and Outliers - Data
- Anomalies and Outliers - Quiz
- Coding Your Data
- Coding Your Data - Quiz
- Slides
- Data Visualization
- Line Graphs
- Line Graph - Assignment
- Bar Charts
- Bar Charts - Quiz
- Dual Axis Charts
- Dual Axis Charts - Assignment
- Pie Charts
- Pie Charts - Quiz
- Histograms
- Histograms - Data
- Histograms - Quiz
- Box Plots
- Box Plots - Quiz
- Cumulative Frequency
- Cumulative Frequency - Quiz
- Comparing Visualizations
- Slides
- Sampling
- Populations and Samples
- Populations and Samples - Quiz
- Random Sampling
- Random Sampling - Quiz
- Non-Random Sampling
- Non-Random Sampling - Quiz
- Slides
- Probability
- What is Probability?
- What is Probability - Quiz
- Set Notation
- Set Notation - Quiz
- Independent Events
- Independent Events - Quiz
- Mutually Exclusive Events
- Mutually Exclusive Events - Quiz
- Tree Diagrams
- Tree Diagrams - Quiz
- Venn Diagrams
- Venn Diagram - Quiz
- Conditional Probability
- Conditional Probability - Quiz
- Bayes' Theorem
- Slides
- Discrete Distributions
- What is a Discrete Random Variable?
- What is a Discrete Random Variable - Quiz
- Probability Mass Functions
- Probability Mass Functions - Quiz
- The Expectation of a Discrete Random Variable
- The Expectation of a Discrete Random Variable
- The Variance of a Discrete Random Variable
- The Variance of a Discrete Random Variable - Data
- The Variance of a Discrete Random Variable - Quiz
- The Binomial Distribution - Intro
- The Binomial Distribution - Intro - Quiz
- The Binomial Distribution Formula - Part 1
- The Binomial Distribution Formula - Part 1
- The Binomial Distribution Formula - Part 2
- The Binomial Distribution Formula - Part 2 - Quiz
- Using Excel to Solve Binomial Problems
- Using Excel to Solve Binomial Problems
- Applying the Binomial Distribution to Real-World Problems
- Applying the Binomial Distribution to Real-World Problems - Quiz
- Conditional Probability with the Binomial Distribution
- Conditional Probability with the Binomial Distribution - Quiz
- The Poisson Distribution - Intro
- The Poisson Distribution - Intro - Quiz
- Using Excel to Solve Poisson Problems
- Using Excel to Solve Poisson Problems - Quiz
- Applying the Poisson Distribution Real-World Problems
- Applying the Poisson Distribution Real-World Problems - Quiz
- Conditional Probability with the Poisson Distribution
- Conditional Probability with the Poisson Distribution - Quiz
- The Geometric Distribution
- The Geometric Distribution - Quiz
- Expectation and Variance of Distributions
- Expectation and Variance of Distributions - Quiz
- Approximating the Binomial Distribution with the Poisson Distribution
- Approximating the Binomial Distribution with the Poisson Distribution - Quiz
- Derivation of the Poisson Formula
- Slides
- Continuous Distributions
- What is a Continuous Distribution?
- What is a Continuous Distribution - Quiz
- The Normal Distribution - Intro
- The Normal Distribution - Intro - Quiz
- Calculating Probabilities with the Normal Distribution
- Calculating Probabilities with the Normal Distribution - Quiz
- The Inverse Normal Distribution
- The Inverse Normal Distribution - Quiz
- Z-Scores
- Finding Unknown Means and Standard Deviations
- Finding Unknown Means and Standard Deviations - Quiz
- Conditional Probability with the Normal Distribution
- Conditional Probability with the Normal Distribution - Quiz
- Normal Approximations to Binomial Distributions - Part 1
- Normal Approximations to Binomial Distributions - Part 1 - Quiz
- Normal Approximations to Binomial Distributions - Part 2
- Normal Approximations to Binomial Distributions - Part 2 - Quiz
- Normal Approximations to Poisson Distributions
- Normal Approximations to Poisson Distributions - Quiz
- The Central Limit Theorem
- The Central Limit Theorem - Quiz
- The Limitations of the Central Limit Theorem
- The Limitations of the Central Limit Theorem - Quiz
- Continuous Random Variables - Probability Density Functions
- Continuous Random Variables - Cumulative Distribution Functions
- Continuous Random Variables - Expectation and Variance
- Continuous Random Variables - Medians and Quartiles
- Slides
- Hypothesis Tests
- Introduction to Hypothesis Tests - P-Values
- Introduction to Hypothesis Tests - P-Values
- Binomial Hypothesis Tests - Part 1
- Binomial Hypothesis Tests - Part 2
- Binomial Hypothesis Tests - Quiz
- Binomial Hypothesis Tests - Critical Regions
- Binomial Hypothesis Tests - Critical Regions - Quiz
- Two-Tailed Tests
- Two-Tailed Tests
- Poisson Hypothesis Tests
- Poisson Hypothesis Tests - Quiz
- Poisson Critical Regions
- Poisson Critical Regions - Quiz
- Normal Hypothesis Tests
- Normal Hypothesis Tests - Quiz
- Normal Hypothesis Tests - Critical Regions
- Normal Hypothesis Tests - Critical Regions - Quiz
- T-Tests
- Confidence Intervals
- Slides
- Regression
- Correlation
- Data for Linear Regression Video
- Linear Regression
- Evaluating a Regression Line
- Correlation Hypothesis Tests - Intro
- Carrying Out a Test for Correlation
- Correlation Confidence Intervals
- Working with Non-Linear Data - Exponential Models
- Working with Non-Linear Data - Polynomial Models
- Slides
- Quality of Tests
- Type I Errors
- Type II Errors
- Size and Power
- P-Hacking
- Slides
- Chi-Squared Tests
- The Chi-Squared Distribution
- Chi-Squared Tests for Goodness of Fit
- Grouping
- Using Estimated Parameters in Chi-Squared Tests
- Chi-Squared Tests for Association
- Slides

Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis

- Ratings
- 4.58
- Subscribers
- 195,361
- Subscribers last month

(February 2024) - 2,046
- Level
- all
- Video Duration
- 4 hours 48 minutes
- Created
- Jul 20th, 2017
- Last updated
- Feb 21st, 2024
- Price
- $99.99

**Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist?**

**And you want to acquire the quantitative skills needed for the job?**

Well then, you’ve come to the right place!

*Statistics for Data Science and Business Analysis is here for you! (with TEMPLATES in Excel included)*

**This is where you start. And it is the perfect beginning! **

In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. We have created a course that is:

Easy to understand

Comprehensive

Practical

To the point

Packed with plenty of exercises and resources

Data-driven

Introduces you to the statistical scientific lingo

Teaches you about data visualization

Shows you the main pillars of quant research

It is no secret that a lot of these topics have been explained online. Thousands of times. However, it is next to impossible to find a structured program that gives you an understanding of why certain statistical tests are being used so often. Modern software packages and programming languages are automating most of these activities, but this course gives you something more valuable – critical thinking abilities. Computers and programming languages are like ships at sea. They are fine vessels that will carry you to the desired destination, but it is up to you, the aspiring data scientist or BI analyst, to navigate and point them in the right direction.

**Teaching is our passion**

We worked full-time for several months to create the best possible Statistics course, which would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts and course notes, as well as a glossary with all new terms you will learn, are just some of the perks you will get by subscribing.

**What makes this course different from the rest of the Statistics courses out there?**

High-quality production – HD video and animations (This isn’t a collection of boring lectures!)

Knowledgeable instructor (An adept mathematician and statistician who has competed at an international level)

Complete training – we will cover all major statistical topics and skills you need to become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist

Extensive Case Studies that will help you reinforce everything you’ve learned

Excellent support - if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day

Dynamic - we don’t want to waste your time! The instructor sets a very good pace throughout the whole course

**Why do you need these skills?**

**Salary/Income**– careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow**Promotions**– If you understand Statistics well, you will be able to back up your business ideas with quantitative evidence, which is an easy path to career growth**Secure Future**– as we said, the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you’ve probably heard of the number of jobs that will be automated soon, right? Well, data science careers are the ones doing the automating, not getting automated**Growth**- this isn’t a boring job. Every day, you will face different challenges that will test your existing skills and require you to learn something new

Please bear in mind that the course comes with Udemy’s 30-day unconditional money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you.

*Click 'Buy now' and let's start learning together today!*

- Introduction
- What does the course cover?
- Download all resources
- Sample or population data?
- Understanding the difference between a population and a sample
- Population vs sample
- The fundamentals of descriptive statistics
- The various types of data we can work with
- Types of data
- Levels of measurement
- Levels of measurement
- Categorical variables. Visualization techniques for categorical variables
- Categorical variables. Visualization Techniques
- Categorical variables. Visualization techniques. Exercise
- Numerical variables. Using a frequency distribution table
- Numerical variables. Using a frequency distribution table
- Numerical variables. Using a frequency distribution table. Exercise
- Histogram charts
- Histogram charts
- Histogram charts. Exercise
- Cross tables and scatter plots
- Cross Tables and Scatter Plots
- Cross tables and scatter plots. Exercise
- Measures of central tendency, asymmetry, and variability
- The main measures of central tendency: mean, median and mode
- Mean, median and mode. Exercise
- Measuring skewness
- Skewness
- Skewness. Exercise
- Measuring how data is spread out: calculating variance
- Variance. Exercise
- Standard deviation and coefficient of variation
- Standard deviation
- Standard deviation and coefficient of variation. Exercise
- Calculating and understanding covariance
- Covariance. Exercise
- The correlation coefficient
- Correlation
- Correlation coefficient
- Practical example: descriptive statistics
- Practical example
- Practical example: descriptive statistics
- Distributions
- Introduction to inferential statistics
- What is a distribution?
- What is a distribution
- The Normal distribution
- The Normal distribution
- The standard normal distribution
- The standard normal distribution
- Standard Normal Distribution. Exercise
- Understanding the central limit theorem
- The central limit theorem
- Standard error
- Standard error
- Estimators and estimates
- Working with estimators and estimates
- Estimators and estimates
- Confidence intervals - an invaluable tool for decision making
- Confidence intervals
- Calculating confidence intervals within a population with a known variance
- Confidence intervals. Population variance known. Exercise
- Confidence interval clarifications
- Student's T distribution
- Student's T distribution
- Calculating confidence intervals within a population with an unknown variance
- Population variance unknown. T-score. Exercise
- What is a margin of error and why is it important in Statistics?
- Margin of error
- Confidence intervals: advanced topics
- Calculating confidence intervals for two means with dependent samples
- Confidence intervals. Two means. Dependent samples. Exercise
- Calculating confidence intervals for two means with independent samples (part 1)
- Confidence intervals. Two means. Independent samples (Part 1). Exercise
- Calculating confidence intervals for two means with independent samples (part 2)
- Confidence intervals. Two means. Independent samples (Part 2). Exercise
- Calculating confidence intervals for two means with independent samples (part 3)
- Practical example: inferential statistics
- Practical example: inferential statistics
- Practical example: inferential statistics
- Hypothesis testing: Introduction
- The null and the alternative hypothesis
- Further reading on null and alternative hypotheses
- Null vs alternative
- Establishing a rejection region and a significance level
- Rejection region and significance level
- Type I error vs Type II error
- Type I error vs type II error
- Hypothesis testing: Let's start testing!
- Test for the mean. Population variance known
- Test for the mean. Population variance known. Exercise
- What is the p-value and why is it one of the most useful tools for statisticians
- p-value
- Test for the mean. Population variance unknown
- Test for the mean. Population variance unknown. Exercise
- Test for the mean. Dependent samples
- Test for the mean. Dependent samples. Exercise
- Test for the mean. Independent samples (Part 1)
- Test for the mean. Independent samples (Part 1)
- Test for the mean. Independent samples (Part 2)
- Test for the mean. Independent samples (Part 2)
- Test for the mean. Independent samples (Part 2). Exercise
- Practical example: hypothesis testing
- Practical example: hypothesis testing
- Practical example: hypothesis testing
- The fundamentals of regression analysis
- Introduction to regression analysis
- Introduction
- Correlation and causation
- Correlation and causation
- The linear regression model made easy
- The linear regression model
- What is the difference between correlation and regression?
- Correlation vs regression
- A geometrical representation of the linear regression model
- A geometrical representation of the linear regression model
- A practical example - Reinforced learning
- Subtleties of regression analysis
- Decomposing the linear regression model - understanding its nuts and bolts
- Decomposition
- What is R-squared and how does it help us?
- R-squared
- The ordinary least squares setting and its practical applications
- The ordinary least squares setting and its practical applications
- Studying regression tables
- Studying regression tables
- Regression tables. Exercise
- The multiple linear regression model
- The multiple linear regression model
- The adjusted R-squared
- The adjusted R-squared
- What does the F-statistic show us and why do we need to understand it?
- Assumptions for linear regression analysis
- OLS assumptions
- OLS assumptions
- A1. Linearity
- A1. Linearity
- A2. No endogeneity
- A2. No endogeneity
- A3. Normality and homoscedasticity
- A3. Normality and homoscedasticity
- A4. No autocorrelation
- A4. No autocorrelation
- A5. No multicollinearity
- A5. No multicollinearity
- Dealing with categorical data
- Dummy variables
- Practical example: regression analysis
- Practical example: regression analysis
- Bonus lecture
- Bonus lecture: Next steps

Learn statistics with fun, real-world projects; probability distributions, hypothesis tests, regression analysis & more!

- Ratings
- 4.62
- Subscribers
- 7,003
- Subscribers last month

(February 2024) - 331
- Level
- all
- Video Duration
- 7 hours 48 minutes
- Created
- Nov 15th, 2022
- Last updated
- Dec 7th, 2023
- Price
- $99.99

This is a **hands-on, project-based course** designed to help you learn and apply **essential statistics concepts** for data analysis & business intelligence. Our goal is to *simplify* and *demystify* the world of statistics using familiar tools like Microsoft Excel, and empower everyday people to understand and apply these tools and techniques – even if you have absolutely no background in math or stats!

We'll start by discussing the role of statistics in business intelligence, the difference between sample and population data, and the importance of using statistical techniques to make smart predictions and data-driven decisions.

Next we'll explore our data using **descriptive statistics** and **probability distributions**, introduce the normal distribution and empirical rule, and learn how to apply the **central limit theorem** to make inferences about populations of any type.

From there we'll practice making estimates with **confidence intervals**, and using **hypothesis tests** to evaluate assumptions about unknown population parameters. We'll introduce the basic hypothesis testing framework, then dive into concepts like null and alternative hypotheses, t-scores, p-values, type I vs. type II errors, and more.

Last but not least, we'll introduce the fundamentals of **regression analysis**, explore the difference between correlation and causation, and practice using basic linear regression models to make predictions using Excel's Analysis Toolpak.

Throughout the course, you'll play the role of a Recruitment Analyst for **Maven Business School**. Your goal is to use the statistical techniques you've learned to explore student data, predict the performance of future classes, and propose changes to help improve graduate outcomes.

You'll also practice applying your skills to **5 real-world BONUS PROJECTS**, and use statistics to explore data from restaurants, medical centers, pharmaceutical companys, safety teams, airlines, and more.

**COURSE OUTLINE:**

**Why Statistics?***Discuss the role of statistics in the context of business intelligence and decision-making, and introduce the statistics workflow*

**Understanding Data with Descriptive Statistics***Understand data using descriptive statistics, including frequency distributions and measures of central tendency & variability***PROJECT #1**: Maven Pizza Parlor

**Modeling Data with Probability Distributions***Model data with probability distributions, and use the normal distribution to calculate probabilities and make value estimates***PROJECT #2**: Maven Medical Center

**The Central Limit Theorem***Introduce the Central Limit Theorem, which leverages the normal distribution to make inferences on populations with any distribution*

**Making Estimates with Confidence Intervals***Make estimates with confidence intervals, which use sample statistics to define a range where an unknown population parameter likely lies***PROJECT #3**: Maven Pharma

**Drawing Conclusions with Hypothesis Tests***Draw conclusions with hypothesis tests, which let you evaluate assumptions about population parameters using sample statistics***PROJECT #4**: Maven Safety Council

**Making Predictions with Regression Analysis***Make predictions with regression analysis, and estimate the values of a dependent variable via its relationship with independent variables***PROJECT #5**: Maven Airlines

Join today and get **immediate, lifetime access **to the following:

*7.5 hours of high-quality video**Statistics for Data Analysis PDF ebook (150+ pages)**Downloadable Excel project files & solutions**Expert support and Q&A forum**30-day Udemy satisfaction guarantee*

If you're an analyst, data scientist, business intelligence professional, or anyone looking to use statistics to make smart, data-driven decisions, **this course is for you**!

Happy learning!

-Enrique Ruiz *(Lead Statistics & Excel Instructor, **Maven Analytics**)*

- Getting Started
- Course Structure & Outline
- READ ME: Important Notes for New Students
- DOWNLOAD: Course Resources
- Setting Expectations
- The Course Project
- Helpful Resources
- Why Statistics?
- Section Intro
- Why Statistics?
- Populations & Samples
- The Statistics Workflow
- QUIZ: Why Statistics?
- Understanding Data with Descriptive Statistics
- Section Intro
- Descriptive Statistics Basics
- Types of Variables
- Types of Descriptive Statistics
- Categorical Frequency Distributions
- Numerical Frequency Distributions
- Histograms
- ASSIGNMENT: Frequency Distributions
- KNOWLEDGE CHECK: Frequency Distributions
- SOLUTION: Frequency Distributions
- Mean, Median, and Mode
- Left & Right Skew
- ASSIGNMENT: Measures of Central Tendency
- KNOWLEDGE CHECK: Measures of Central Tendency
- SOLUTION: Measures of Central Tendency
- Min, Max & Range
- Interquartile Range
- Box & Whisker Plots
- Variance & Standard Deviation
- PRO TIP: Coefficient of Variation
- ASSIGNMENT: Measures of Variability
- KNOWLEDGE CHECK: Measures of Variability
- SOLUTION: Measures of Variability
- Key Takeaways
- QUIZ: Descriptive Statistics
- PROJECT #1: Maven Pizza Parlor
- PROJECT BRIEF: Maven Pizza Parlor
- SOLUTION: Maven Pizza Parlor
- Modeling Data with Probability Distributions
- Section Intro
- Probability Distribution Basics
- Types of Probability Distributions
- The Normal Distribution
- Z Scores
- The Empirical Rule
- ASSIGNMENT: Normal Distributions
- KNOWLEDGE CHECK: Normal Distributions
- SOLUTION: Normal Distributions
- Excel's Normal Distribution Functions
- Calculating Probabilities with the Normal Distribution
- The NORM.DIST Function
- The NORM.S.DIST Function
- ASSIGNMENT: Calculating Probabilities
- KNOWLEDGE CHECK: Calculating Probabilities
- SOLUTION: Calculating Probabilities
- PRO TIP: Plotting the Normal Curve
- Estimating X or Z Values with the Normal Distribution
- The NORM.INV Function
- The NORM.S.INV Function
- ASSIGNMENT: Estimating Values
- KNOWLEDGE CHECK: Estimating Values
- SOLUTION: Estimating Values
- Key Takeaways
- QUIZ: Probability Distributions
- PROJECT #2: Maven Medical Center
- PROJECT BRIEF: Maven Medical Center
- SOLUTION: Maven Medical Center
- The Central Limit Theorem
- Section Intro
- The Central Limit Theorem
- DEMO: Proving the Central Limit Theorem
- Standard Error
- Implications of the Central Limit Theorem
- Applications of the Central Limit Theorem
- Key Takeaways
- QUIZ: The Central Limit Theorem
- Making Estimates with Confidence Intervals
- Section Intro
- Confidence Intervals Basics
- Confidence Level
- Margin of Error
- DEMO: Calculating Confidence Intervals
- The CONFIDENCE.NORM Function
- ASSIGNMENT: Confidence Intervals
- KNOWLEDGE CHECK: Confidence Intervals
- SOLUTION: Confidence Intervals
- Types of Confidence Intervals
- T Distribution
- Excel's T Distribution Functions
- Confidence Intervals with the T Distribution
- ASSIGNMENT: Confidence Intervals (T Distribution)
- KNOWLEDGE CHECK: Confidence Intervals (T Distribution)
- SOLUTION: Confidence Intervals (T Distribution)
- Confidence Intervals for Proportions
- ASSIGNMENT: Confidence Intervals (Proportions)
- KNOWLEDGE CHECK: Confidence Intervals (Proportions)
- SOLUTION: Confidence Intervals (Proportions)
- Confidence Intervals for Two Populations
- Dependent Samples
- ASSIGNMENT: Confidence Intervals (Dependent Samples)
- KNOWLEDGE CHECK: Confidence Intervals (Dependent Samples)
- SOLUTION: Confidence Intervals (Dependent Samples)
- Independent Samples
- ASSIGNMENT: Confidence Intervals (Independent Samples)
- KNOWLEDGE CHECK: Confidence Intervals (Independent Samples)
- SOLUTION: Confidence Intervals (Independent Samples)
- PRO TIP: Difference Between Proportions
- Key Takeaways
- QUIZ: Confidence Intervals
- PROJECT #3: Maven Pharma
- PROJECT BRIEF: Maven Pharma
- SOLUTION: Maven Pharma
- Drawing Conclusions with Hypothesis Tests
- Section Intro
- Hypothesis Testing Basics
- Null & Alternative Hypothesis
- Significance Level
- Test Statistic (T-score)
- P-Value
- Drawing Conclusions from Hypothesis Tests
- ASSIGNMENT: Hypothesis Tests
- KNOWLEDGE CHECK: Hypothesis Tests
- SOLUTION: Hypothesis Tests
- Relationship between Confidence Intervals & Hypothesis Tests
- Type I & Type II Errors
- One Tail & Two Tail Hypothesis Tests
- DEMO: One Tail Hypothesis Test
- Hypothesis Tests for Proportions
- ASSIGNMENT: Hypothesis Tests (Proportions)
- KNOWLEDGE CHECK: Hypothesis Tests (Proportions)
- SOLUTION: Hypothesis Tests (Proportions)
- Hypothesis Tests for Dependent Samples
- ASSIGNMENT: Hypothesis Tests (Dependent Samples)
- KNOWLEDGE CHECK: Hypothesis Tests (Dependent Samples)
- SOLUTION: Hypothesis Tests (Dependent Samples)
- Hypothesis Tests for Independent Samples
- ASSIGNMENT: Hypothesis Tests (Independent Samples)
- KNOWLEDGE CHECK: Hypothesis Tests (Independent Samples)
- SOLUTION: Hypothesis Tests (Independent Samples)
- Key Takeaways
- QUIZ: Hypothesis Tests
- PROJECT #4: Maven Safety Council
- PROJECT BRIEF: Maven Safety Council
- SOLUTION: Maven Safety Council
- Making Predictions with Regression Analysis
- Section Intro
- Linear Relationships
- Correlation (R)
- ASSIGNMENT: Linear Relationships
- KNOWLEDGE CHECK: Linear Relationships
- SOLUTION: Linear Relationships
- Linear Regression & Least Squared Error
- Excel's Linear Regression Functions
- ASSIGNMENT: Simple Linear Regression
- KNOWLEDGE CHECK: Simple Linear Regression
- SOLUTION: Simple Linear Regression
- Determination (R-Squared)
- Standard Error
- Homoskedasticity & Heteroskedasticity
- Hypothesis Testing with Regression
- ASSIGNMENT: Model Evaluation
- KNOWLEDGE CHECK: Model Evaluation
- SOLUTION: Model Evaluation
- Excel's Regression Tool (Analysis ToolPak)
- PRO TIP: Multiple Linear Regression
- Key Takeaways
- QUIZ: Regression Analysis
- PROJECT #5: Maven Airlines
- PROJECT BRIEF: Maven Airlines
- SOLUTION: Maven Airlines
- BONUS LESSON
- BONUS LESSON

Learn everything from Probability & Statistics, then test your knowledge with 600+ practice questions

- Ratings
- 4.73
- Subscribers
- 88,448
- Subscribers last month

(February 2024) - 945
- Level
- all
- Video Duration
- 15 hours 4 minutes
- Created
- Feb 3rd, 2018
- Last updated
- Feb 16th, 2024
- Price
- $169.99

**HOW BECOME A PROBABILITY & STATISTICS MASTER IS SET UP TO MAKE COMPLICATED MATH EASY:**

This 163-lesson course includes video and text explanations of everything from Probability and Statistics, and it includes 45 quizzes (with solutions!) and an additional 8 workbooks with extra practice problems, to help you test your understanding along the way. Become a Probability & Statistics Master is organized into the following sections:

Visualizing data, including bar graphs, pie charts, Venn diagrams, histograms, and dot plots

Analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots

Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores

Probability, including union vs. intersection and independent and dependent events and Bayes' theorem

Discrete random variables, including binomial, Bernoulli, Poisson, and geometric random variables

Sampling, including types of studies, bias, and sampling distribution of the sample mean or sample proportion, and confidence intervals

Hypothesis testing, including inferential statistics, significance levels, type I and II errors, test statistics, and p-values

Regression, including scatterplots, correlation coefficient, the residual, coefficient of determination, RMSE, and chi-square

**AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:**

**Videos**: Watch over my shoulder as I solve problems for every single math issue you’ll encounter in class. We start from the beginning... I explain the problem setup and why I set it up that way, the steps I take and why I take them, how to work through the yucky, fuzzy middle parts, and how to simplify the answer when you get it.

**Notes**: The notes section of each lesson is where you find the most important things to remember. It’s like Cliff Notes for books, but for math. Everything you need to know to pass your class and nothing you don’t.

**Quizzes**: When you think you’ve got a good grasp on a topic within a course, you can test your knowledge by taking one of the quizzes. If you pass, great! If not, you can review the videos and notes again or ask for help in the Q&A section.

**Workbooks**: Want even more practice? When you've finished the section, you can review everything you've learned by working through the bonus workbook. The workbooks include tons of extra practice problems, so they're a great way to solidify what you just learned in that section.

**HERE'S WHAT SOME STUDENTS OF BECOME A PROBABILITY & STATISTICS MASTER HAVE TOLD ME:**

“Krista is an experienced teacher who offers Udemy students complete subject matter coverage and efficient and effective lessons/learning experiences. She not only understands the course material, but also selects/uses excellent application examples for her students and presents them clearly and skillfully using visual teaching aids/tools.” -

*John*“Really good, thorough, well explained lessons.” -

*Scott F.*“This is my second course (algebra previously) from Ms. King's offerings. I enjoyed this course and learned a lot! Each video explains a concept, followed by the working of several examples. I learned the most by listening to Ms King's teaching of the concept, stopping the video, and then attempting to work the example problems. After working the problems, then watching her complete the examples, I found that I really retained the concepts. A great instructor!” -

*Charles M.*

**YOU'LL ALSO GET:**

Lifetime access to Become a Probability & Statistics Master

Friendly support in the Q&A section

Udemy Certificate of Completion available for download

30-day money back guarantee

*Enroll today!*

*I can't wait for you to get started on mastering probability and statistics.*

*- Krista :)*

- Getting started
- What we'll learn in this course
- How to get the most out of this course
- Download the formula sheet
- The EVERYTHING download
- Visualizing data
- Introduction to visualizing data
- RESOURCE: Quiz solutions for this section
- One-way tables
- One-way tables
- One-way tables
- Bar graphs and pie charts
- Bar graphs and pie charts
- Bar graphs and pie charts
- Line graphs and ogives
- Line graphs and ogives
- Line graphs and ogives
- Two-way tables
- Two-way tables
- Two-way tables
- Venn diagrams
- Venn diagrams
- Venn diagrams
- Frequency tables and dot plots
- Frequency tables and dot plots
- Frequency tables and dot plots
- Relative frequency tables
- Relative frequency tables
- Relative frequency tables
- Joint distributions
- Joint distributions
- Joint distributions
- Histograms and stem-and-leaf plots
- Histograms and stem-and-leaf plots
- Histograms and stem-and-leaf plots
- Building histograms from data sets
- Building histograms from data sets
- Building histograms from data sets
- BONUS! Extra practice problems. :)
- Analyzing data
- Introduction to analyzing data
- RESOURCE: Quiz solutions for this section
- Measures of central tendency
- Measures of central tendency
- Measures of central tendency
- Measures of spread
- Measures of spread
- Measures of spread
- Changing the data, and outliers
- Changing the data, and outliers
- Changing the data, and outliers
- Box-and-whisker plots
- Box-and-whisker plots
- Box-and-whisker plots
- BONUS! Extra practice problems. :)
- Data distributions
- Introduction to data distributions
- RESOURCE: Quiz solutions for this section
- Mean, variance, and standard deviation
- Mean, variance, and standard deviation
- Mean, variance, and standard deviation
- Frequency histograms and polygons, and density curves
- Frequency histograms and polygons, and density curves
- Frequency histograms and polygons, and density curves
- Symmetric and skewed distributions and outliers
- Symmetric and skewed distributions and outliers
- Symmetric and skewed distributions and outliers
- Normal distributions and z-scores
- Normal distributions and z-scores
- Normal distributions and z-scores
- Chebyshev's Theorem
- Chebyshev's Theorem
- Chebyshev's Theorem
- Covariance
- Covariance
- Covariance
- Correlation coefficient
- Correlation coefficient
- Correlation coefficient
- Weighted means and grouped data
- Weighted means and grouped data
- Weighted means and grouped data
- BONUS! Extra practice problems. :)
- Probability
- Introduction to probability
- RESOURCE: Quiz solutions for this section
- Simple probability
- Simple probability
- Simple probability
- The addition rule, and union vs. intersection
- The addition rule, and union vs. intersection
- The addition rule, and union vs. intersection
- Independent and dependent events and conditional probability
- Independent and dependent events and conditional probability
- Independent and dependent events and conditional probability
- Bayes' Theorem
- Bayes' Theorem
- Bayes' Theorem
- BONUS! Extra practice problems. :)
- Discrete random variables
- Introduction to discrete random variables
- RESOURCE: Quiz solutions for this section
- Discrete probability
- Discrete probability
- Discrete probability
- Transforming random variables
- Transforming random variables
- Transforming random variables
- Combinations of random variables
- Combinations of random variables
- Combinations of random variables
- Permutations and combinations
- Permutations and combinations
- Permutations and combinations
- Binomial random variables
- Binomial random variables
- Binomial random variables
- Poisson distributions
- Poisson distributions
- Poisson distributions
- "At least" and "at most," and mean, variance, and standard deviation
- "At least" and "at most," and mean, variance, and standard deviation
- "At least" and "at most," and mean, variance, and standard deviation
- Bernoulli random variables
- Bernoulli random variables
- Bernoulli random variables
- Geometric random variables
- Geometric random variables
- Geometric random variables
- BONUS! Extra practice problems. :)
- Sampling
- Introduction to sampling
- RESOURCE: Quiz solutions for this section
- Types of studies
- Types of studies
- Types of studies
- Sampling and bias
- Sampling and bias
- Sampling and bias
- Sampling distribution of the sample mean
- Sampling distribution of the sample mean
- Sampling distribution of the sample mean
- Conditions for inference with the SDSM
- Conditions for inference with the SDSM
- Conditions for inference with the SDSM
- Sampling distribution of the sample proportion
- Sampling distribution of the sample proportion
- Sampling distribution of the sample proportion
- Conditions for inference with the SDSP
- Conditions for inference with the SDSP
- Conditions for inference with the SDSP
- The student's t-distribution
- The student's t-distribution
- The student's t-distribution
- Confidence interval for the mean
- Confidence interval for the mean
- Confidence interval for the mean
- Confidence interval for the proportion
- Confidence interval for the proportion
- Confidence interval for the proportion
- BONUS! Extra practice problems. :)
- Hypothesis testing
- Introduction to hypothesis testing
- RESOURCE: Quiz solutions for this section
- Inferential statistics and hypotheses
- Inferential statistics and hypotheses
- Inferential statistics and hypotheses
- Significance level and type I and II errors
- Significance level and type I and II errors
- Significance level and type I and II errors
- Test statistics for one- and two-tailed tests
- Test statistics for one- and two-tailed tests
- Test statistics for one- and two-tailed tests
- The p-value and rejecting the null
- The p-value and rejecting the null
- The p-value and rejecting the null
- Hypothesis testing for the population proportion
- Hypothesis testing for the population proportion
- Hypothesis testing for the population proportion
- Confidence interval for the difference of means
- Confidence interval for the difference of means
- Confidence interval for the difference of means
- Hypothesis testing for the difference of means
- Hypothesis testing for the difference of means
- Hypothesis testing for the difference of means
- Matched-pair hypothesis testing
- Matched-pair hypothesis testing
- Matched-pair hypothesis testing
- Confidence interval for the difference of proportions
- Confidence interval for the difference of proportions
- Confidence interval for the difference of proportions
- Hypothesis testing for the difference of proportions
- Hypothesis testing for the difference of proportions
- Hypothesis testing for the difference of proportions
- BONUS! Extra practice problems. :)
- Regression
- Introduction to regression
- RESOURCE: Quiz solutions for this section
- Scatterplots and regression
- Scatterplots and regression

A rigorous and engaging deep-dive into statistics and machine-learning, with hands-on applications in Python and MATLAB.

- Ratings
- 4.66
- Subscribers
- 24,942
- Subscribers last month

(February 2024) - 467
- Level
- all
- Video Duration
- 38 hours 19 minutes
- Created
- May 23rd, 2020
- Last updated
- Jan 31st, 2024
- Price
- $99.99

**Statistics and probability control your life.** I don't just mean What YouTube's algorithm recommends you to watch next, and I don't just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms, Earthquakes, the stock market, whether it will snow in the first week of December, and countless other phenomena are probabilistic and statistical. Even the very nature of the most fundamental deep structure of the universe is governed by probability and statistics.

**You need to understand statistics**.

Nearly all areas of human civilization are incorporating code and numerical computations. This means that many jobs and areas of study are based on applications of statistical and machine-learning techniques in programming languages like Python and MATLAB. This is often called 'data science' and is an increasingly important topic. Statistics and machine learning are also fundamental to artificial intelligence (AI) and business intelligence.

If you want to make yourself a future-proof employee, employer, data scientist, or researcher in any technical field -- ranging from data scientist to engineering to research scientist to deep learning modeler -- you'll need to know statistics and machine-learning. And you'll need to know how to implement concepts like probability theory and confidence intervals, k-means clustering and PCA, Spearman correlation and logistic regression, in computer languages like Python or MATLAB.

**There are six reasons why you should take this course:**

This course covers everything you need to understand the fundamentals of statistics, machine learning, and data science, from bar plots to ANOVAs, regression to k-means, t-test to non-parametric permutation testing.

After completing this course, you will be able to understand a wide range of statistical and machine-learning analyses, even specific advanced methods that aren't taught here. That's because you will learn the foundations upon which advanced methods are build.

This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.

Enrolling in the course gives you access to the Q&A, in which I actively participate every day.

I've been studying, developing, and teaching statistics for over 20 years, and I think math is, like, really cool.

**What you need to know before taking this course:**

High-school level maths. This is an applications-oriented course, so I don't go into a lot of detail about proofs, derivations, or calculus.

Basic coding skills in Python or MATLAB. This is necessary only if you want to follow along with the code. You can successfully complete this course without writing a single line of code! But participating in the coding exercises will help you learn the material. The MATLAB code relies on the Statistics and Machine Learning toolbox (you can use Octave if you don't have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.

I recommend taking my free course called "

*Statistics literacy for non-statisticians*". It's 90 minutes long and will give you a bird's-eye-view of the main topics in statistics that I go into much much much more detail about here in this course. Note that the free short course is not required for this course, but complements this course nicely. And you can get through the whole thing in less than an hour if you watch if on 1.5x speed!You do not need any previous experience with statistics, machine learning, deep learning, or data science. That's why you're here!

**Is this course up to date?**

Yes, I maintain all of my courses regularly. I add new lectures to keep the course "alive," and I add new lectures (or sometimes re-film existing lectures) to explain maths concepts better if students find a topic confusing or if I made a mistake in the lecture (rare, but it happens!).

You can check the "Last updated" text at the top of this page to see when I last worked on improving this course!

**What if you have questions about the material?**

This course has a Q&A (question and answer) section where you can post your questions about the course material (about the maths, statistics, coding, or machine learning aspects). I try to answer all questions within a day. You can also see all other questions and answers, which really improves how much you can learn! And you can contribute to the Q&A by posting to ongoing discussions.

And, you can also post your code for feedback or just to show off -- I love it when students actually write better code than me! (Ahem, doesn't happen so often.)

**What should you do now?**

First of all, congrats on reading this far; that means you are seriously interested in learning statistics and machine learning. Watch the preview videos, check out the reviews, and, when you're ready, invest in your brain by learning from this course!

- Introductions
- [Important] Getting the most out of this course
- About using MATLAB or Python
- Statistics guessing game!
- Using the Q&A forum
- (optional) Entering time-stamped notes in the Udemy video player
- Math prerequisites
- Should you memorize statistical formulas?
- Arithmetic and exponents
- Scientific notation
- Summation notation
- Absolute value
- Natural exponent and logarithm
- The logistic function
- Rank and tied-rank
- IMPORTANT: Download course materials
- Download materials for the entire course!
- What are (is?) data?
- Is "data" singular or plural?!?!!?!
- Where do data come from and what do they mean?
- Types of data: categorical, numerical, etc
- Code: representing types of data on computers
- Sample vs. population data
- Samples, case reports, and anecdotes
- The ethics of making up data
- Visualizing data
- Bar plots
- Code: bar plots
- Box-and-whisker plots
- Code: box plots
- "Unsupervised learning": Boxplots of normal and uniform noise
- Histograms
- Code: histograms
- "Unsupervised learning": Histogram proportion
- Pie charts
- Code: pie charts
- When to use lines instead of bars
- Linear vs. logarithmic axis scaling
- Code: line plots
- "Unsupervised learning": log-scaled plots
- Descriptive statistics
- Descriptive vs. inferential statistics
- Accuracy, precision, resolution
- Data distributions
- Code: data from different distributions
- "Unsupervised learning": histograms of distributions
- The beauty and simplicity of Normal
- Measures of central tendency (mean)
- Measures of central tendency (median, mode)
- Code: computing central tendency
- "Unsupervised learning": central tendencies with outliers
- Measures of dispersion (variance, standard deviation)
- Code: Computing dispersion
- Interquartile range (IQR)
- Code: IQR
- QQ plots
- Code: QQ plots
- Statistical "moments"
- Histograms part 2: Number of bins
- Code: Histogram bins
- Violin plots
- Code: violin plots
- "Unsupervised learning": asymmetric violin plots
- Shannon entropy
- Code: entropy
- "Unsupervised learning": entropy and number of bins
- Data normalizations and outliers
- Garbage in, garbage out (GIGO)
- Z-score standardization
- Code: z-score
- Min-max scaling
- Code: min-max scaling
- "Unsupervised learning": Invert the min-max scaling
- What are outliers and why are they dangerous?
- Removing outliers: z-score method
- The modified z-score method
- Code: z-score for outlier removal
- "Unsupervised learning": z vs. modified-z
- Multivariate outlier detection
- Code: Euclidean distance for outlier removal
- Removing outliers by data trimming
- Code: Data trimming to remove outliers
- Non-parametric solutions to outliers
- Nonlinear data transformations
- An outlier lecture on personal accountability
- Probability theory
- What is probability?
- Probability vs. proportion
- Computing probabilities
- Code: compute probabilities
- Probability and odds
- "Unsupervised learning": probabilities of odds-space
- Probability mass vs. density
- Code: compute probability mass functions
- Cumulative distribution functions
- Code: cdfs and pdfs
- "Unsupervised learning": cdf's for various distributions
- Creating sample estimate distributions
- Monte Carlo sampling
- Sampling variability, noise, and other annoyances
- Code: sampling variability
- Expected value
- Conditional probability
- Code: conditional probabilities
- Tree diagrams for conditional probabilities
- The Law of Large Numbers
- Code: Law of Large Numbers in action
- The Central Limit Theorem
- Code: the CLT in action
- "Unsupervised learning": Averaging pairs of numbers
- Hypothesis testing
- IVs, DVs, models, and other stats lingo
- What is an hypothesis and how do you specify one?
- Sample distributions under null and alternative hypotheses
- P-values: definition, tails, and misinterpretations
- P-z combinations that you should memorize
- Degrees of freedom
- Type 1 and Type 2 errors
- Parametric vs. non-parametric tests
- Multiple comparisons and Bonferroni correction
- Statistical vs. theoretical vs. clinical significance
- Cross-validation
- Statistical significance vs. classification accuracy
- The t-test family
- Purpose and interpretation of the t-test
- One-sample t-test
- Code: One-sample t-test
- "Unsupervised learning": The role of variance
- Two-samples t-test
- Code: Two-samples t-test
- "Unsupervised learning": Importance of N for t-test
- Wilcoxon signed-rank (nonparametric t-test)
- Code: Signed-rank test
- Mann-Whitney U test (nonparametric t-test)
- Code: Mann-Whitney U test
- Permutation testing for t-test significance
- Code: permutation testing
- "Unsupervised learning": How many permutations?
- Confidence intervals on parameters
- What are confidence intervals and why do we need them?
- Computing confidence intervals via formula
- Code: compute confidence intervals by formula
- Confidence intervals via bootstrapping (resampling)
- Code: bootstrapping confidence intervals
- "Unsupervised learning:" Confidence intervals for variance
- Misconceptions about confidence intervals
- Correlation
- Motivation and description of correlation
- Covariance and correlation: formulas
- Code: correlation coefficient
- Code: Simulate data with specified correlation
- Correlation matrix
- Code: correlation matrix
- "Unsupervised learning": average correlation matrices
- "Unsupervised learning": correlation to covariance matrix
- Partial correlation
- Code: partial correlation
- The problem with Pearson
- Nonparametric correlation: Spearman rank
- Fisher-Z transformation for correlations
- Code: Spearman correlation and Fisher-Z
- "Unsupervised learning": Spearman correlation
- "Unsupervised learning": confidence interval on correlation
- Kendall's correlation for ordinal data
- Code: Kendall correlation
- "Unsupervised learning": Does Kendall vs. Pearson matter?
- The subgroups correlation paradox
- Cosine similarity
- Code: Cosine similarity vs. Pearson correlation
- Analysis of Variance (ANOVA)
- ANOVA intro, part1
- ANOVA intro, part 2
- Sum of squares
- The F-test and the ANOVA table
- The omnibus F-test and post-hoc comparisons
- The two-way ANOVA
- One-way ANOVA example
- Code: One-way ANOVA (independent samples)
- Code: One-way repeated-measures ANOVA
- Two-way ANOVA example
- Code: Two-way mixed ANOVA
- Regression
- Introduction to GLM / regression
- Least-squares solution to the GLM
- Evaluating regression models: R2 and F
- Simple regression
- Code: simple regression
- "Unsupervised learning": Compute R2 and F
- Multiple regression
- Standardizing regression coefficients
- Code: Multiple regression
- Polynomial regression models
- Code: polynomial modeling
- "Unsupervised learning": Polynomial design matrix
- Logistic regression
- Code: Logistic regression
- Under- and over-fitting
- "Unsupervised learning": Overfit data
- Comparing "nested" models
- What to do about missing data

## 3. Top 3 Recommended YouTube Videos

Here are Outlecture's top 3 recommended YouTube videos, carefully selected for you.

Title | View count | View count last month (February 2024) | Like count | Publish date |
---|---|---|---|---|

Statistics Formulas -1 Channel: Bright Maths | 277,013 | 34,353 | 11,062 | Feb 3rd, 2023 |

Beginner to Pro FREE Excel Data Analysis Course Channel: Chandoo | 1,783,977 | 32,722 | 46,290 | Aug 12th, 2021 |

Introduction to Statistics Channel: The Organic Chemistry Tutor | 666,681 | 65,848 | 9,808 | Feb 9th, 2023 |

YouTube has become a familiar platform for everyday use, where viewers can watch videos for free, although they may contain advertisements. Recently, there has been an increase in the availability of high-quality educational materials on this platform.
It is an excellent option for those who want to learn without paying or simply obtaining a quick understanding of a topic.

We highly recommend utilizing YouTube as a valuable learning resource.

Recommended for

- Wanting to learn without spending money
- Wanting to quickly understand the overview of Statistics

The details of each course are as follows:

Bright Maths

- View count
- 277,013
- View count last month

(February 2024) - 34,353
- Like count
- 11,062
- Publish date
- Feb 3rd, 2023

Chandoo

- View count
- 1,783,977
- View count last month

(February 2024) - 32,722
- Like count
- 46,290
- Publish date
- Aug 12th, 2021

In this comprehensive video, learn:

1) How to approach a data analysis project

2) A real-world example data with 10 problems

3) Step by step walk thru

4) How to calculate descriptive statistics (mean, median, quartiles, range, distinct items & count)

5) Exploratory data analysis in Excel

6) Analysis with formulas vs. pivots (necessary techniques)

7) Top / bottom performing items

8) Anomaly detection

9) Best in class analysis

10) Complete report preparation

11) Combining data in different tables (spreadsheets)

12) Answering open ended questions

13) Preparing and using Excel charts

14) Challenges & homework assignment

You will also learn below Excel features:

1) Using Tables

2) Formulas

3) Pivot Tables & Power Pivot measures

4) Conditional formatting

5) Charts

6) Data Validation

7) Keyboard Shortcuts & tricks

⏱ Video Timestamps:

==================

0:00 - Introduction

2:12 - Descriptive statistics in Excel

8:05 - Exploratory Data Analysis (EDA) with conditional formatting

13:20 - Sales by Country report with formulas

19:01 - Sales by Country report with Pivots

23:43 - Top 5 products with $ per unit

27:13 - Anomaly detection in your data

31:24 - Best in category analysis

33:36 - Profit analysis (combining two tables)

37:26 - Dynamic country level sales report

41:55 - Which products to discontinue (Open ended questions)

💥📁 Sample files:

================

Blank data file (perfect for following along) - https://chandoo.org/wp/wp-content/uploads/2021/08/beginner-DA-course-blank.xlsx

Completed workbook - https://chandoo.org/wp/wp-content/uploads/2021/08/beginner-DA-course.xlsx

⛔⚠ Got an ERROR - Can't Change Array?

==================================

Here is the fix - https://chandoo.org/wp/errors-with-data-analysis-course/

What to watch next?

=================

I recommend setting aside sometime to practice what you just learned. But if you are in the mood to watch more, I suggest these videos:

5 key skills you need to be a GREAT data analyst - https://youtu.be/gVr9f1GJdZc

5 Excel Skills you need to focus on - https://youtu.be/cEuq_9CsHIY

15 Excel functions you should know - https://youtu.be/B5hayFelHDU

My playlist on data analysis - https://www.youtube.com/watch?v=gVr9f1GJdZc&list=PLmejDGrsgFyDAWOAnEiK0P787q3grsk9R

Want to learn more? Join my Excel School 👉

=====================================

If you want more step by step education & resources to be AWESOME at your work, consider joining my Excel School program. This online class will teach you everything you need for data analysis + reporting roles.

Visit https://chandoo.org/wp/excel-school-program/ to sign up today.

My Recommended Excel Books 📚👌:

===============================

https://chandoo.org/wp/best-excel-power-bi-books/

😎 SAY HELLO 👋

===============

Apart from YouTube, I frequently post on,

my blog - https://chandoo.org/wp/

twitter - https://twitter.com/r1c1/

Instagram - https://www.instagram.com/chandoo.xlsx

Have a beautiful day 🌼🌞😀

#DataAnalysis #Excel

The Organic Chemistry Tutor

- View count
- 666,681
- View count last month

(February 2024) - 65,848
- Like count
- 9,808
- Publish date
- Feb 9th, 2023

Introduction to Statistics:

https://www.youtube.com/watch?v=XZo4xyJXCak

Descriptive Vs Inferential Statistics:

https://www.youtube.com/watch?v=VHYOuWu9jQI

Qualitative and Quantitative Data:

https://www.youtube.com/watch?v=5rUVYWfZOb8

Statistic Vs Parameter:

https://www.youtube.com/watch?v=Mb9BuEkbaHQ

Scales of Measurement:

https://www.youtube.com/watch?v=LuBD49SFpWs

__________________________________

Mean, Median, Mode, & Range:

https://www.youtube.com/watch?v=A1mQ9kD-i9I

Weighted Mean & Averages:

https://www.youtube.com/watch?v=LdrBNhWw9AM

Find Missing Value Given The Mean:

https://www.youtube.com/watch?v=l8KrAo089_U

Excel - Mean, Median, Mode, & Range:

https://www.youtube.com/watch?v=k17_euuiTKw

Arithmetic, Geometric, & Harmonic Mean:

https://www.youtube.com/watch?v=6G6i8vSa8Zs

___________________________________

Simple Frequency Tables:

https://www.youtube.com/watch?v=lyRbCwDDnJo

Relative Frequency Distribution Table:

https://www.youtube.com/watch?v=gq3FPpm2yvA

Cumulative Relative Frequency Table:

https://www.youtube.com/watch?v=6hJGa4Zp62M

Dot Plots and Frequency Tables:

https://www.youtube.com/watch?v=Iu17mY1VfZU

Stem and Leaf Plots:

https://www.youtube.com/watch?v=MUCvUgGfzdo

____________________________________

Final Exams and Video Playlists:

https://www.video-tutor.net/

## 5. Wrap-up

We introduced recommended courses for Statistics. If you are interested in learning other related courses, please refer to the following.

If you want to further explore and learn after taking one of the courses we introduced today, we recommend visiting the official website or community site.

If you want to stay up-to-date on the latest information, we suggest following the official Twitter account.

Furthermore, We highly recommend utilizing General AI such as ChatGPT as a study aid. This can enable more effective learning, so please give it a try.

We hope you found our website and article helpful. Thank you for visiting.