Learn the statistics you need in the office: descriptive and inferential statistics, hypothesis testing, and regression analysis.
About This Video
In Detail …
Learn the statistics you need in the office: descriptive and inferential statistics, hypothesis testing, and regression analysis.
About This Video
In Detail
This course will teach you fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. Modern software packages and programming languages are now automating most of these activities, but this course gives you something more valuable—critical thinking abilities. This course will help you understand the fundamentals of statistics, learn how to work with different types of data, calculate correlation and covariance, and more.
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.
The course has been designed as follows:
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
By the end of this course, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. With this course, you will develop a habit of critical thinking that will take you miles ahead in your career.
Audience
This course targets anyone who wants a career in data science or business intelligence; individuals who are passionate about numbers and quant analysis; anyone who wants to learn the subtleties of statistics and how it is used in the business world; people who want to learn the fundamentals of statistics; business analysts; and business executives.
Absolutely no prior experience is required for this course. We will start from the basics and gradually build up your knowledge. Everything is in the course.
Chapter 1 : Introduction to the Course
What Does the Course Cover?
Understanding the Difference Between a Population and a Sample
Chapter 2 : Descriptive Statistics Fundamentals
The Various Types of Data We can Work With
Levels of Measurement
Categorical Variables and Visualization Techniques for Categorical Variables
Numerical Variables and Using a Frequency Distribution Table
Histogram Charts
Cross Tables and Scatter Plots
The Main Measures of Central Tendency: Mean, Median, Mode
Measuring Skewness
Measuring How Data is Spread Out: Calculating Variance
Standard Deviation and Coefficient of Variation
Calculating and Understanding Covariance
The Correlation Coefficient
Practical Example
Chapter 3 : Inferential Statistics Fundamentals
Introduction to Inferential Statistics
What is a Distribution?
The Normal Distribution
The Standard Normal Distribution
Understanding the central limit theorem
Standard Error
Working with Estimators and Estimates
Chapter 4 : Confidence Intervals
Confidence Intervals - an Invaluable Tool for Decision Making
Calculating Confidence Intervals Within a Population with a Known Variance
Confidence Interval Clarifications
Student’s T Distribution
Calculating Confidence Intervals Within a Population with an Unknown Variance
What is a Margin of Error and Why is it Important in Statistics?
Calculating Confidence Intervals for Two Means with Dependent Samples
Calculating Confidence Intervals for Two Means with Independent Samples (Part 1)
Calculating Confidence Intervals for Two Means with Independent Samples (Part 2)
Calculating Confidence Intervals for Two Means with Independent Samples (Part 3)
Practical Example: Inferential Statistics
Chapter 5 : Hypothesis Testing
The Null and the Alternative Hypothesis
Establishing a Rejection Region and a Significance Level
Type I Error Versus Type II Error
Test for the Mean; Population Variance Known
What is P-Value and Why is it One of the Most Useful Tools for Statisticians?
Test for the Mean; Population Variance Unknown
Test for the Mean; Dependent Samples
Test for the Mean; Independent Samples (Part 1)
Test for the Mean; Independent Samples (Part 2)
Practical Example: Hypothesis Testing
Chapter 6 : The Fundamentals of Regression Analysis
Introduction to Regression Analysis
Correlation and Causation
The Linear Regression Model Made Easy
What is the Difference Between Correlation and Regression?
A Geometrical Representation of the Linear Regression Model
A Practical Example - Reinforced Learning
Chapter 7 : Subtleties of Regression Analysis
Decomposing the Linear Regression Model - Understanding its Nuts and Bolts
What is R-Squared and How Does it Help Us?
The Ordinary Least Squares Setting and its Practical Applications
Studying Regression Tables
The Multiple Linear Regression Model
Adjusted R-Squared
What Does the F-Statistic Show Us and Why Do We Need to Understand It?
Chapter 8 : Assumptions for Linear Regression Analysis
OLS Assumptions
A1. Linearity
A2. No Endogeneity
A3. Normality and Homoscedasticity
A4. No Autocorrelation
A5. No Multicollinearity
Chapter 9 : Dealing with Categorical Data
Dummy Variables
Chapter 10 : Practical Example: Regression Analysis
Practical Example: Regression Analysis