Introduction to Statistics and Data Analysis
5.0
(2)
28 learners
What you'll learn
This course includes
- 9.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 35 lessons • 9.5 hours of video
Introduction to Statistics and Data Analysis
35 lessons
• 9.5 hours
Introduction to Statistics and Data Analysis
35 lessons
• 9.5 hours
- Introduction to Statistics and Data Analysis 22:21
- Population Statistics and Random Sampling 23:45
- Random Sampling in Statistics: Expected Value and Variance of the Sample Mean 16:07
- Random Sampling Without Replacement (Finite "n" Correction) 12:15
- Sample Variance in Random Population Sampling 11:38
- Normal Approximation to Sample Mean 19:41
- Confidence Intervals 16:57
- Central Limit Theorem Example & Hypothesis Testing 09:33
- Hypothesis Testing in Statistics 24:55
- Hypothesis Testing Procedure 18:01
- Hypothesis Testing: Type I and Type II Errors 10:06
- Hypothesis Testing Example: Salk Vaccine Trial 16:06
- Could Tobacco be Good for you? Two Sided Rejection Regions in Hypothesis Testing 12:20
- Lies, Damn Lies, and Statistics... P-Hacking 19:14
- Parameter Estimation and Fitting Distributions 24:13
- Method of Moments to Fit Distributions from Data 11:02
- Error in the Method of Moments 18:41
- Bootstrapping and Monte Carlo Sampling in Statistics 16:59
- Maximum Likelihood Estimation (MLE) with Examples 23:46
- Maximum Likelihood Estimation Example: Fitting a Normal Distribution with Data 15:53
- Properties of Maximum Likelihood Estimation 14:00
- Bayesian Maximum Aposteriori Estimation (MAP): Extending Maximum Likelihood Estimation 12:59
- Consistency of Parameter Estimates in Statistics 10:24
- The Chi-Squared Test : Are Two Distributions the Same? (with Python Example) 22:44
- Student's t-distribution in Statistics 16:39
- Hypothesis Testing Revisited: Normal, t, and Chi-Squared Distribution Tests 10:37
- Properties of Chi-Squared and Student's t Distributions 10:10
- Bayesian Inference: Overview 30:16
- Bayesian Updates and Conjugate Priors 17:38
- Conjugate Priors Example: Normal Distribution and the Exponential Family of Distributions 18:48
- Density Estimation with Gaussian Mixture Models (GMM) and Empirical Priors 18:16
- Monte Carlo Sampling and Bootstrapping in Bayesian Inference 18:15
- Bayesian Linear Regression and Maximum Likelihood Estimates 19:15
- Bayesian Linear Regression and Maximum a Posteriori (MAP) Estimate 15:17
- Bayesian Linear Regression [Python Example] 16:46
Take this course with you
Download the app to continue lessons from your phone anytime.
