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Statistics
Introduction
Welcome to Statistics (0:22)
Welcome to the Course!
Population vs sample (4:02)
Download Course Notes and Glossary
Descriptive Statistics Fundamentals
Types of data (4:33)
Levels of measurement (3:43)
Categorical variables. Visualization techniques (4:52)
Numerical variables. Frequency distribution table (3:09)
The histogram (2:14)
Cross table and scatter plot (4:44)
Mean, median, mode (4:20)
Skewness (2:37)
Variance (5:55)
Standard deviation and coefficient of variation (4:40)
Covariance (3:23)
Correlation (3:17)
Practical Example - Descriptive Statistics
Practical Example - Descriptive Statistics (16:08)
Inferential Statistics Fundamentals
Introduction (1:00)
What is a distribution (4:33)
The Normal Distribution (3:54)
The Standard Normal Distribution (3:30)
Central limit theorem (4:20)
Standard error (1:26)
Estimators and estimates (3:07)
Confidence Intervals
Definition of confidence intervals (2:41)
Population variance known, z-score (8:01)
Confidence Interval Clarifications (4:38)
Student's T Distribution (3:22)
Population variance unknown, t-score (4:36)
Margin of error (4:52)
Confidence intervals. Two means. Dependent samples (6:04)
Confidence intervals. Two means. Independent samples (Part1) (4:31)
Confidence intervals. Two means. Independent samples (Part2) (3:57)
Confidence intervals. Two means. Independent samples (Part 3) (1:27)
Practical Example - Confidence Intervals
Practical Example - Confidence Intervals (10:05)
Hypothesis testing
Null vs Alternative (5:51)
Rejection region and significance level (7:05)
Type I error vs type II error (4:14)
Test for the mean. Population variance known (6:34)
p-value (4:13)
Test for the mean. Population variance unknown (4:48)
Test for the mean. Dependent samples (5:18)
Test for the mean. Independent samples (Part1) (4:22)
Test for the mean. Independent samples (Part2) (4:26)
Practical Example - Hypothesis testing
Practical Example - Hypothesis Testing (7:16)
Estimators and estimates
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