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Course Content
01 – Introduction
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Lesson 001 What does the course cover
03:54
02 – Sample or population data
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Lesson 001 Understanding the difference between a population and a sample
04:02
03 – The fundamentals of descriptive statistics
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Lesson 001 The various types of data we can work with
04:33
Lesson 003 Levels of measurement
03:43
Lesson 005 Categorical variables. Visualization techniques for categorical variables
04:52
Lesson 008 Numerical variables. Using a frequency distribution table
03:10
Lesson 011 Histogram charts
02:14
Lesson 014 Cross tables and scatter plots
04:44
04 – Measures of central tendency, asymmetry, and variability
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Lesson 001 The main measures of central tendency mean, median and mode
04:20
Lesson 003 Measuring skewness
02:37
Lesson 006 Measuring how data is spread out calculating variance
05:55
Lesson 008 Standard deviation and coefficient of variation
04:40
Lesson 011 Calculating and understanding covariance
03:23
Lesson 013 The correlation coefficient
03:17
05 – Practical example descriptive statistics
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Lesson 001 Practical example
16:16
06 – Distributions
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Lesson 001 Introduction to inferential statistics
01:01
Lesson 002 What is a distribution
04:33
Lesson 004 The Normal distribution
03:54
Lesson 006 The standard normal distribution
03:31
Lesson 009 Understanding the central limit theorem
04:20
Lesson 011 Standard error
01:27
07 – Estimators and estimates
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Lesson 001 Working with estimators and estimates
03:07
Lesson 003 Confidence intervals – an invaluable tool for decision making
02:41
Lesson 005 Calculating confidence intervals within a population with a known variance
08:01
Lesson 007 Confidence interval clarifications
04:38
Lesson 008 Student’s T distribution
03:23
Lesson 010 Calculating confidence intervals within a population with an unknown variance
04:36
Lesson 012 What is a margin of error and why is it important in Statistics
04:53
08 – Confidence intervals advanced topics
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Lesson 001 Calculating confidence intervals for two means with dependent samples
06:04
Lesson 003 Calculating confidence intervals for two means with independent samples (part 1)
04:31
Lesson 005 Calculating confidence intervals for two means with independent samples (part 2)
03:57
Lesson 007 Calculating confidence intervals for two means with independent samples (part 3)
01:27
09 – Practical example inferential statistics
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Lesson 001 Practical example inferential statistics
10:06
10 – Hypothesis testing Introduction
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Lesson 001 The null and the alternative hypothesis
05:52
Lesson 004 Establishing a rejection region and a significance level
07:05
Lesson 006 Type I error vs Type II error
04:14
11 – Hypothesis testing Let’s start testing!
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Lesson 001 Test for the mean. Population variance known
06:34
Lesson 003 What is the p-value and why is it one of the most useful tools for statisticians
04:13
Lesson 005 Test for the mean. Population variance unknown
04:49
Lesson 007 Test for the mean. Dependent samples
05:18
Lesson 009 Test for the mean. Independent samples (Part 1)
04:22
Lesson 011 Test for the mean. Independent samples (Part 2)
04:26
12 – Practical example hypothesis testing
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Lesson 001 Practical example hypothesis testing
07:16
13 – The fundamentals of regression analysis
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Lesson 001 Introduction to regression analysis
01:02
Lesson 003 Correlation and causation
04:13
Lesson 005 The linear regression model made easy
05:50
Lesson 007 What is the difference between correlation and regression
01:44
Lesson 009 A geometrical representation of the linear regression model
01:25
Lesson 011 A practical example – Reinforced learning
05:46
14 – Subtleties of regression analysis
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Lesson 001 Decomposing the linear regression model – understanding its nuts and bolts
03:38
Lesson 003 What is R-squared and how does it help us
05:24
Lesson 005 The ordinary least squares setting and its practical applications
02:24
Lesson 007 Studying regression tables
04:54
Lesson 010 The multiple linear regression model
02:56
Lesson 012 The adjusted R-squared
05:24
Lesson 014 What does the F-statistic show us and why do we need to understand it
02:01
15 – Assumptions for linear regression analysis
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Lesson 001 OLS assumptions
02:21
Lesson 003 A1. Linearity
01:50
Lesson 005 A2. No endogeneity
04:09
Lesson 007 A3. Normality and homoscedasticity
05:47
Lesson 009 A4. No autocorrelation
03:14
Lesson 011 A5. No multicollinearity
03:26
16 – Dealing with categorical data
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Lesson 001 Dummy variables
05:03
17 – Practical example regression analysis
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Lesson 001 Practical example regression analysis
14:09
18 – Bonus lecture
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