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What does the course cover?
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Understanding the difference between a population and a sample
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The various types of data we can work with
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Levels of measurement
• Урок 5. 00:04:53
Categorical variables. Visualization techniques for categorical variables
• Урок 6. 00:03:10
Numerical variables. Using a frequency distribution table
• Урок 7. 00:02:15
Histogram charts
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Cross tables and scatter plots
• Урок 9. 00:04:21
The main measures of central tendency: mean, median and mode
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Measuring skewness
• Урок 11. 00:05:56
Measuring how data is spread out: calculating variance
• Урок 12. 00:04:41
Standard deviation and coefficient of variation
• Урок 13. 00:03:24
Calculating and understanding covariance
• Урок 14. 00:03:18
The correlation coefficient
• Урок 15. 00:16:16
Practical example
• Урок 16. 00:01:01
Introduction to inferential statistics
• Урок 17. 00:04:34
What is a distribution?
• Урок 18. 00:03:55
The Normal distribution
• Урок 19. 00:03:31
The standard normal distribution
• Урок 20. 00:04:21
Understanding the central limit theorem
• Урок 21. 00:01:28
Standard error
• Урок 22. 00:03:08
Working with estimators and estimates
• Урок 23. 00:02:42
Confidence intervals - an invaluable tool for decision making
• Урок 24. 00:08:02
Calculating confidence intervals within a population with a known variance
• Урок 25. 00:04:39
Confidence interval clarifications
• Урок 26. 00:03:23
Student's T distribution
• Урок 27. 00:04:37
Calculating confidence intervals within a population with an unknown variance
• Урок 28. 00:04:53
What is a margin of error and why is it important in Statistics?
• Урок 29. 00:06:05
Calculating confidence intervals for two means with dependent samples
• Урок 30. 00:04:32
Calculating confidence intervals for two means with independent samples (part 1)
• Урок 31. 00:03:58
Calculating confidence intervals for two means with independent samples (part 2)
• Урок 32. 00:01:28
Calculating confidence intervals for two means with independent samples (part 3)
• Урок 33. 00:10:07
Practical example: inferential statistics
• Урок 34. 00:05:53
The null and the alternative hypothesis
• Урок 35. 00:07:06
Establishing a rejection region and a significance level
• Урок 36. 00:04:15
Type I error vs Type II error
• Урок 37. 00:06:35
Test for the mean. Population variance known
• Урок 38. 00:04:14
What is the p-value and why is it one of the most useful tools for statisticians
• Урок 39. 00:04:49
Test for the mean. Population variance unknown
• Урок 40. 00:05:19
Test for the mean. Dependent samples
• Урок 41. 00:04:23
Test for the mean. Independent samples (Part 1)
• Урок 42. 00:04:27
Test for the mean. Independent samples (Part 2)
• Урок 43. 00:07:17
Practical example: hypothesis testing
• Урок 44. 00:01:03
Introduction to regression analysis
• Урок 45. 00:04:13
Correlation and causation
• Урок 46. 00:05:51
The linear regression model made easy
• Урок 47. 00:01:44
What is the difference between correlation and regression?
• Урок 48. 00:01:26
A geometrical representation of the linear regression model
• Урок 49. 00:05:46
A practical example - Reinforced learning
• Урок 50. 00:03:38
Decomposing the linear regression model - understanding its nuts and bolts
• Урок 51. 00:05:25
What is R-squared and how does it help us?
• Урок 52. 00:02:24
The ordinary least squares setting and its practical applications
• Урок 53. 00:04:55
Studying regression tables
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The multiple linear regression model
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What does the F-statistic show us and why do we need to understand it?
• Урок 57. 00:02:22
OLS assumptions
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A1. Linearity
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A2. No endogeneity
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A3. Normality and homoscedasticity
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A4. No autocorrelation
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A5. No multicollinearity
• Урок 63. 00:05:04
Dummy variables
• Урок 64. 00:14:10
Practical example: regression analysis
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